Abstract
IoT devices are everywhere sensing, collecting, storing, and computing massive amounts of data. In the Internet of Things scenario, diversified services will generate traffic with different characteristics and put forward different business requirements. The application based on network intelligent awareness plays a key role in effectively managing network and deepening the control of network. In this chapter, we propose an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Then, we propose a hybrid IDS architecture and introduce a machine learning aided detection method. In addition, we model the time-series network traffic by the recurrent neural network (RNN). The attention mechanism is introduced for assisting network traffic classification in the form of the following two models: the attention aids long short term memory (LSTM) and the hierarchical attention network (HAN). Finally, we propose to design a machine learning-based in-network Distributed Denial of Service (DDoS) detection framework. Benefit from switch processing performance, the in-network mechanism could achieve high scalability and line speed performance.
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H. Yao, P. Gao, J. Wang, P. Zhang, C. Jiang, Z. Han, Capsule network assisted IoT traffic classification mechanism for smart cities. IEEE Int. Things J. 6(5), 7515–7525 (2019)
H. Yao, P. Gao, P. Zhang, J. Wang, C. Jiang, L. Lu, Hybrid intrusion detection system for edge-based iiot relying on machine-learning-aided detection. IEEE Netw. 33(5), 75–81 (2019)
H. Yao, C. Liu, P. Zhang, S. Wu, C. Jiang, S. Yu, Identification of encrypted traffic through attention mechanism based long short term memory. IEEE Trans. Big Data 8, 241–252 (2019)
W. He, Y. Liu, H. Yao, T. Mai, N. Zhang, F.R. Yu, Distributed variational bayes-based in-network security for the Internet of Things. IEEE Int. Things J. 8(8), 6293–6304 (2020)
Y. Kawamoto, N. Yamada, H. Nishiyama, N. Kato, Y. Shimizu, Y. Zheng, A feedback control based crowd dynamics management in IoT system. IEEE Int. Things J. 4, 1466–1476 (2017)
Y. Kawamoto, H. Nishiyama, N. Kato, Y. Shimizu, A. Takahara, T. Jiang, Effectively collecting data for the location-based authentication in internet of things. IEEE Syst. J. 11, 1403–1411 (2017)
S. Verma, Y. Kawamoto, Z.M. Fadlullah, H. Nishiyama, N. Kato, A survey on network methodologies for real-time analytics of massive IoT data and open research issues. IEEE Commun. Surv. Tutor. 19, 1457–1477 (2017)
T. Wang, J. Tan, W. Ding, Y. Zhang, F. Yang, J. Song, Z. Han, Inter-community detection scheme for social Internet of Things: a compressive sensing over graphs approach. IEEE Int. Things J. 5, 1–1 (2018)
J. Ni, K. Zhang, X. Lin, X.S. Shen, Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun. Surv. Tutor. 20, 601–628 (2018)
C.L. Hsu, C.C. Lin, An empirical examination of consumer adoption of internet of things services: network externalities and concern for information privacy perspectives. Comput. Human Behav. 62, 516–527 (2016)
D. Ventura, D. Casado-Mansilla, J. López-de Armentia, P. Garaizar, D. López-de Ipina, V. Catania, ARIIMA: a real IoT implementation of a machine-learning architecture for reducing energy consumption, in International Conference on Ubiquitous Computing and Ambient Intelligence, (Belfast, UK) (2014), pp. 444–451
T. Yonezawa, L. Gurgen, D. Pavia, M. Grella, H. Maeomichi, ClouT: leveraging cloud computing techniques for improving management of massive IoT data, in IEEE International Conference on Service-Oriented Computing and Applications, (Washington, DC) (2014), pp. 324–327
Y. Ma, J. Rao, W. Hu, X. Meng, X. Han, Y. Zhang, Y. Chai, C. Liu, An efficient index for massive IoT data in cloud environment, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management (2012) pp. 2129–2133
Z. Ding, J. Xu, Q. Yang, SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data. J. Supercomput. 66, 1260–1284 (2013)
