Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant

The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability a...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhou, Man [verfasserIn] Han, Lansheng [verfasserIn] Lu, Hongwei [verfasserIn] Fu, Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net

Übergeordnetes Werk:	Enthalten in: Computer networks - Amsterdam [u.a.] : Elsevier, 1976, 172
Übergeordnetes Werk:	volume:172

DOI / URN:	10.1016/j.comnet.2020.107174

Katalog-ID:	ELV003977145

Internformat


LEADER	01000caa a22002652 4500
001	ELV003977145
003	DE-627
005	20230524145032.0
007	cr uuu---uuuuu
008	230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.comnet.2020.107174 \|2 doi
035			\|a (DE-627)ELV003977145
035			\|a (ELSEVIER)S1389-1286(19)31187-9
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|a 620 \|q DE-600
084			\|a 54.32 \|2 bkl
084			\|a 53.76 \|2 bkl
100	1		\|a Zhou, Man \|e verfasserin \|4 aut
245	1	0	\|a Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant
264		1	\|c 2020
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process.
650		4	\|a Distributed collaborative intrusion detection
650		4	\|a Vehicle Ad Hoc Network
650		4	\|a Reputation evaluation
650		4	\|a Invariant detection
650		4	\|a Stochastic Petri Net
700	1		\|a Han, Lansheng \|e verfasserin \|4 aut
700	1		\|a Lu, Hongwei \|e verfasserin \|4 aut
700	1		\|a Fu, Cai \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Computer networks \|d Amsterdam [u.a.] : Elsevier, 1976 \|g 172 \|h Online-Ressource \|w (DE-627)306652749 \|w (DE-600)1499744-7 \|w (DE-576)081954360 \|7 nnns
773	1	8	\|g volume:172
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.32 \|j Rechnerkommunikation
936	b	k	\|a 53.76 \|j Kommunikationsdienste \|j Fernmeldetechnik
951			\|a AR
952			\|d 172

