DUdetector: A dual-granularity unsupervised model for network anomaly detection
Internet of Things (IoT) devices are often used as springboards for network intrusion due to the open nature of IoT protocol stacks that enable automatic inter-connection and data sharing among devices, so it is critical to develop network anomaly detection algorithms that can be deployed at importa...
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| Vydáno v: | Computer networks (Amsterdam, Netherlands : 1999) Ročník 257; s. 110937 |
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| Jazyk: | angličtina |
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Elsevier B.V
01.02.2025
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| ISSN: | 1389-1286 |
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| Abstract | Internet of Things (IoT) devices are often used as springboards for network intrusion due to the open nature of IoT protocol stacks that enable automatic inter-connection and data sharing among devices, so it is critical to develop network anomaly detection algorithms that can be deployed at important nodes such as gateways and routers. However, existing detection algorithms based on signature rules and supervised machine learning heavily rely on known anomaly types, yielding low detection accuracy when deployed in realistic network environments with a significant number of unknown attacks. With this in mind, we propose DUdetector, an unsupervised anomaly detection algorithm by employing Transformer and Conv1d&MaxPool1d AutoEncoder with residual connection (abbr., CM&RC-AE) to realize a dual-granularity learning from the perspective of segments and points, respectively. Specifically, we perform coarse-grained segment-level anomaly detection based on an improved Transformer to detect whether there is any anomalous traffic within a time window. Then, we perform fine-grained point-level anomaly detection based on CM&RC-AE for each packet within the problematic segment output by the first step. Extensive experiments on three datasets (SSDP Flood, Mirai and IDS2017) demonstrate that our DUdetector achieves a better performance than existing work: an F1-score of 95.98% for Mirai, and over 99.2% for both SSDP Flood and IDS2017, with false positive rates less than 0.5% for all three datasets. |
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| AbstractList | Internet of Things (IoT) devices are often used as springboards for network intrusion due to the open nature of IoT protocol stacks that enable automatic inter-connection and data sharing among devices, so it is critical to develop network anomaly detection algorithms that can be deployed at important nodes such as gateways and routers. However, existing detection algorithms based on signature rules and supervised machine learning heavily rely on known anomaly types, yielding low detection accuracy when deployed in realistic network environments with a significant number of unknown attacks. With this in mind, we propose DUdetector, an unsupervised anomaly detection algorithm by employing Transformer and Conv1d&MaxPool1d AutoEncoder with residual connection (abbr., CM&RC-AE) to realize a dual-granularity learning from the perspective of segments and points, respectively. Specifically, we perform coarse-grained segment-level anomaly detection based on an improved Transformer to detect whether there is any anomalous traffic within a time window. Then, we perform fine-grained point-level anomaly detection based on CM&RC-AE for each packet within the problematic segment output by the first step. Extensive experiments on three datasets (SSDP Flood, Mirai and IDS2017) demonstrate that our DUdetector achieves a better performance than existing work: an F1-score of 95.98% for Mirai, and over 99.2% for both SSDP Flood and IDS2017, with false positive rates less than 0.5% for all three datasets. |
| ArticleNumber | 110937 |
| Author | Ma, Qi Geng, Haijun Chi, Haotian Yang, Jing Yin, Xia Zhang, Zhi |