J. Camhi, Former Cisco CEO John Chambers predicts 500 billion connected devices by 2025. Business Insider (2015)
J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29, 1645–1660 (2013)
S. Chen, H. Xu, D. Liu, B. Hu, A vision of IoT: applications, challenges, and opportunities with China perspective. IEEE Int. Things J. 1, 349–359 (2014)
D. Singh, G. Tripathi, A.J. Jara, A survey of internet-of-things: Future vision, architecture, challenges and services,” in IEEE World Forum on Internet of Things (WF-IoT), (Seoul, South Korea) (2014), pp. 287–292
J. Zheng, D. Simplot-Ryl, C. Bisdikian, H.T. Mouftah, The internet of things [guest editorial]. IEEE Commun. Mag. 49, 30–31 (2011)
Y. Li, Q. Zhang, R. Gao, X. Xin, H. Yao, F. Tian, M. Guizani, An elastic resource allocation algorithm based on dispersion degree for hybrid requests in satellite optical networks. IEEE Int. Things J. 9(9), 6536–6549 (2021)
W. Wang, M. Zhu, X. Zeng, X. Ye, Y. Sheng, Malware traffic classification using convolutional neural network for representation learning, in IEEE International Conference on Information Networking, (Da Nang, Vietnam) (2017), pp. 712–717
S. Sabour, N. Frosst, G.E. Hinton, Dynamic routing between capsules, in Advances in Neural Information Processing Systems (2017), pp. 3856–3866
J. Gong, X. Qiu, S. Wang, X. Huang, Information aggregation via dynamic routing for sequence encoding (2018). arXiv:1806.01501
Q. Liang, X. Wang, X. Tian, F. Wu, Q. Zhang, Two-dimensional route switching in cognitive radio networks: a game-theoretical framework. IEEE/ACM Trans. Netw. 23, 1053–1066 (2015)
S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
X. Tian, Y. Cheng, B. Liu, Design of a scalable multicast scheme with an application-network cross-layer approach. IEEE Trans. Multimedia 11, 1160–1169 (2009)
J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, et al., Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)
Y. Lecun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436 (2015)
T. Mai, H. Yao, J. Xu, N. Zhang, Q. Liu, S. Guo, Automatic double-auction mechanism for federated learning service market in Internet of Things. IEEE Trans. Netw. Sci. Eng. 9, 3123–3135 (2022)
F. Wang, H. Yao, Q. Zhang, J. Wang, R. Gao, D. Guo, M. Guizani, Dynamic distributed multi-path aided load balancing for optical data center networks. IEEE Trans. Netw. Ser. Manag. 19, 991–1005 (2021)
S. Mukherjee, N. Sharma, Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technol. 4, 119–128 (2012)
B. Subba, S. Biswas, S. Karmakar, Intrusion detection systems using linear discriminant analysis and logistic regression, in Annual IEEE India Conference (INDICON), (New Delhi, India) (2015), pp. 1–6
J. Zhang, M. Zulkernine, A hybrid network intrusion detection technique using random forests, in The First International Conference on Availability, Reliability and Security, vol. 2006, (Vienna, Austria) (2006), pp. 262–269
N. McLaughlin, J. Martinez del Rincon, B. Kang, S. Yerima, P. Miller, S. Sezer, Y. Safaei, E. Trickel, Z. Zhao, A. Doupe, et al., Deep android malware detection, in The Seventh ACM on Conference on Data and Application Security and Privacy, (Tempe, AZ) (2017), pp. 301–308
W. Wang, Y. Sheng, J. Wang, X. Zeng, X. Ye, Y. Huang, M. Zhu, HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6, 1792–1806 (2018)
A. Chawla, B. Lee, S. Fallon, P. Jacob, Host based intrusion detection system with combined CNN/RNN model, in Proceedings of Second International Workshop on AI in Security (2019), pp. 149–158
L. Xiao, X. Wan, C. Dai, X. Du, X. Chen, M. Guizani, Security in mobile edge caching with reinforcement learning. IEEE Wirel. Commun. 25, 116–122 (2018)
M. Min, D. Xu, L. Xiao, Y. Tang, D. Wu, Learning-based computation offloading for IoT devices with energy harvesting. IEEE Trans. Vehic. Technol. 68, 1930–1941 (2019)
