Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Geethapriya Thamilarasu; Shiven Chawla

doi:10.3390/s19091977

journal article Open Access Apr 27, 2019

Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Geethapriya Thamilarasu Shiven Chawla

Sensors Vol. 19 No. 9 pp. 1977 · MDPI AG

View at Publisher Save 10.3390/s19091977

Abstract

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Topics

No keywords indexed for this article. Browse by subject →

References

34

[1]

Internet of Things (IoT): A vision, architectural elements, and future directions

Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic et al.

Future Generation Computer Systems 2013 10.1016/j.future.2013.01.010

[2]

Miorandi "Internet of Things: Vision, applications and research challenges" Ad Hoc Netw. (2012) 10.1016/j.adhoc.2012.02.016

[3]

Smith, S. (2015). IoT Connected Devices to Triple to Over 38Bn Units, Juniper Research.

[4]

Roman "On the features and challenges of security and privacy in distributed Internet of Things" Comput. Netw. (2013) 10.1016/j.comnet.2012.12.018

[5]

Khan, A. (2016, January 10). Overview of Security in Internet of Things. Proceedings of the 3rd International Conference on Recent Trends in Engineering Science and Management, Bundi, Rajasthan, India.

[6]

Abomhara "Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks" J. Cyber Secur. Mobil. (2015) 10.13052/jcsm2245-1439.414

[7]

Shieh, S.W. (2015, January 14–17). Emerging Security Threats and Countermeasures in IoT. Proceedings of the ACM Asia Conference on Computer and Communications Security, Singapore.

[8]

Williams, R., McMahon, E., Samtani, S., Patton, M., and Chen, H. (2017, January 22–24). Identifying vulnerabilities of consumer Internet of Things (IoT) devices: A scalable approach. Proceedings of the 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), Beijing, China. 10.1109/isi.2017.8004904

[9]

Hilton, S. (Dyn Blog, 2016). Dyn Analysis Summary Of Friday October 21 Attack, Dyn Blog.

[10]

Solon, O. (The Guardian, 2016). Team of hackers take remote control of Tesla Model S from 12 miles away, The Guardian.

[11]

Pycroft "Security of implantable medical devices with wireless connections: The dangers of cyber-attacks" Expert Rev. Med. Devices (2018) 10.1080/17434440.2018.1483235

[12]

Kasinathan, P., Costamagna, G., Khaleel, H., Pastrone, C., and Spirito, M.A. (2013, January 4–8). DEMO: An IDS Framework for Internet of Things Empowered by 6LoWPAN. Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, Berlin, Germany. 10.1145/2508859.2512494

[13]

Krimmling, J., and Peter, S. (2014, January 29–31). Integration and Evaluation of Intrusion Detection for CoAP in smart city applications. Proceedings of the IEEE Conference on Communications and Network Security (CNS), San Francisco, CA, USA. 10.1109/cns.2014.6997468

[14]

Le "6LoWPAN: A study on QoS security threats and countermeasures using intrusion detection system approach" Int. J. Commun. Syst. (2012) 10.1002/dac.2356

[15]

Chawla, S., and Thamilarasu, G. (2018, January 9–11). Security As a Service: Real-time Intrusion Detection in Internet of Things. Proceedings of the Fifth Cybersecurity Symposium, CyberSec ’18, Coeur d’ Alene, ID, USA. 10.1145/3212687.3212872

[16]

Khan, R., Khan, S.U., Zaheer, R., and Khan, S. (2012, January 17–19). Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges. Proceedings of the 10th International Conference on Frontiers of Information Technology, Islamabad, India. 10.1109/fit.2012.53

[17]

Xu, T., Wendt, J.B., and Potkonjak, M. (2014, January 3–6). Security of IoT Systems: Design Challenges and Opportunities. Proceedings of the International Conference on Computer-Aided Design, San Jose, CA, USA. 10.1109/iccad.2014.7001385

[18]

Heer "Security Challenges in the IP-based Internet of Things" Wirel. Person. Commun. (2011) 10.1007/s11277-011-0385-5

[19]

A roadmap for security challenges in the Internet of Things

Arbia Riahi Sfar, Enrico Natalizio, Yacine Challal et al.