| Author_xml | – sequence: 1 givenname: Haijun surname: Geng fullname: Geng, Haijun email: genghaijun@sxu.edu.cn organization: School of Automation and Software Engineering, Shanxi University, Taiyuan, 030006, China – sequence: 2 givenname: Qi surname: Ma fullname: Ma, Qi organization: School of Automation and Software Engineering, Shanxi University, Taiyuan, 030006, China – sequence: 3 givenname: Haotian surname: Chi fullname: Chi, Haotian organization: School of Automation and Software Engineering, Shanxi University, Taiyuan, 030006, China – sequence: 4 givenname: Zhi surname: Zhang fullname: Zhang, Zhi organization: School of Automation and Software Engineering, Shanxi University, Taiyuan, 030006, China – sequence: 5 givenname: Jing surname: Yang fullname: Yang, Jing organization: School of Automation and Software Engineering, Shanxi University, Taiyuan, 030006, China – sequence: 6 givenname: Xia surname: Yin fullname: Yin, Xia organization: Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China |
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| Cites_doi | 10.1109/COMST.2015.2494502 10.14722/ndss.2018.23204 10.3390/s24020713 10.1145/3178876.3185996 10.1145/2924715.2924719 10.1145/3336191.3371876 10.1109/JIOT.2021.3100509 10.1145/948143.948145 10.1145/342009.335388 10.1007/s11227-019-02805-w 10.1145/3395351.3399421 10.1609/aaai.v35i12.17325 10.1145/2897845.2897860 10.1145/3313391 10.1007/978-3-319-24574-4_28 10.3923/itj.2011.648.655 10.1145/948234.948236 10.1109/COMST.2018.2863942 10.4108/eai.3-12-2015.2262516 10.1109/JIOT.2020.3009180 10.1145/3133956.3134015 10.1145/3460120.3484589 10.1145/3243734.3243862 10.1109/ICCV48922.2021.00986 10.1109/ACCESS.2017.2747560 10.1145/3319535.3363226 10.1016/j.iot.2023.100851 10.1145/3460120.3484585 10.1109/SURV.2013.052213.00046 10.14722/ndss.2024.23216 10.1016/j.comnet.2019.107049 10.1109/INFOCOM41043.2020.9155278 10.1016/j.asoc.2022.108768 10.1016/j.eswa.2022.119330 10.14722/ndss.2014.23269 |
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| Keywords | Transformer Dual-granularity Internet of things attack AutoEncoder Unsupervised anomaly detection |
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| References | R. Wang, K. Nie, T. Wang, Y. Yang, B. Long, Deep learning for anomaly detection, in: Proceedings of the 13th International Conference on Web Search and Data Mining, 2020. Meng, Liu, Zhu, Zhang, Pei, Liu, Chen, Zhang, Tao, Sun (b38) 2019 A. Acar, H. Fereidooni, T. Abera, A.K. Sikder, M. Miettinen, H. Aksu, M. Conti, A.-R. Sadeghi, S. Uluagac, Peek-a-boo: I see your smart home activities, even encrypted!, in: Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2020. Pervez, Farid (b56) 2014 Nguyen, Le (b22) 2023; 23 Muda, Yassin, Sulaiman, Udzir (b8) 2011; 10 Roesch (b14) 1999 B. Krishnamurthy, S. Sen, Y. Zhang, Y. Chen, Sketch-based change detection: Methods, evaluation, and applications, in: Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement, 2003. H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, et al., Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in: Proceedings of the 2018 World Wide Web Conference, 2018. Yu (b11) 2012; 4 D. Han, Z. Wang, W. Chen, Y. Zhong, S. Wang, H. Zhang, J. Yang, X. Shi, X. Yin, Deepaid: Interpreting and improving deep learning-based anomaly detection in security applications, in: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021. T. Nelms, R. Perdisci, M. Antonakakis, M. Ahamad, WebWitness: Investigating, Categorizing, and Mitigating Malware Download Paths, in: 24th USENIX Security Symposium, USENIX Security 15, 2015. H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, Informer: Beyond efficient transformer for long sequence time-series forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. R. Child, S. Gray, A. Radford, I. Sutskever, Generating long sequences with sparse transformers, arXiv preprint C.A. Huang, A. Vaswani, J. Uszkoreit, I. Simon, C. Hawthorne, N. Shazeer, A.M. Dai, M.D. Hoffman, M. Dinculescu, D. Eck, Music Transformer: Generating Music with Long-Term Structure, in: 7th International Conference on Learning Representations, 2019. M. Du, Z. Chen, C. Liu, R. Oak, D. Song, Lifelong anomaly detection through unlearning, in: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019. O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI: 18th International Conference, 2015. J. Devlin, M. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies(NAACL-HLT), 2019. Zhang, Cole, Ge, Wei, Yu, Lu, Chen, Shen, Blasch, Pham (b31) 2016; 16 Waswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (b40) 2017 Beltagy, Peters, Cohan (b54) 2020 M. Antonakakis, R. Perdisci, Y. Nadji, N. Vasiloglou, S. Abu-Nimeh, W. Lee, D. Dagon, From throw-away traffic to bots: detecting the rise of DGA-based malware, in: Proceedings of the 21st USENIX Conference on Security Symposium, 2012. . C. Fu, Q. Li, M. Shen, K. Xu, Realtime robust malicious traffic detection via frequency domain analysis, in: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021, pp. 3431–3446. Altulaihan, Almaiah, Aljughaiman (b4) 2024; 24 (b15) 2024 Khanday, Fatima, Rakesh (b3) 2023; 215 Zhong, Chen, Wang, Chen, Wang, Li, Yin, Shi, Yang, Li (b17) 2020; 169 N. Kitaev, L. Kaiser, A. Levskaya, Reformer: The Efficient Transformer, in: 8th International Conference on Learning Representations, 2020. Mushtaq, Zameer, Umer, Abbasi (b57) 2022; 121 M. Du, F. Li, G. Zheng, V. Srikumar, Deeplog: Anomaly detection and diagnosis from system logs through deep learning, in: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017. J. Xu, H. Wu, J. Wang, M. Long, Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, in: International Conference on Learning Representations, 2022. H. Li, H. Hu, G. Gu, G.-J. Ahn, F. Zhang, vNIDS: Towards elastic security with safe and efficient virtualization of network intrusion detection systems, in: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2018. Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. A. Javaid, Q. Niyaz, W. Sun, M. Alam, A deep learning approach for network intrusion detection system, in: Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, 2016. Bhuyan, Bhattacharyya, Kalita (b28) 2013; 16 (b1) 2023 S. Chen, M. Xue, Z. Tang, L. Xu, H. Zhu, Stormdroid: A streaminglized machine learning-based system for detecting android malware, in: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016. Lopez-Martin, Carro, Sanchez-Esguevillas, Lloret (b18) 2017; 5 M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: identifying density-based local outliers, in: ACM SIGMOD International Conference on Management of Data, 2000. R. Tang, Z. Yang, Z. Li, W. Meng, H. Wang, Q. Li, Y. Sun, D. Pei, T. Wei, Y. Xu, et al., Zerowall: Detecting zero-day web attacks through encoder-decoder recurrent neural networks, in: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 2020. Gao, Gan, Buschendorf, Zhang, Liu, Li, Dong, Lu (b58) 2020; 8 L. Invernizzi, S. Miskovic, R. Torres, C. Kruegel, S. Saha, G. Vigna, S. Lee, M. Mellia, Nazca: Detecting Malware Distribution in Large-Scale Networks, in: 21st Annual Network and Distributed System Security Symposium, NDSS, 2014. Buczak, Guven (b36) 2016; 18 Brown, Mann, Ryder, Subbiah, Kaplan, Dhariwal, Neelakantan, Shyam, Sastry, Askell (b42) 2020; 33 Choi, Kim, Lee, Kim (b26) 2019; 75 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: 9th International Conference on Learning Representations, 2021. (b2) 2023 Y. Mirsky, T. Doitshman, Y. Elovici, A. Shabtai, Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection, in: 25th Annual Network and Distributed System Security Symposium, NDSS, 2018. C. Fung, E. Zeng, L. Bauer, Attributions for ML-based ICS Anomaly Detection: From Theory to Practice, in: 31st Annual Network and Distributed System Security Symposium, NDSS, 2024. W. Lee, D. Xiang, Information-theoretic measures for anomaly detection, in: Proceedings IEEE Symposium on Security and