D. Oh, D. Kim, W.W. Ro, A malicious pattern detection engine for embedded security systems in the internet of things. Sensors 14, 24188–24211 (2014)
L. Wallgren, S. Raza, T. Voigt, Routing attacks and countermeasures in the RPL-based internet of things. Int. J. Distrib. Sensor Netw. 9, 794326 (2013)
A. Le, J. Loo, K.K. Chai, M. Aiash, A specification-based IDS for detecting attacks on RPL-based network topology. Information 7, 25 (2016)
K. Wang, M. Du, D. Yang, C. Zhu, J. Shen, Y. Zhang, Game-theory-based active defense for intrusion detection in cyber-physical embedded systems. ACM Trans. Embed. Comput. Syst. 16, 18:1–18:21 (2016)
G. Giambene, S. Kota, P. Pillai, Satellite-5g integration: a network perspective. IEEE Netw. 32, 25–31 (2018)
K. Wang, M. Du, Y. Sun, A. Vinel, Y. Zhang, Attack detection and distributed forensics in machine-to-machine networks. IEEE Netw. 30, 49–55 (2016)
K. Wang, Y. Wang, Y. Sun, S. Guo, J. Wu, Green industrial Internet of Things architecture: an energy-efficient perspective. IEEE Commun. Mag. 54, 48–54 (2016)
Z. Qin, H. Yao, T. Mai, D. Wu, N. Zhang, S. Guo, Multi-agent reinforcement learning aided computation offloading in aerial computing for the internet-of-things. IEEE Trans. Serv. Comput. (01), 1–12 (2022)
K. Gai, M. Qiu, H. Zhao, Privacy-preserving data encryption strategy for big data in mobile cloud computing. IEEE Transactions on Big Data 7(4), 678–688 (2017)
Z. Cao, G. Xiong, Y. Zhao, Z. Li, L. Guo, A survey on encrypted traffic classification, in International Conference on Applications and Techniques in Information Security, (Berlin, Heidelberg) (2014), pp. 73–81
R. Alshammari, A.N. Zincir-Heywood, Investigating two different approaches for encrypted traffic classification, in 2008 Sixth Annual Conference on Privacy, Security and Trust (2008), pp. 156–166
R. Alshammari, A.N. Zincir-Heywood, Machine learning based encrypted traffic classification: Identifying SSH and Skype, in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (2009), pp. 1–8
M. Dusi, A. Este, F. Gringoli, L. Salgarelli, Using GMM and SVM-based techniques for the classification of SSH-encrypted traffic, in 2009 IEEE International Conference on Communications (2009), pp. 1–6
Z. Wang, The applications of deep learning on traffic identification. BlackHat, USA (2015)
M. Lotfollahi, R.S.H. Zade, M.J. Siavoshani, M. Saberian, Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft. Comput. 24(3), 1999–2012 (2020)
W. Wang, M. Zhu, J. Wang, X. Zeng, Z. Yang, End-to-end encrypted traffic classification with one-dimensional convolution neural networks, in 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) (2017), pp. 43–48
M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, J. Lloret, Network traffic classifier with convolutional and recurrent neural networks for Internet of Things. IEEE Access 5, 18042–18050 (2017)
W. Wang, Y. Sheng, J. Wang, X. Zeng, X. Ye, Y. Huang, M. Zhu, Hast-ids: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6, 1792–1806 (2018)
A.H. Lashkari, G. Draper-Gil, M.S.I. Mamun, A.A. Ghorbani, Characterization of encrypted and VPN traffic using time-related features, in Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP), (Rome, Italy) (2016), pp. 407–414
G. Aceto, D. Ciuonzo, A. Montieri, A. Pescapè, Mobile encrypted traffic classification using deep learning, in 2018 Network Traffic Measurement and Analysis Conference (TMA) (2018), pp. 1–8
R. Li, X. Xiao, S. Ni, H. Zheng, S. Xia, Byte segment neural network for network traffic classification, in 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS) (2018), pp. 1–10
A. Graves, Long Short-Term Memory (Springer, Berlin, 2012), pp. 37–45
D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate (2014). arXiv preprint arXiv:1409.0473
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, E. Hovy, Hierarchical attention networks for document classification, in Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (San Diego, CA) (2016), pp. 1480–1489
