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IJSTR >> Volume 9 - Issue 4, April 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Comprehensive Analysis On Intrusion Detection In Iot Based Smart Environments Using Machine Learning Approaches

[Full Text]

 

AUTHOR(S)

Umar Albalawi

 

KEYWORDS

Cyber-attacks, Internet of Things, Intrusion, Anomaly, Machine Learning, Security attacks, Smart Environment

 

ABSTRACT

With the expansion in the quantity of Internet-connected devices, security and privacy concerns were the important obstructions hindering the extensive adoption of the IoT. Security in IoT has become a major consideration for all, including the organizations, consumers, and the government. While attacks on any system can't be completely secured perpetually, real-time detection of the attacks are significant to protect the systems in a compelling way. Privacy and security are the major important concerns in the domain of real-time communication and predominantly on IoTs. With the development of IoT, the security of network layer has drawn better focus. The vulnerabilities of security in the IoT can create security threats dependent on any application. In this manner there is a basic prerequisite for security development and enhancement for the IoT system for preventing security attacks dependent on vulnerabilities of security. In this study we reviewed IoT system, security attacks, security requirements and its applications based on Machine learning approaches. The goal of this review was to analyze the Machine learning techniques that could be utilized to develop and enhance the security techniques for IoT systems.

 

REFERENCES

[1] K. K. Patel and S. M. Patel, “Internet of Things-IOT: Definition, Characteristics, Architecture, Enabling Technologies, Application & Future Challenges”, International Journal of Engineering Science and Computing, Vol. 6, Issue No. 5, pp.6122-6131, 2016.
[2] M. Ammar, G. Russello, and B. Crispo, “Internet of Things: A survey on the security of IoT frameworks”, Journal of Information Security and Applications, Elsevier, Vol.38, pp.8-27, 2018.
[3] I. Alqassem and D. Svetinovic, “A Taxonomy of Security and Privacy Requirements for the Internet of Things (IoT)”, Proceedings of the 2014 IEEE IEEM, pp.1244-1248, 2014.
[4] K. Chen, S. Zhang, Z. Li, Y. Zhang, Q. Deng, S. Ray, and Y. Jin, “Internet-of-Things Security and Vulnerabilities: Taxonomy, Challenges, and Practice”, Journal of Hardware and Systems Security, Vol.2, pp.97–110, 2018.
[5] J. K. Amfo and J. B. Hayfron-Acquah, “Modeling of Hybrid Intrusion Detection System in Internet of Things using Support Vector Machine and Decision Tree”, International Journal of Computer Applications, Volume 181 – No. 15, pp.45-52, 2018.
[6] S. Geetha and A. V. Phamila, “Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems”, Network Intrusion Detection and Prevention Systems for Attacks in IoT Systems, Chapter-6, IGI Global, pp.128-141, 2019.
[7] H. Jayakumar, K. Lee, W. S. Lee, A. Raha, Y. Kim, and V. Raghunathan, “Powering the Internet of Things”, ACM Transactions, pp.375-380, 2014.
[8] E. Leloglu, “A Review of Security Concerns in Internet of Things”, Journal of Computer and Communications, Vol.5, pp.121-136, 2017.
[9] B. B. Zarpelão, R. S. Miani, C. T. Kawakani, and S. C. de Alvarenga, “A survey of intrusion detection in Internet of Things”, Journal of Network and Computer Applications, Elsevier, pp.1-13, 2017.
[10] M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, and M. Guizani, “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security”, arXiv.org, pp.1-42, 2018.
[11] M. Hasan, Md. M. Islam, Md I. I. Zarif, and M.M.A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches”, Internet of Things, Elsevier, Vol.7, pp.1-14, 2019.
[12] S. Jaiswal and D. Gupta, “Security Requirements for Internet of Things (IoT)”, Proceedings of International Conference on Communication and Networks, Advances in Intelligent Systems and Computing, Springer, pp.419-427, 2017.
[13] M. S. Alam and S. T. Vuong, “Random Forest Classification for Detecting Android Malware”, IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, IEEE, pp.663-6692013.