Indexfelder

author_variant	m z mz l h lh h l hl c f cf
matchkey_str	zhoumanhanlanshengluhongweifucai:2020----:itiueclaoaienrsodtcinytmovhclrdon
hierarchy_sort_str	2020
bklnumber	54.32 53.76
publishDate	2020
allfields	10.1016/j.comnet.2020.107174 doi (DE-627)ELV003977145 (ELSEVIER)S1389-1286(19)31187-9 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Zhou, Man verfasserin aut Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process. Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net Han, Lansheng verfasserin aut Lu, Hongwei verfasserin aut Fu, Cai verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172
spelling	10.1016/j.comnet.2020.107174 doi (DE-627)ELV003977145 (ELSEVIER)S1389-1286(19)31187-9 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Zhou, Man verfasserin aut Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process. Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net Han, Lansheng verfasserin aut Lu, Hongwei verfasserin aut Fu, Cai verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172
allfields_unstemmed	10.1016/j.comnet.2020.107174 doi (DE-627)ELV003977145 (ELSEVIER)S1389-1286(19)31187-9 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Zhou, Man verfasserin aut Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process. Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net Han, Lansheng verfasserin aut Lu, Hongwei verfasserin aut Fu, Cai verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172
allfieldsGer	10.1016/j.comnet.2020.107174 doi (DE-627)ELV003977145 (ELSEVIER)S1389-1286(19)31187-9 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Zhou, Man verfasserin aut Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process. Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net Han, Lansheng verfasserin aut Lu, Hongwei verfasserin aut Fu, Cai verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172
allfieldsSound	10.1016/j.comnet.2020.107174 doi (DE-627)ELV003977145 (ELSEVIER)S1389-1286(19)31187-9 DE-627 ger DE-627 rda eng 004 620 DE-600 54.32 bkl 53.76 bkl Zhou, Man verfasserin aut Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process. Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net Han, Lansheng verfasserin aut Lu, Hongwei verfasserin aut Fu, Cai verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 172 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:172 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.32 Rechnerkommunikation 53.76 Kommunikationsdienste Fernmeldetechnik AR 172
language	English
source	Enthalten in Computer networks 172 volume:172
sourceStr	Enthalten in Computer networks 172 volume:172
format_phy_str_mv	Article
bklname	Rechnerkommunikation Kommunikationsdienste Fernmeldetechnik
institution	findex.gbv.de
topic_facet	Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net
dewey-raw	004
isfreeaccess_bool	false
container_title	Computer networks
authorswithroles_txt_mv	Zhou, Man @@aut@@ Han, Lansheng @@aut@@ Lu, Hongwei @@aut@@ Fu, Cai @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	306652749
dewey-sort	14
id	ELV003977145
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV003977145</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524145032.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.comnet.2020.107174</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV003977145</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1389-1286(19)31187-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Man</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed collaborative intrusion detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vehicle Ad Hoc Network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reputation evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invariant detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic Petri Net</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Lansheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Hongwei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fu, Cai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computer networks</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1976</subfield><subfield code="g">172</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306652749</subfield><subfield code="w">(DE-600)1499744-7</subfield><subfield code="w">(DE-576)081954360</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:172</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.32</subfield><subfield code="j">Rechnerkommunikation</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.76</subfield><subfield code="j">Kommunikationsdienste</subfield><subfield code="j">Fernmeldetechnik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">172</subfield></datafield></record></collection>
author	Zhou, Man
spellingShingle	Zhou, Man ddc 004 bkl 54.32 bkl 53.76 misc Distributed collaborative intrusion detection misc Vehicle Ad Hoc Network misc Reputation evaluation misc Invariant detection misc Stochastic Petri Net Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant
authorStr	Zhou, Man
ppnlink_with_tag_str_mv	@@773@@(DE-627)306652749
format	electronic Article
dewey-ones	004 - Data processing & computer science 620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
topic_title	004 620 DE-600 54.32 bkl 53.76 bkl Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant Distributed collaborative intrusion detection Vehicle Ad Hoc Network Reputation evaluation Invariant detection Stochastic Petri Net
topic	ddc 004 bkl 54.32 bkl 53.76 misc Distributed collaborative intrusion detection misc Vehicle Ad Hoc Network misc Reputation evaluation misc Invariant detection misc Stochastic Petri Net
topic_unstemmed	ddc 004 bkl 54.32 bkl 53.76 misc Distributed collaborative intrusion detection misc Vehicle Ad Hoc Network misc Reputation evaluation misc Invariant detection misc Stochastic Petri Net
topic_browse	ddc 004 bkl 54.32 bkl 53.76 misc Distributed collaborative intrusion detection misc Vehicle Ad Hoc Network misc Reputation evaluation misc Invariant detection misc Stochastic Petri Net
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Computer networks
hierarchy_parent_id	306652749
dewey-tens	000 - Computer science, knowledge & systems 620 - Engineering
hierarchy_top_title	Computer networks
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360
title	Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant
ctrlnum	(DE-627)ELV003977145 (ELSEVIER)S1389-1286(19)31187-9
title_full	Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant
author_sort	Zhou, Man
journal	Computer networks
journalStr	Computer networks
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works 600 - Technology
recordtype	marc
publishDateSort	2020
contenttype_str_mv	zzz
author_browse	Zhou, Man Han, Lansheng Lu, Hongwei Fu, Cai
container_volume	172
class	004 620 DE-600 54.32 bkl 53.76 bkl
format_se	Elektronische Aufsätze
author-letter	Zhou, Man
doi_str_mv	10.1016/j.comnet.2020.107174
dewey-full	004 620
author2-role	verfasserin
title_sort	distributed collaborative intrusion detection system for vehicular ad hoc networks based on invariant
title_auth	Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant
abstract	The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process.
abstractGer	The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process.
abstract_unstemmed	The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process.
collection_details	GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant
remote_bool	true
author2	Han, Lansheng Lu, Hongwei Fu, Cai
author2Str	Han, Lansheng Lu, Hongwei Fu, Cai
ppnlink	306652749
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.comnet.2020.107174
up_date	2024-07-06T21:25:57.140Z
_version_	1803866506150281216
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV003977145</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524145032.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.comnet.2020.107174</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV003977145</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1389-1286(19)31187-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Man</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The characteristics of high mobility and rapid topology change of the Vehicle Ad Hoc Network (VANET) makes it vulnerable to various malicious attacks. The adversary utilizes the instability of the communication link induced by the frequent changes of topology structure to undermine the reliability and timeliness of vehicular communication, which raises serious security threats. In this paper, a distributed collaborative intrusion detection system based on invariant called DCDIV is proposed to identify betray attacks in VANET. Firstly, the paper designs a distributed collaborative detection framework to implement the storage and calculation of big data and the rapid tracking and collection of information. Secondly, considering the strict delay limitation and the high reliability requirement of information transmission between vehicles, a reputation-based cooperative communication method is exploited to establish a stable and reliable communication link, where a novel cluster head vehicle selection method based on global reputation state, traffic density, and link life is presented. Following this, the paper uses the dynamic behavior analysis technology to mine the invariant, which contributes to determine the normal driving characteristics of vehicles, so as to detect malicious behaviors. Finally, this paper utilizes the Stochastic Petri Net to describe the state of the system and its dynamic transfer, and then defines the security state of the system. The simulation results demonstrate that the DCDIV has higher detection rate, lower false alarm rate, and faster attack detection rate compared with existing methods, and ensures system security during the detection process.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed collaborative intrusion detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vehicle Ad Hoc Network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reputation evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Invariant detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic Petri Net</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Lansheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Hongwei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fu, Cai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computer networks</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1976</subfield><subfield code="g">172</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306652749</subfield><subfield code="w">(DE-600)1499744-7</subfield><subfield code="w">(DE-576)081954360</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:172</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.32</subfield><subfield code="j">Rechnerkommunikation</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.76</subfield><subfield code="j">Kommunikationsdienste</subfield><subfield code="j">Fernmeldetechnik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">172</subfield></datafield></record></collection>
score	7.4023495

Nicht das Richtige dabei?

Schreiben Sie uns!

Distributed collaborative intrusion detection system for vehicular Ad Hoc networks based on invariant

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?