Y.-A. Chung, H.-T. Lin, and S.-W. Yang, Cost-aware pretraining for multiclass cost-sensitive deep learning (2015). arXiv preprint arXiv:1511.09337
H. Yao, T. Mai, J. Wang, Z. Ji, C. Jiang, Y. Qian, Resource trading in blockchain-based industrial Internet of Things. IEEE Trans. Ind. Inf. 15(6), 3602–3609 (2019)
C. Qiu, H. Yao, C. Jiang, S. Guo, F. Xu, Cloud computing assisted blockchain-enabled Internet of Things. IEEE Trans. Cloud Comput. 10, 247–257 (2019)
R. Mahmoud, T. Yousuf, F. Aloul, and I. Zualkernan, Internet of things (IoT) security: Current status, challenges and prospective measures, in 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST) (IEEE, Piscataway, 2015), pp. 336–341
H. Yao, H. Liu, P. Zhang, S. Wu, C. Jiang, S. Guo, A learning-based approach to intra-domain qos routing. IEEE Trans. Veh. Technol. 69(6), 6718–6730 (2020)
C. Qiu, H. Yao, F.R. Yu, F. Xu, C. Zhao, Deep q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks. IEEE Trans. Vehic. Technol. 68(6), 5871–5883 (2019)
Q. Yan, F.R. Yu, Q. Gong, J. Li, Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: a survey, some research issues, and challenges. IEEE Commun. Surv. Tutor. 18(1), 602–622 (2016)
F. Li, X. Xu, H. Yao, J. Wang, C. Jiang, S. Guo, Multi-controller resource management for software-defined wireless networks. IEEE Commun. Lett. 23(3), 506–509 (2019)
F. Li, H. Yao, J. Du, C. Jiang, Y. Qian, Stackelberg game-based computation offloading in social and cognitive industrial Internet of Things. IEEE Trans. Ind. Inform. 16(8), 5444–5455 (2019)
C. Qiu, F.R. Yu, H. Yao, C. Jiang, F. Xu, C. Zhao, Blockchain-based software-defined industrial Internet of Things: a dueling deep Q-learning approach. IEEE Int. Things J. 6(3), 4627–4639 (2018)
H. Yao, T. Mai, X. Xu, P. Zhang, M. Li, Y. Liu, NetworkAI: An intelligent network architecture for self-learning control strategies in software defined networks. IEEE Int. Things J. 5(6), 4319–4327 (2018)
H. Yao, S. Ma, J. Wang, P. Zhang, C. Jiang, S. Guo, A continuous-decision virtual network embedding scheme relying on reinforcement learning. IEEE Trans. Netw. Service Manag. 17, 864–875 (2020)
R. Bifulco, G. Rétvári, A survey on the programmable data plane: abstractions, architectures, and open problems, in 2018 IEEE 19th International Conference on High Performance Switching and Routing (HPSR) (IEEE, Piscataway, 2018), pp. 1–7
R. Harrison, Q. Cai, A. Gupta, J. Rexford, Network-wide heavy hitter detection with commodity switches, in Proceedings of the Symposium on SDN Research (2018), pp. 1–7
J. Liang, R. Liu, Stacked denoising autoencoder and dropout together to prevent overfitting in deep neural network, in 2015 8th International Congress on Image and Signal Processing (CISP) (IEEE, Piscataway, 2015), pp. 697–701
G. Karatas, O. Demir, O.K. Sahingoz, Deep learning in intrusion detection systems, in 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) (IEEE, Piscataway, 2018), pp. 113–116
Y. Mirsky, T. Doitshman, Y. Elovici, A. Shabtai, Kitsune: an ensemble of autoencoders for online network intrusion detection (2018). Preprint arXiv:1802.09089
F.A. Khan, A. Gumaei, A. Derhab, A. Hussain, A novel two-stage deep learning model for efficient network intrusion detection. IEEE Access 7, 30373–30385 (2019)
N. Shone, T.N. Ngoc, V.D. Phai, Q. Shi, A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Topics Comput. Intell. 2(1), 41–50 (2018)
D. Li, L. Deng, M. Lee, H. Wang, IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. Int. J. Inf. Manag. 49, 533–545 (2019)
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Yao, H., Guizani, M. (2023). Intelligent IoT Network Awareness. In: Intelligent Internet of Things Networks . Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-26987-5_3
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