Privacy, S&P, 2000. R. Sommer, V. Paxson, Enhancing byte-level network intrusion detection signatures with context, in: 10th ACM Conference on Computer and Communications Security, 2003. Chen, Chen, Zhang, Yuan, Cheng (b50) 2021; 9 Onwuzurike, Mariconti, Andriotis, Cristofaro, Ross, Stringhini (b32) 2019; 22 Li, Jin, Xuan, Zhou, Chen, Wang, Yan (b44) 2019; 32 K. Borders, J. Springer, M. Burnside, Chimera: A declarative language for streaming network traffic analysis, in: 21st USENIX Security Symposium (USENIX Security), 2012. Jing, Yan, Pedrycz (b27) 2018; 21 Wu, Xu, Wang, Long (b46) 2021; 34 Buczak, Guven (b13) 2015; 18 Muda (10.1016/j.comnet.2024.110937_b8) 2011; 10 10.1016/j.comnet.2024.110937_b34 10.1016/j.comnet.2024.110937_b33 10.1016/j.comnet.2024.110937_b35 10.1016/j.comnet.2024.110937_b30 Li (10.1016/j.comnet.2024.110937_b44) 2019; 32 Pervez (10.1016/j.comnet.2024.110937_b56) 2014 Beltagy (10.1016/j.comnet.2024.110937_b54) 2020 Altulaihan (10.1016/j.comnet.2024.110937_b4) 2024; 24 10.1016/j.comnet.2024.110937_b37 10.1016/j.comnet.2024.110937_b39 Mushtaq (10.1016/j.comnet.2024.110937_b57) 2022; 121 Jing (10.1016/j.comnet.2024.110937_b27) 2018; 21 Buczak (10.1016/j.comnet.2024.110937_b13) 2015; 18 Zhong (10.1016/j.comnet.2024.110937_b17) 2020; 169 10.1016/j.comnet.2024.110937_b23 10.1016/j.comnet.2024.110937_b25 10.1016/j.comnet.2024.110937_b24 10.1016/j.comnet.2024.110937_b21 10.1016/j.comnet.2024.110937_b20 Chen (10.1016/j.comnet.2024.110937_b50) 2021; 9 10.1016/j.comnet.2024.110937_b29 (10.1016/j.comnet.2024.110937_b1) 2023 (10.1016/j.comnet.2024.110937_b15) 2024 Lopez-Martin (10.1016/j.comnet.2024.110937_b18) 2017; 5 Waswani (10.1016/j.comnet.2024.110937_b40) 2017 Khanday (10.1016/j.comnet.2024.110937_b3) 2023; 215 10.1016/j.comnet.2024.110937_b12 10.1016/j.comnet.2024.110937_b55 10.1016/j.comnet.2024.110937_b52 Zhang (10.1016/j.comnet.2024.110937_b31) 2016; 16 10.1016/j.comnet.2024.110937_b51 10.1016/j.comnet.2024.110937_b10 Roesch (10.1016/j.comnet.2024.110937_b14) 1999 10.1016/j.comnet.2024.110937_b53 10.1016/j.comnet.2024.110937_b19 Choi (10.1016/j.comnet.2024.110937_b26) 2019; 75 Brown (10.1016/j.comnet.2024.110937_b42) 2020; 33 10.1016/j.comnet.2024.110937_b16 Yu (10.1016/j.comnet.2024.110937_b11) 2012; 4 Wu (10.1016/j.comnet.2024.110937_b46) 2021; 34 (10.1016/j.comnet.2024.110937_b2) 2023 Bhuyan (10.1016/j.comnet.2024.110937_b28) 2013; 16 Nguyen (10.1016/j.comnet.2024.110937_b22) 2023; 23 Buczak (10.1016/j.comnet.2024.110937_b36) 2016; 18 10.1016/j.comnet.2024.110937_b5 10.1016/j.comnet.2024.110937_b45 10.1016/j.comnet.2024.110937_b7 10.1016/j.comnet.2024.110937_b47 10.1016/j.comnet.2024.110937_b6 10.1016/j.comnet.2024.110937_b9 10.1016/j.comnet.2024.110937_b41 10.1016/j.comnet.2024.110937_b43 Gao (10.1016/j.comnet.2024.110937_b58) 2020; 8 10.1016/j.comnet.2024.110937_b49 10.1016/j.comnet.2024.110937_b48 Onwuzurike (10.1016/j.comnet.2024.110937_b32) 2019; 22 Meng (10.1016/j.comnet.2024.110937_b38) 2019 |
| References_xml | – reference: T. Nelms, R. Perdisci, M. Antonakakis, M. Ahamad, WebWitness: Investigating, Categorizing, and Mitigating Malware Download Paths, in: 24th USENIX Security Symposium, USENIX Security 15, 2015. – reference: H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng, et al., Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in: Proceedings of the 2018 World Wide Web Conference, 2018. – volume: 24 start-page: 713 year: 2024 ident: b4 article-title: Anomaly detection IDS for detecting DoS attacks in IoT networks based on machine learning algorithms publication-title: Sensors – year: 2019 ident: b38 article-title: Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs publication-title: IJCAI – reference: O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI: 18th International Conference, 2015. – volume: 4 start-page: 280 year: 2012 end-page: 288 ident: b11 article-title: A nonparametric adaptive CUSUM method and its application in network anomaly detection publication-title: Int. J. Adv. Comput. Technol – reference: R. Wang, K. Nie, T. Wang, Y. Yang, B. Long, Deep learning for anomaly detection, in: Proceedings of the 13th International Conference on Web Search and Data Mining, 2020. – year: 2017 ident: b40 article-title: Attention is all you need publication-title: NIPS – volume: 5 start-page: 18042 year: 2017 end-page: 18050 ident: b18 article-title: Network traffic classifier with convolutional and recurrent neural networks for internet of things publication-title: IEEE Access – reference: A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: 9th International Conference on Learning Representations, 2021. – reference: R. Tang, Z. Yang, Z. Li, W. Meng, H. Wang, Q. Li, Y. Sun, D. Pei, T. Wei, Y. Xu, et al., Zerowall: Detecting zero-day web attacks through encoder-decoder recurrent neural networks, in: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 2020. – reference: R. Sommer, V. Paxson, Enhancing byte-level network intrusion detection signatures with context, in: 10th ACM Conference on Computer and Communications Security, 2003. – year: 2020 ident: b54 article-title: Longformer: The long-document transformer – volume: 9 start-page: 9179 year: 2021 end-page: 9189 ident: b50 article-title: Learning graph structures with transformer for multivariate time-series anomaly detection in IoT publication-title: IEEE Internet Things J. – year: 2023 ident: b2 article-title: The state of IoT security – volume: 22 start-page: 1 year: 2019 end-page: 34 ident: b32 article-title: Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version) publication-title: ACM Trans. Priv. Secur. – reference: Y. Mirsky, T. Doitshman, Y. Elovici, A. Shabtai, Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection, in: 25th Annual Network and Distributed System Security Symposium, NDSS, 2018. – reference: M. Du, Z. Chen, C. Liu, R. Oak, D. Song, Lifelong anomaly detection through unlearning, in: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019. – volume: 16 start-page: 303 year: 2013 end-page: 336 ident: b28 article-title: Network anomaly detection: methods, systems and tools publication-title: IEEE Commun. Surv. Tutor. – volume: 10 start-page: 648 year: 2011 end-page: 655 ident: b8 article-title: A K-means and naive Bayes learning approach for better intrusion detection publication-title: Inf. Technol. J. – reference: A. Acar, H. Fereidooni, T. Abera, A.K. Sikder, M. Miettinen, H. Aksu, M. Conti, A.-R. Sadeghi, S. Uluagac, Peek-a-boo: I see your smart home activities, even encrypted!, in: Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2020. – reference: J. Xu, H. Wu, J. Wang, M. Long, Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, in: International Conference on Learning Representations, 2022. – reference: C. Fung, E. Zeng, L. Bauer, Attributions for ML-based ICS Anomaly Detection: From Theory to Practice, in: 31st Annual Network and Distributed System Security Symposium, NDSS, 2024. – reference: L. Invernizzi, S. Miskovic, R. Torres, C. Kruegel, S. Saha, G. Vigna, S. Lee, M. Mellia, Nazca: Detecting Malware Distribution in Large-Scale Networks, in: 21st Annual Network and Distributed System Security Symposium, NDSS, 2014. – volume: 18 start-page: 1153 year: 2015 end-page: 1176 ident: b13 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. – reference: R. Child, S. Gray, A. Radford, I. Sutskever, Generating long sequences with sparse transformers, arXiv preprint – year: 2023 ident: b1 article-title: The state of IoT – year: 2024 ident: b15 article-title: Suricata: An open source threat detection engine – reference: K. Borders, J. Springer, M. Burnside, Chimera: A declarative language for streaming network traffic analysis, in: 21st USENIX Security Symposium (USENIX Security), 2012. – volume: 33 start-page: 1877 year: 2020 end-page: 1901 ident: b42 article-title: Language models are few-shot learners publication-title: Adv. Neural Inf. Process. Syst. – volume: 34 start-page: 22419 year: 2021 end-page: 22430 