Abstract
Cyber threats are a showstopper for Internet of Things (IoT) has recently been used at an industrial scale. Network layer attacks on IoT can cause significant disruptions and loss of information. Among such attacks, routing attacks are especially hard to defend against because of the ad-hoc nature of IoT systems and resource constraints of IoT devices. Hence, an efficient approach for detecting and predicting IoT attacks is needed. Systems confidentiality, integrity and availability depends on continuous security and robustness against routing attacks. We propose a deep-learning based machine learning method for detection of routing attacks for IoT. In our study, the Cooja IoT simulator has been utilized for generation of high-fidelity attack data, within IoT networks ranging from 10 to 1000 nodes. We propose a highly scalable, deep-learning based attack detection methodology for detection of IoT routing attacks which are decreased rank, hello-flood and version number modification attacks, with high accuracy and precision. Application of deep learning for cyber-security in IoT requires the availability of substantial IoT attack data and we believe that the IoT attack dataset produced in this work can be utilized for further research.
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D. Janakiram, V.A. Reddy, and A. V. U. P. Kumar. Outlier detection in wireless sensor networks using Bayesian belief networks. In Communication System Software and Middleware, 2006. Comsware 2006. First International Conference on, pages 1–6. IEEE, 2006.
Y. Zhang, N. Meratnia, and P. J. M. Havinga. Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. Ad hoc networks, 11(3):1062–1074, 2013.
S. Kaplantzis, A. Shilton, N. Mani, and Y. A. Sekercioglu. Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on, pages 335–340. IEEE, 2007.
S. Rajasegarar, C. Leckie, M. Palaniswami, and J. C. Bezdek. Quarter sphere based distributed anomaly detection in wireless sensor networks. In Communications, 2007. ICC’07. IEEE International Conference on, pages 3864–3869. IEEE, 2007.
Z. Yang, N. Meratnia, and P. Havinga. An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine. In Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. International Conference on, pages 151–156. IEEE, 2008.
A. L. Buczak and E. Guven. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2):1153–1176, 2016.
T. Hurley, J. E. Perdomo, and A. Perez-Pons. HMM-based intrusion detection system for software defined networking. In Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on, pages 617–621. IEEE, 2016.
C-F. Tsai, Y-F. Hsu, C-Y. Lin, and W-Y. Lin. Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10):11994–12000, 2009.
M. D. N. Chowdhury, K. Ferens, and M. Ferens. Network intrusion detection using machine learning. In Proceedings of the International Conference on Security and Management (SAM), page 30. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016.
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: a survey. Computer networks, 38(4):393–422, 2002.
L. Wallgren, S. Raza, and T. Voigt. Routing attacks and countermeasures in the RPL-based internet of things. International Journal of Distributed Sensor Networks, 9(8):794326, 2013.
P. Kasinathan, G. Costamagna, H. Khaleel, C. Pastrone, and M. A. Spirito. An IDS framework for internet of things empowered by 6LoWPAN. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security, pages 1337–1340. ACM, 2013.
P. Pongle and G. Chavan. Real time intrusion and wormhole attack detection in internet of things. International Journal of Computer Applications, 121(9), 2015.
F. Nait-Abdesselam, B. Bensaou, and T. Taleb. Detecting and avoiding wormhole attacks in wireless ad hoc networks. IEEE Communications Magazine, 46(4):127–133, 2008.
Z. Banković, D. Fraga, J. M. Moya, and J. C. Vallejo. Detecting unknown attacks in wireless sensor networks that contain mobile nodes. Sensors, 12(8):10834–10850, 2012.
F. Osterlind, A. Dunkels, J. Eriksson, N. Finne, and T. Voigt. Cross-level sensor network simulation with cooja. In Local computer networks, proceedings 2006 31st IEEE conference on, pages 641–648. IEEE, 2006.
S. Raza, L. Wallgren, and T. Voigt. SVELTE: Realtime intrusion detection in the internet of things. Ad hoc networks, 11(8):2661–2674, 2013.
T. Avram, S. Oh, and S. Hariri. Analyzing attacks in wireless ad hoc network with self-organizing maps. In Communication Networks and Services Research, 2007. CNSR’07. Fifth Annual Conference on, pages 166–175. IEEE, 2007.
