Cross-domain network attack detection enabled by heterogeneous transfer learning
In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attac...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Chunrui [verfasserIn] Wang, Gang [verfasserIn] Wang, Shen [verfasserIn] Zhan, Dechen [verfasserIn] Yin, Mingyong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computer networks - Amsterdam [u.a.] : Elsevier, 1976, 227 |
|---|---|
| Übergeordnetes Werk: |
volume:227 |
| DOI / URN: |
10.1016/j.comnet.2023.109692 |
|---|
| Katalog-ID: |
ELV066054214 |
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| 245 | 1 | 0 | |a Cross-domain network attack detection enabled by heterogeneous transfer learning |
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| 520 | |a In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. | ||
| 650 | 4 | |a Attack detection | |
| 650 | 4 | |a Transfer learning | |
| 650 | 4 | |a Heterogeneous datasets | |
| 650 | 4 | |a Cross domain | |
| 700 | 1 | |a Wang, Gang |e verfasserin |0 (orcid)0000-0002-8493-8236 |4 aut | |
| 700 | 1 | |a Wang, Shen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Dechen |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Mingyong |e verfasserin |4 aut | |
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10.1016/j.comnet.2023.109692 doi (DE-627)ELV066054214 (ELSEVIER)S1389-1286(23)00137-8 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Zhang, Chunrui verfasserin (orcid)0000-0001-5612-6516 aut Cross-domain network attack detection enabled by heterogeneous transfer learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. Attack detection Transfer learning Heterogeneous datasets Cross domain Wang, Gang verfasserin (orcid)0000-0002-8493-8236 aut Wang, Shen verfasserin aut Zhan, Dechen verfasserin aut Yin, Mingyong verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 227 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 227 |
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10.1016/j.comnet.2023.109692 doi (DE-627)ELV066054214 (ELSEVIER)S1389-1286(23)00137-8 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Zhang, Chunrui verfasserin (orcid)0000-0001-5612-6516 aut Cross-domain network attack detection enabled by heterogeneous transfer learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. Attack detection Transfer learning Heterogeneous datasets Cross domain Wang, Gang verfasserin (orcid)0000-0002-8493-8236 aut Wang, Shen verfasserin aut Zhan, Dechen verfasserin aut Yin, Mingyong verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 227 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 227 |
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10.1016/j.comnet.2023.109692 doi (DE-627)ELV066054214 (ELSEVIER)S1389-1286(23)00137-8 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Zhang, Chunrui verfasserin (orcid)0000-0001-5612-6516 aut Cross-domain network attack detection enabled by heterogeneous transfer learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. Attack detection Transfer learning Heterogeneous datasets Cross domain Wang, Gang verfasserin (orcid)0000-0002-8493-8236 aut Wang, Shen verfasserin aut Zhan, Dechen verfasserin aut Yin, Mingyong verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 227 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 227 |
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10.1016/j.comnet.2023.109692 doi (DE-627)ELV066054214 (ELSEVIER)S1389-1286(23)00137-8 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Zhang, Chunrui verfasserin (orcid)0000-0001-5612-6516 aut Cross-domain network attack detection enabled by heterogeneous transfer learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. Attack detection Transfer learning Heterogeneous datasets Cross domain Wang, Gang verfasserin (orcid)0000-0002-8493-8236 aut Wang, Shen verfasserin aut Zhan, Dechen verfasserin aut Yin, Mingyong verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 227 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 227 |
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10.1016/j.comnet.2023.109692 doi (DE-627)ELV066054214 (ELSEVIER)S1389-1286(23)00137-8 DE-627 ger DE-627 rda eng 004 620 VZ 54.32 bkl 53.76 bkl Zhang, Chunrui verfasserin (orcid)0000-0001-5612-6516 aut Cross-domain network attack detection enabled by heterogeneous transfer learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. Attack detection Transfer learning Heterogeneous datasets Cross domain Wang, Gang verfasserin (orcid)0000-0002-8493-8236 aut Wang, Shen verfasserin aut Zhan, Dechen verfasserin aut Yin, Mingyong verfasserin aut Enthalten in Computer networks Amsterdam [u.a.] : Elsevier, 1976 227 Online-Ressource (DE-627)306652749 (DE-600)1499744-7 (DE-576)081954360 nnns volume:227 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 Rechnerkommunikation VZ 53.76 Kommunikationsdienste Fernmeldetechnik VZ AR 227 |
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004 620 VZ 54.32 bkl 53.76 bkl Cross-domain network attack detection enabled by heterogeneous transfer learning Attack detection Transfer learning Heterogeneous datasets Cross domain |
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Cross-domain network attack detection enabled by heterogeneous transfer learning |
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Zhang, Chunrui Wang, Gang Wang, Shen Zhan, Dechen Yin, Mingyong |
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10.1016/j.comnet.2023.109692 |
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cross-domain network attack detection enabled by heterogeneous transfer learning |
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Cross-domain network attack detection enabled by heterogeneous transfer learning |
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In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. |
| abstractGer |
In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. |
| abstract_unstemmed |
In recent years, cybersecurity has been endlessly challenged by more and more sophisticated network attacks, due to lacking the ability to detect unknown attacks in time. Recent researches show that machine learning helps to improve the efficacy of network attack detection, by training network attack classification models with huge amount labeled data. However in internal networks, due to the scarcity of attack instances and lacking expert labor force to label the data, it is always difficult to obtain sufficient labeled data to train such models. To uncover unknown attacks with machine learning techniques in internal networks, we propose to exploit transfer learning to utilize public datasets that contains attack instances to train a prediction model that will be used for un-labeled internal datasets. The main problem is to address the heterogeneity between datasets. Specifically, we project two heterogeneous datasets into a common latent space and formulate an optimization problem to minimize the distance of two distributions in the common space. Then we apply MLP classifier to the projected data to identify attack instances in internal networks. We conduct experiments that perform transfer learning between the NSLKDD to UNSW-NB15 datasets. The results validate that the proposed method notably improves the cross-domain attack detection accuracy in learning scenarios, such as “DoS to DoS” and “R2L to Exploits”. |
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Cross-domain network attack detection enabled by heterogeneous transfer learning |
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Wang, Gang Wang, Shen Zhan, Dechen Yin, Mingyong |
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