Digital Communications and Networks 2018 10.1016/j.dcan.2017.04.003

[20]

Zhou, W., Jia, Y., Peng, A., Zhang, Y., and Liu, P. (2018). The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved. IEEE Internet Things J. 10.1109/jiot.2018.2847733

[21]

Zhao, K., and Ge, L. (2013, January 14–15). A Survey on the Internet of Things Security. Proceedings of the 2013 9th International Conference on Computational Intelligence and Security (CIS), Leshan, Sichuan, China. 10.1109/cis.2013.145

[22]

Kasinathan, P., Pastrone, C., Spirito, M.A., and Vinkovits, M. (2013, January 7–9). Denial-of-Service detection in 6LoWPAN based Internet of Things. Proceedings of the 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Lyon, France. 10.1109/wimob.2013.6673419

[23]

Danda, J.M.R., and Hota, C. (2016). Attack Identification Framework for IoT Devices. Information Systems Design and Intelligent Applications, Springer India. 10.1007/978-81-322-2752-6_49

[24]

Le, A., Loo, J., Chai, K.K., and Aiash, M. (2016). A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology. Information, 7. 10.3390/info7020025

[25]

Surendar, M., and Umamakeswari, A. (2016, January 23–25). InDReS: An Intrusion Detection and response system for Internet of Things with 6LoWPAN. Proceedings of the 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India. 10.1109/wispnet.2016.7566473

[26]

Bostani "Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach" Comput. Commun. (2017) 10.1016/j.comcom.2016.12.001

[27]

Fu "An Automata Based Intrusion Detection Method for Internet of Things" Mobile Inf. Syst. (2017)

[28]

Raza "SVELTE: Real-time Intrusion Detection in the Internet of Things" Ad Hoc Netw. (2013) 10.1016/j.adhoc.2013.04.014

[29]

Liu, C., Yang, J., Chen, R., Zhang, Y., and Zeng, J. (2011, January 26–28). Research on immunity-based intrusion detection technology for the Internet of Things. Proceedings of the 2011 Seventh International Conference on Natural Computation, Shanghai, China. 10.1109/icnc.2011.6022060

[30]

Arrington, B., Barnett, L., Rufus, R., and Esterline, A. (2016, January 1–4). Behavioral Modeling Intrusion Detection System (BMIDS) Using Internet of Things (IoT) Behavior-Based Anomaly Detection via Immunity-Inspired Algorithms. Proceedings of the 2016 25th International Conference on Computer Communication and Networks (ICCCN), Waikoloa, HI, USA. 10.1109/icccn.2016.7568495

[31]

Liu "An intrusion detection method for internet of things based on suppressed fuzzy clustering" EURASIP J. Wirel. Commun. Netw. (2018) 10.1186/s13638-018-1128-z

[32]

Nielsen, M.A. (2015). Neural Networks and Deep Learning, Determination Press. Available online: http://neuralnetworksanddeeplearning.com/.

[33]

Thomson, C., Romdhani, I., Al-Dubai, A., Qasem, M., Ghaleb, B., and Wadhaj, I. (2016). Cooja Simulator Manual, Edinburgh Napier University.

[34]

Alghuried, A. (2017). A Model for Anomalies Detection in Internet of Things (IoT) Using Inverse Weight Clustering and Decision Tree. [Masters’s Thesis, Dublin Institute of Technology].

Metrics

259

Citations

34

References

Details

Published: Apr 27, 2019
Vol/Issue: 19(9)
Pages: 1977
License: View

Authors

G

Geethapriya Thamilarasu

School of STEM, University of Washington Bothell, Bothell, WA 98011, USA

S

Shiven Chawla

School of STEM, University of Washington Bothell, Bothell, WA 98011, USA

Cite This Article

Geethapriya Thamilarasu, Shiven Chawla (2019). Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things. Sensors, 19(9), 1977. https://doi.org/10.3390/s19091977

Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

You May Also Like