ident: b46 article-title: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting publication-title: Adv. Neural Inf. Process. Syst. – year: 2014 ident: b56 article-title: Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs publication-title: The 8th International Conference on Software, Knowledge, Information Management and Applications – reference: C.A. Huang, A. Vaswani, J. Uszkoreit, I. Simon, C. Hawthorne, N. Shazeer, A.M. Dai, M.D. Hoffman, M. Dinculescu, D. Eck, Music Transformer: Generating Music with Long-Term Structure, in: 7th International Conference on Learning Representations, 2019. – reference: M. Du, F. Li, G. Zheng, V. Srikumar, Deeplog: Anomaly detection and diagnosis from system logs through deep learning, in: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017. – reference: A. Javaid, Q. Niyaz, W. Sun, M. Alam, A deep learning approach for network intrusion detection system, in: Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, 2016. – reference: M. Antonakakis, R. Perdisci, Y. Nadji, N. Vasiloglou, S. Abu-Nimeh, W. Lee, D. Dagon, From throw-away traffic to bots: detecting the rise of DGA-based malware, in: Proceedings of the 21st USENIX Conference on Security Symposium, 2012. – volume: 8 start-page: 951 year: 2020 end-page: 961 ident: b58 article-title: Omni SCADA intrusion detection using deep learning algorithms publication-title: IEEE Internet Things J. – reference: H. Li, H. Hu, G. Gu, G.-J. Ahn, F. Zhang, vNIDS: Towards elastic security with safe and efficient virtualization of network intrusion detection systems, in: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2018. – reference: B. Krishnamurthy, S. Sen, Y. Zhang, Y. Chen, Sketch-based change detection: Methods, evaluation, and applications, in: Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement, 2003. – reference: H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, Informer: Beyond efficient transformer for long sequence time-series forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2021. – volume: 121 year: 2022 ident: b57 article-title: A two-stage intrusion detection system with auto-encoder and LSTMs publication-title: Appl. Soft Comput. – reference: S. Chen, M. Xue, Z. Tang, L. Xu, H. Zhu, Stormdroid: A streaminglized machine learning-based system for detecting android malware, in: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016. – volume: 169 year: 2020 ident: b17 article-title: HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning publication-title: Comput. Netw. – reference: C. Fu, Q. Li, M. Shen, K. Xu, Realtime robust malicious traffic detection via frequency domain analysis, in: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021, pp. 3431–3446. – reference: W. Lee, D. Xiang, Information-theoretic measures for anomaly detection, in: Proceedings IEEE Symposium on Security and Privacy, S&P, 2000. – reference: N. Kitaev, L. Kaiser, A. Levskaya, Reformer: The Efficient Transformer, in: 8th International Conference on Learning Representations, 2020. – volume: 18 start-page: 1153 year: 2016 end-page: 1176 ident: b36 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. – reference: M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: identifying density-based local outliers, in: ACM SIGMOD International Conference on Management of Data, 2000. – volume: 16 start-page: 36 year: 2016 end-page: 49 ident: b31 article-title: ScanMe mobile: a cloud-based android malware analysis service publication-title: ACM SIGAPP Appl. Comput. Rev. – volume: 215 year: 2023 ident: b3 article-title: Implementation of intrusion detection model for ddos attacks in lightweight IoT networks publication-title: Expert Syst. Appl. – reference: Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. – volume: 75 start-page: 5597 year: 2019 end-page: 5621 ident: b26 article-title: Unsupervised learning approach for network intrusion detection system using autoencoders publication-title: J. Supercomput. – reference: . – year: 1999 ident: b14 article-title: Snort: Lightweight intrusion detection for networks. publication-title: Lisa – reference: J. Devlin, M. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies(NAACL-HLT), 2019. – volume: 21 start-page: 586 year: 2018 end-page: 618 ident: b27 article-title: Security data collection and data analytics in the internet: A survey publication-title: IEEE Commun. Surv. Tutor. – reference: D. Han, Z. Wang, W. Chen, Y. Zhong, S. Wang, H. Zhang, J. Yang, X. Shi, X. Yin, Deepaid: Interpreting and improving deep learning-based anomaly detection in security applications, in: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021. – volume: 32 year: 2019 ident: b44 article-title: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting publication-title: Adv. Neural Inf. Process. Syst. – volume: 23 year: 2023 ident: b22 article-title: Robust detection of unknown dos/ddos attacks in IoT networks using a hybrid learning model publication-title: Internet Things – ident: 10.1016/j.comnet.2024.110937_b9 – ident: 10.1016/j.comnet.2024.110937_b34 – ident: 10.1016/j.comnet.2024.110937_b30 – year: 2019 ident: 10.1016/j.comnet.2024.110937_b38 article-title: Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs – volume: 18 start-page: 1153 issue: 2 year: 2016 ident: 10.1016/j.comnet.2024.110937_b36 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2015.2494502 – volume: 4 start-page: 280 issue: 1 year: 2012 ident: 10.1016/j.comnet.2024.110937_b11 article-title: A nonparametric adaptive CUSUM method and its application in network anomaly detection publication-title: Int. J. Adv. Comput. Technol – ident: 10.1016/j.comnet.2024.110937_b25 doi: 10.14722/ndss.2018.23204 – year: 2014 ident: 10.1016/j.comnet.2024.110937_b56 article-title: Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs – volume: 24 start-page: 713 issue: 2 year: 2024 ident: 10.1016/j.comnet.2024.110937_b4 article-title: Anomaly detection IDS for detecting DoS attacks in IoT networks based on machine learning algorithms publication-title: Sensors doi: 10.3390/s24020713 – ident: 10.1016/j.comnet.2024.110937_b39 doi: 10.1145/3178876.3185996 – ident: 10.1016/j.comnet.2024.110937_b47 – volume: 16 start-page: 36 issue: 1 year: 2016 ident: 10.1016/j.comnet.2024.110937_b31 article-title: ScanMe mobile: a cloud-based android malware analysis service publication-title: ACM SIGAPP Appl. Comput. Rev. doi: 10.1145/2924715.2924719 – ident: 10.1016/j.comnet.2024.110937_b6 doi: 10.1145/3336191.3371876 – volume: 9 start-page: 9179 issue: 12 year: 2021 ident: 10.1016/j.comnet.2024.110937_b50 article-title: Learning graph structures with transformer for multivariate time-series anomaly detection in IoT publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3100509 – ident: 10.1016/j.comnet.2024.110937_b16 doi: 10.1145/948143.948145 – volume: 32 year: 2019 ident: 10.1016/j.comnet.2024.110937_b44 article-title: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.comnet.2024.110937_b7 doi: 10.1145/342009.335388 – volume: 75 start-page: 5597 year: 2019 ident: 10.1016/j.comnet.2024.110937_b26 article-title: Unsupervised learning approach for network intrusion detection system using autoencoders publication-title: J. Supercomput. doi: 10.1007/s11227-019-02805-w – ident: 10.1016/j.comnet.2024.110937_b52 doi: 10.1145/3395351.3399421 – volume: 33 start-page: 1877 year: 2020 ident: 10.1016/j.comnet.2024.110937_b42 article-title: Language models are few-shot learners publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.comnet.2024.110937_b45 doi: 10.1609/aaai.v35i12.17325 – year: 2020 ident: 10.1016/j.comnet.2024.110937_b54 – ident: 10.1016/j.comnet.2024.110937_b12 doi: 10.1145/2897845.2897860 – volume: 22 start-page: 1 issue: 2 year: 2019 ident: 10.1016/j.comnet.2024.110937_b32 article-title: Mamadroid: Detecting android malware by building markov chains of behavioral models (extended version) publication-title: ACM Trans. Priv. Secur. doi: 10.1145/3313391 – ident: 