T. Clausen and P. Jacquet. Optimized link state routing protocol (OLSR). Technical report, 2003.
T. Issariyakul and E. Hossain. Introduction to network simulator NS2. Springer Science & Business Media, 2011.
U. S. R. K. Dhamodharan and R. Vayanaperumal. Detecting and preventing sybil attacks in wireless sensor networks using message authentication and passing method. The Scientific World Journal, 2015:7, 2015.
A. A. Diro and N. Chilamkurti. Distributed attack detection scheme using deep learning approach for internet of things. Future Generation Computer Systems, 2017.
A. Alrawais, A. Alhothaily, C. Hu, and X. Cheng. Fog computing for the internet of things: Security and privacy issues. IEEE Internet Computing, 21(2):34–42, 2017.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani. A detailed analysis of the KDD CUP 99 data set. In Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium on, pages 1–6. IEEE, 2009.
Jack V. Tu. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11):1225–1231, 1996.
David H Wolpert. The lack of a priori distinctions between learning algorithms. Neural computation, 8(7):1341–1390, 1996.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
S. Walt, S. C. Colbert, and G. Varoquaux. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2):22–30, 2011.
A. Dunkels, B. Gronvall, and T. Voigt. Contiki-a lightweight and flexible operating system for tiny networked sensors. In Local Computer Networks, 2004. 29th Annual IEEE International Conference on, pages 455–462. IEEE, 2004.
T. Winter. RPL: IPv6 routing protocol for low-power and lossy networks. RFC 6550, RFC Editor, October 2012.
A. Al-Fuqaha, M. Guizani, M. Mohammadi, and M. Aledhari, M.and Ayyash. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4):2347–2376, 2015.
A. Aris, S. F Oktug, and S. B. O. Yalcin. RPL version number attacks: In-depth study. In Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP, pages 776–779. IEEE, 2016.
W. McKinney. Data structures for statistical computing in Python. In Proceedings of the 9th Python in Science Conference, volume 445, pages 51–56. SciPy Austin, TX, 2010.
I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3(Mar):1157–1182, 2003.
L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001.
N. Moustafa, J. Slay, and G. Creech. Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Transactions on Big Data, PP(99):1–1, 2017.
E. Alpaydin. Introduction to machine learning. MIT press, 2014.
Rajat Raina, Anand Madhavan, and Andrew Y Ng. Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th annual international conference on machine learning, pages 873–880. ACM, 2009.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436, 2015.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533, 1986.
Miguel Moreira and Emile Fiesler. Neural networks with adaptive learning rate and momentum terms. Technical report, Idiap, 1995.
M. D Zeiler. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.
D. Hunter, H. Yu, M. S. Pukish III, J. Kolbusz, and B. M. Wilamowski. Selection of proper neural network sizes and architectures-a comparative study. IEEE Transactions on Industrial Informatics, 8(2):228–240, May 2012.
Yuchun Tang, Yan-Qing Zhang, Nitesh V Chawla, and Sven Krasser. SVMs modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(1):281–288, 2009.
Patrick Glauner, Jorge Augusto Meira, Petko Valtchev, Radu State, and Franck Bettinger. The challenge of non-technical loss detection using artificial intelligence: A survey. International Journal of Computational Intelligence Systems, 10(1):760, 2017.
Patrick Glauner, Andre Boechat, Lautaro Dolberg, Radu State, Franck Bettinger, Yves Rangoni, and Diogo Duarte. Large-scale detection of non-technical losses in imbalanced data sets. In Innovative Smart Grid Technologies Conference (ISGT), 2016 IEEE Power & Energy Society, pages 1–5. IEEE, 2016.
N. Moustafa and J. Slay. UNSW-NB15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In Military Communications and Information Systems Conference (MilCIS), 2015, pages 1–6. IEEE, 2015.
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Yavuz, F.Y., Ünal, D. & Gül, E. Deep Learning for Detection of Routing Attacks in the Internet of Things. Int J Comput Intell Syst 12, 39–58 (2018). https://doi.org/10.2991/ijcis.2018.25905181
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DOI: https://doi.org/10.2991/ijcis.2018.25905181