[14] A. Azmoodeh, A. Dehghantanha, and K. R. Choo, “Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning”, IEEE Transactions on Sustainable Computing, vol.4, no.1, pp.88-95, 2019.
[15] J. Canedo and A. Skjellum, “Using Machine Learning to Secure IoT Systems”, Annual Conference on Privacy, Security and Trust (PST), IEEE, pp. 219-222, 2016.
[16] S. Rathore and J. H. Park, “Semi-supervised learning based distributed attack detection framework for IoT”, Applied Soft Computing, Elsevier, pp.1-20, 2018.
[17] M. Esmalifalak, L. Liu, N. Nguyen, R. Zheng, and Z. Han, “Detecting Stealthy False Data Injection using Machine Learning in Smart Grid”, IEEE Systems Journal, pp.1-9, 2014.
[18] H. H. Pajouh, R. Javidan, R. Khaymi, A. Dehghantanha and K. R. Choo, “A Two-layer Dimension Reduction and Two-tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks”, IEEE, pp.1-11, 2016.
[19] H. H. Pajouh, A. Dehghantanha, R. Khayami, and K. R. Choo, “A deep Recurrent Neural Network based approach for internet of things malware threat hunting”, Future Generation Computer Systems, Elsevier,2018, https://doi.org/10.1016/j.future.2018.03.007
[20] H. S. Ham, H. H. Kim, M.S. Kim, and M. J. Choi, “Linear SVM-Based Android Malware Detection for Reliable IoT Services”, Journal of Applied Mathematics, Hindawi, pp.1-10, 2014.
[21] F. Hussain, A. Anpalagan, A. S. Khwaja, and M. Naeem, “Resource allocation and congestion control in clustered M2M communication using Q-learning”, Transactions on Emerging Telecommunications Technologies, Wiley Online Library, pp.1-12, 2016,.
[22] E. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb, “MalDozer: Automatic framework for android malware detection using deep learning”, Digital Investigation, Elsevier, pp.48-59, 2018.
[23] Y. Li, D. E. Quevedo, S. Dey, and L. Shi, “SINR-based DoS Attack on Remote State Estimation: A Game-theoretic Approach”, IEEE, pp.1-10, 2015.
[24] N. An, A. Duff, G. Naik, M. Faloutsos, S. Weber, and S. Mancoridis, “Behavioral Anomaly Detection of Malware on Home Routers, International Conference on Malicious and Unwanted Software (MALWARE)”, IEEE, pp. 47-54, 2017.
[25] N. Nesa, T. Ghosh, and I. Banerjee, “Non-parametric sequence-based learning approach for outlier detection in IoT”,FutureGenerationComputerSystems,Elsevier,2017,https://doi.org/10.1016/j.future.2017.11.021.
[26] M. Ozay, I. Esnaola, F. T. Y. Vural, S. R. Kulkarni, and H. V. Poor, “Machine Learning Methods for Attack Detection in the Smart Grid”, IEEE Transactions on Neural Networks and Learning Systems, pp.1-14, 2015,.
[27] P. Shukla, “ML-IDS: A Machine Learning Approach to Detect Wormhole Attacks in Internet of Things”, Intelligent Systems Conference, IEEE, pp.234-240, 2017.
[28] C. Shi, J. Liu, H. Liu, and Y. Chen, “Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT”, In Proceedings of Mobihoc ’17, ACM, pp.1-10, 2017.
[29] J. Su et al., “Lightweight Classification of IoT Malware Based on Image Recognition”, IEEE International Conference on Computer Software & Applications, IEEE, pp.664-669, 2018.
[30] E. Viegas, A. Santin, L. Oliveira, A. Francüa, R. Jasinski, and V. Pedroni, “A Reliable and Energy-Efficient Classifier Combination Scheme for Intrusion Detection in Embedded Systems”, Computers & Security, Elsevier, pp.1-15, 2018.
[31] L. Xiao, Y. Li, G. Han, G. Liu, and W. Zhuang, “PHY-layer Spoofing Detection with Reinforcement Learning in Wireless Networks”, IEEE Globecom 2015, IEEE, pp.1-11, 2015.
[32] W. Zhou and B. Yu, “A Cloud-Assisted Malware Detection and Suppression Framework for Wireless Multimedia System in IoT Based on Dynamic Differential Game”, Computer System Security, China Communications, IEEE, pp.209-223, 2018.
[33] Saad Almutairi, S. Manimurugan, Majed Aborokbah, “A New Secure Transmission Scheme between Senders and Receiver Using HVCHC without Any Loss”, EURASIP Journal on Wireless Communications and Networking, 2019:88, 2019, https://doi.org/10.1186/s13638-019-1399-z
[34] S.Manimurugan and C.Narmatha., “Secure and Efficient Medical Image Transmission by New Tailored Visual Cryptography Scheme with LS Compressions”, International Journal of Digital Crime and Forensics (IJDCF), Volume 7, Issue 1, Pp 26-50, 2015.