10.1016/j.comnet.2024.110937_b55 doi: 10.1007/978-3-319-24574-4_28 – ident: 10.1016/j.comnet.2024.110937_b41 – year: 2023 ident: 10.1016/j.comnet.2024.110937_b1 – volume: 10 start-page: 648 issue: 3 year: 2011 ident: 10.1016/j.comnet.2024.110937_b8 article-title: A K-means and naive Bayes learning approach for better intrusion detection publication-title: Inf. Technol. J. doi: 10.3923/itj.2011.648.655 – ident: 10.1016/j.comnet.2024.110937_b48 – ident: 10.1016/j.comnet.2024.110937_b10 doi: 10.1145/948234.948236 – ident: 10.1016/j.comnet.2024.110937_b51 – volume: 21 start-page: 586 issue: 1 year: 2018 ident: 10.1016/j.comnet.2024.110937_b27 article-title: Security data collection and data analytics in the internet: A survey publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2018.2863942 – year: 2023 ident: 10.1016/j.comnet.2024.110937_b2 – volume: 34 start-page: 22419 year: 2021 ident: 10.1016/j.comnet.2024.110937_b46 article-title: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting publication-title: Adv. Neural Inf. Process. Syst. – ident: 10.1016/j.comnet.2024.110937_b20 doi: 10.4108/eai.3-12-2015.2262516 – volume: 8 start-page: 951 issue: 2 year: 2020 ident: 10.1016/j.comnet.2024.110937_b58 article-title: Omni SCADA intrusion detection using deep learning algorithms publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3009180 – ident: 10.1016/j.comnet.2024.110937_b19 doi: 10.1145/3133956.3134015 – ident: 10.1016/j.comnet.2024.110937_b23 doi: 10.1145/3460120.3484589 – year: 2017 ident: 10.1016/j.comnet.2024.110937_b40 article-title: Attention is all you need – ident: 10.1016/j.comnet.2024.110937_b29 doi: 10.1145/3243734.3243862 – year: 1999 ident: 10.1016/j.comnet.2024.110937_b14 article-title: Snort: Lightweight intrusion detection for networks. – ident: 10.1016/j.comnet.2024.110937_b49 doi: 10.1109/ICCV48922.2021.00986 – volume: 5 start-page: 18042 year: 2017 ident: 10.1016/j.comnet.2024.110937_b18 article-title: Network traffic classifier with convolutional and recurrent neural networks for internet of things publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2747560 – volume: 18 start-page: 1153 issue: 2 year: 2015 ident: 10.1016/j.comnet.2024.110937_b13 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2015.2494502 – ident: 10.1016/j.comnet.2024.110937_b21 doi: 10.1145/3319535.3363226 – volume: 23 year: 2023 ident: 10.1016/j.comnet.2024.110937_b22 article-title: Robust detection of unknown dos/ddos attacks in IoT networks using a hybrid learning model publication-title: Internet Things doi: 10.1016/j.iot.2023.100851 – ident: 10.1016/j.comnet.2024.110937_b33 – ident: 10.1016/j.comnet.2024.110937_b37 doi: 10.1145/3460120.3484585 – volume: 16 start-page: 303 issue: 1 year: 2013 ident: 10.1016/j.comnet.2024.110937_b28 article-title: Network anomaly detection: methods, systems and tools publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/SURV.2013.052213.00046 – ident: 10.1016/j.comnet.2024.110937_b5 doi: 10.14722/ndss.2024.23216 – volume: 169 year: 2020 ident: 10.1016/j.comnet.2024.110937_b17 article-title: HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning publication-title: Comput. Netw. doi: 10.1016/j.comnet.2019.107049 – ident: 10.1016/j.comnet.2024.110937_b24 doi: 10.1109/INFOCOM41043.2020.9155278 – volume: 121 year: 2022 ident: 10.1016/j.comnet.2024.110937_b57 article-title: A two-stage intrusion detection system with auto-encoder and LSTMs publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.108768 – ident: 10.1016/j.comnet.2024.110937_b43 – year: 2024 ident: 10.1016/j.comnet.2024.110937_b15 – volume: 215 year: 2023 ident: 10.1016/j.comnet.2024.110937_b3 article-title: Implementation of intrusion detection model for ddos attacks in lightweight IoT networks publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119330 – ident: 10.1016/j.comnet.2024.110937_b35 doi: 10.14722/ndss.2014.23269 – ident: 10.1016/j.comnet.2024.110937_b53 |
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