Abstract
Intrusions into the networks of the connected objects are rapidly evolving and affect its entire architecture (physical, network, application layers), as devices, networks and applications are increasingly connected and integrated. Securing these systems, which are generally constrained in resources, is becoming a necessity. Intrusion Detection Systems have proven to be an important security tool to detect attacks on the IoT network and resources. To create the IDS, Security researchers have recently used machine learning techniques because of the excellent results given by these methods (image and voice recognition, product recommendation, detection of spam and financial fraud …). Deep learning methods known for his or her successful ability to extract high-level functionality from big data are often a resilient mechanism for detecting small variants of attacks. The target of this work is to provide a general study on IDSs implementation techniques for IoT, precisely the classical methods and also the machine learning techniques. Finally, we give some recommendations of selected works that have practiced each of the methods presented.
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References
Ahmed M, Naser Mahmood A, Hu J (2016) A survey of network anomaly detection techniques. J Netw Comput Appl
Pacheco J, Hariri S (2016) IoT security framework for smart cyber infrastructures. In: Proceedings—IEEE 1st international workshops on foundations and applications of self-systems, FAS-W 2016
Kasinathan P, Pastrone C, Spirito MA, Vinkovits M (2013) Denial-of-service detection in 6LoWPAN based internet of things. In: International conference wireless mobility computing and network communication, pp 600–607
Scarfone K, Mell P (2007) Guide to intrusion detection and prevention systems (IDPS). Natl Inst Stand Technol
Amaral JP, Oliveira LM, Rodrigues JJPC, Han G, Shu L (2014) Policy and network-based intrusion detection system for IPv6-enabled wireless sensor networks. In: 2014 IEEE international conference on communications, ICC 2014
Bamou A, Khardioui M, El Ouadghiri MD, Aghoutane B (2020) Implementing and evaluating an intrusion detection system for denial of service attacks in IoT environments. In: Lecture notes in networks and systems
Le A, Loo J, Luo Y, Lasebae A (2011) Specification-based IDS for securing RPL from topology attacks. IFIP Wirel Days 1(1):4–6
Raza S, Wallgren L, Voigt T (2013) SVELTE: real-time intrusion detection in the internet of things. Ad Hoc Networks
Thanigaivelan NK, Nigussie E, Kanth RK, Virtanen S, Isoaho J (2016) Distributed internal anomaly detection system for internet-of-things. In: 2016 13th IEEE annual consumer communications and networking conference (CCNC), pp 319–320
Saxena AK, Sinha S, Shukla P (2017) General study of intrusion detection system and survey of agent based intrusion detection system. In: Proceeding—IEEE international conference on computing communication and automation ICCCA 2017, vol 2017, pp. 417–421
Khan ZA, Herrmann P (2017) A trust based distributed intrusion detection mechanism for internet of things. In: Proceedings of the International Conference on Advance Information Networking and Application, AINA, pp 1169–1176
Khardioui M, Bamou A, El Ouadghiri MD, Aghoutane B (2020) Implementation and evaluation of an intrusion detection system for IoT: against routing attacks. Lect Notes Netw Syst. 92:155–166
Ikram W, Petersen S, Orten P, Thornhill NF (2014) Adaptive multi-channel transmission power control for industrial wireless instrumentation. IEEE Trans Ind Inf
Sedjelmaci H, Senouci SM, Al-Bahri M (2016) A lightweight anomaly detection technique for low-resource IoT devices: a game-theoretic methodology. In: 2016 IEEE international conference on communication ICC 2016
Arrington B, Barnett LE, Rufus R, Esterline A (2016) Behavioral modeling intrusion detection system (BMIDS) using internet of things (IoT) behavior-based anomaly detection via immunity-inspired algorithms. In: 2016 25th international conference on computer communications and networks, ICCCN 2016
Wagner C, François J, State R, Engel T (2011) Machine learning approach for IP-flow record anomaly detection. Lecture notes on computer science (including subseries of lecture notes artificial intelligence, lecture notes in bioinformatics), vol 6640 LNCS, no. PART 1, pp 28–39
Ng AY, Jordan MI (2002) On discriminative versus generative classifiers: a comparison of logistic regression and naive bayes. In: Advances in neural information processing systems
Gokhale DV, Box GEP, Tiao GC (1974) Bayesian inference in statistical analysis. Biometrics
Kotsiantis SB (2013) Decision trees: a recent overview. Artif Intell Rev 39(4):261–283
Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. In: IEEE Communication on survey tutorials
Doshi R, Apthorpe N, Feamster N (2018) Machine learning DDoS detection for consumer internet of things devices. In: Proceedings—2018 IEEE symposium on security and privacy workshops, SPW 2018
An N, Duff A, Naik G, Faloutsos M, Weber S, Mancoridis S (2018) Behavioral anomaly detection of malware on home routers. In: Proceedings of the 2017 12th international conference on malicious and unwanted software, MALWARE 2017
Moustafa N, Turnbull B, Choo KKR (2019) An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Internet Things J
Bassey J, Adesina D, Li X, Qian L, Aved A, Kroecker T (2019) Intrusion detection for IoT devices based on RF fingerprinting using deep learning. In: 2019 4th international conference on fog mobile edge computing FMEC 2019, pp 98–104
Vrizlynn LL (2017) Thing, “IEEE 802.11 network anomaly detection and attack classification: a deep learning approach. In: IEEE wireless communications and networking conference (WCNC)
Staudemeyer RC (2015) Applying long short-term memory recurrent neural networks to intrusion detection. South Afr Comput J
Roy B, Cheung H (2019) A deep learning approach for intrusion detection in internet of things using bi-directional long short-term memory recurrent neural network. In: 2018 28th international telecommunication networks and applications conference, ITNAC 2018
Feng Q, Zhang Y, Li C, Dou Z, Wang J (2017) Anomaly detection of spectrum in wireless communication via deep auto-encoders. J Supercomput 73(7):3161–3178
Feng P, Yu M, Naqvi SM, Chambers JA (2014) Deep learning for posture analysis in fall detection. In: International conference digital signal processing DSP, vol 2014, pp 12–17
Chen Y, Zhang Y, Maharjan S, Alam M, Wu T (2019) Deep learning for secure mobile edge computing in cyber-physical transportation systems. IEEE Netw 33(4):36–41
Hiromoto RE, Haney M, Vakanski A (2017) A secure architecture for IoT with supply chain risk management. In: Proceedings of 2017 IEEE 9th international conference on intelligence data acquisitor advance on computing system technology applied. IDAACS 2017, vol 1, pp 431–435
Dimokranitou A, Tsechpenakis G, Yu Zheng J, Tuceryan M (2017) Adversarial autoencoders for anomalous event detection. Master thesis Purde University
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Bamou, A., EL Ouadghiri, M.D., Aghoutane, B. (2022). Current Works on IDS Development Strategies for IoT. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_2
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