SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning

Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these s...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Manimaran, S. [verfasserIn] Sastry, V.N. [verfasserIn] Gopalan, N.P. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Sensors Threats Smartphone Benign activity Malicious activity Classification

Übergeordnetes Werk:	Enthalten in: Computers & security - Amsterdam [u.a.] : Elsevier Science, 1982, 118
Übergeordnetes Werk:	volume:118

DOI / URN:	10.1016/j.cose.2022.102729

Katalog-ID:	ELV007933967

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520			\|a Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases.
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700	1		\|a Sastry, V.N. \|e verfasserin \|4 aut
700	1		\|a Gopalan, N.P. \|e verfasserin \|4 aut
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author_variant	s m sm v s vs n g ng
matchkey_str	manimaranssastryvngopalannp:2022----:bdlnvlrmwrfresraetraseetooadodmrp
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allfields	10.1016/j.cose.2022.102729 doi (DE-627)ELV007933967 (ELSEVIER)S0167-4048(22)00124-9 DE-627 ger DE-627 rda eng 004 DE-600 54.38 bkl Manimaran, S. verfasserin aut SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases. Sensors Threats Smartphone Benign activity Malicious activity Classification Sastry, V.N. verfasserin aut Gopalan, N.P. verfasserin aut Enthalten in Computers & security Amsterdam [u.a.] : Elsevier Science, 1982 118 Online-Ressource (DE-627)320415864 (DE-600)2001917-8 (DE-576)094531331 nnns volume:118 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 54.38 Computersicherheit AR 118
spelling	10.1016/j.cose.2022.102729 doi (DE-627)ELV007933967 (ELSEVIER)S0167-4048(22)00124-9 DE-627 ger DE-627 rda eng 004 DE-600 54.38 bkl Manimaran, S. verfasserin aut SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases. Sensors Threats Smartphone Benign activity Malicious activity Classification Sastry, V.N. verfasserin aut Gopalan, N.P. verfasserin aut Enthalten in Computers & security Amsterdam [u.a.] : Elsevier Science, 1982 118 Online-Ressource (DE-627)320415864 (DE-600)2001917-8 (DE-576)094531331 nnns volume:118 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 54.38 Computersicherheit AR 118
allfields_unstemmed	10.1016/j.cose.2022.102729 doi (DE-627)ELV007933967 (ELSEVIER)S0167-4048(22)00124-9 DE-627 ger DE-627 rda eng 004 DE-600 54.38 bkl Manimaran, S. verfasserin aut SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases. Sensors Threats Smartphone Benign activity Malicious activity Classification Sastry, V.N. verfasserin aut Gopalan, N.P. verfasserin aut Enthalten in Computers & security Amsterdam [u.a.] : Elsevier Science, 1982 118 Online-Ressource (DE-627)320415864 (DE-600)2001917-8 (DE-576)094531331 nnns volume:118 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 54.38 Computersicherheit AR 118
allfieldsGer	10.1016/j.cose.2022.102729 doi (DE-627)ELV007933967 (ELSEVIER)S0167-4048(22)00124-9 DE-627 ger DE-627 rda eng 004 DE-600 54.38 bkl Manimaran, S. verfasserin aut SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases. Sensors Threats Smartphone Benign activity Malicious activity Classification Sastry, V.N. verfasserin aut Gopalan, N.P. verfasserin aut Enthalten in Computers & security Amsterdam [u.a.] : Elsevier Science, 1982 118 Online-Ressource (DE-627)320415864 (DE-600)2001917-8 (DE-576)094531331 nnns volume:118 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 54.38 Computersicherheit AR 118
allfieldsSound	10.1016/j.cose.2022.102729 doi (DE-627)ELV007933967 (ELSEVIER)S0167-4048(22)00124-9 DE-627 ger DE-627 rda eng 004 DE-600 54.38 bkl Manimaran, S. verfasserin aut SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases. Sensors Threats Smartphone Benign activity Malicious activity Classification Sastry, V.N. verfasserin aut Gopalan, N.P. verfasserin aut Enthalten in Computers & security Amsterdam [u.a.] : Elsevier Science, 1982 118 Online-Ressource (DE-627)320415864 (DE-600)2001917-8 (DE-576)094531331 nnns volume:118 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_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_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_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 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 54.38 Computersicherheit AR 118
language	English
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author	Manimaran, S.
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title	SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning
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title_full	SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning
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title_sort	sbtddl: a novel framework for sensor-based threats detection on android smartphones using deep learning
title_auth	SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning
abstract	Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases.
abstractGer	Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases.
abstract_unstemmed	Sensors in the smartphone play a vital role in various user-friendly mobile services. The mobile application requires user permission to access the permission imposed sensors and not for other sensors. The sensors in the smartphone are vulnerable to various attacks. The attackers can exploit these sensors to trigger malware, extract the sensitive information of users and other nearby devices, and expose users’ confidential information. We propose SBTDDL, a novel context-aware framework for detecting sensor-based threats on Android smartphones using deep learning. In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases.
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title_short	SBTDDL: A novel framework for sensor-based threats detection on Android smartphones using deep learning
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up_date	2024-07-06T17:58:21.683Z
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In our work, (a) we identify the sensor-based threats by using the state (on and off) of the sensors in the smartphone for different user activities, (b) binary classification is performed in the sequence prediction model to classify the benign and malicious activities on the device, (c) SBTDDL performs better in detecting the sensor-based threats compared to the state-of-art existing methods by attaining the accuracy of 99% in identifying benign and malicious activities, (d) SBTDDL also detects the malicious activity occurring like benign activity, and the performance is not affected when the total number of benign and malicious activities increases.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Threats</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smartphone</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield 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