ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Comparative Study On Intrusion Detection Systems for Secured Communication in Internet of Things


Affiliations
1 Department of Computer Science, P.K.R. Arts College for Women, India
     

   Subscribe/Renew Journal


The virtual and physical worlds are bridged using the largest digital mega-trend called the Internet of Things (IoT). Between mankind, new interactions and new business models are emerging due to the incremental growth in the Internet, machines, objects, and people connectivity. Secured communication is a typical challenge that is raised due to IoT high diversity, restricted computational resources, and protocols and standards. Because of the huge attack surface in IoT networks, they are highly vulnerable to various attacks, even with some security measures. So, for detecting attacks, it is necessary to design defense mechanisms. In IoT environments, it is highly crucial to have security defense measures like Intrusion Detection Systems (IDS). Hence, authentication and encryption traditional security countermeasures are not sufficient. At network level, to solve those issues and to protect Internet-connected frameworks, major solutions are provided by IDS. Highly unique challenges are faced by IoT specific characteristics like malware detection, ransomware, processor architecture heterogeneity, and the gap in security design. However, as in literature, various problems are raised in traditional IDS, like the high false alarm rate. In IoT, for intrusion detection, a detailed study of traditional Deep Learning (DL) and Machine Learning (ML) techniques and recent technologies is presented in this review. For presenting every selected work objective and methodology, they are analysed and this review work discusses their results. IoT systems cannot be secured by applying traditional security techniques directly due to their computational constraints and intrinsic resources. In real time, on IoT devices, unknown and known attacks are detected using ML techniques in IDS. An IDS is presented in this review and its working is independent of network structure and IoT protocols. This IDS do not require any prior knowledge of security threats. Therefore, for providing security as a service to IoT networks, an artificially intelligent IDS is developed. This review paper provides a clear discussion of various attack detection techniques, along with their benefits and drawbacks.

Keywords

Genetic Algorithms (GA), Deep Learning (DL), Intrusion Detection Systems (IDS), Internet of Things (IoT).
Subscription Login to verify subscription
User
Notifications
Font Size

  • G.J. Joyia, R.M. Liaqat and A. Farooq, “Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain”, Journal of Communication, Vol. 12, No. 4, pp. 240-247, 2017.

  • R. Patan and A.H. Gandomi, “Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks”, Information, Vol. 11, No. 4, pp. 203-216, 2020.

  • S. Kannan, G. Dhiman, and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoT-Based Bat Agents for Traffic Management”, Electronics, Vol. 10, No. 7, pp. 785-796, 2021.

  • V. Chang, B. Gobinathan, A. Pinagapan and S. Kannan, “Automatic Detection of Cyberbullying using multi-feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 1-17, 2021.

  • T. Karthikeyan, K. Praghash and K.H. Reddy, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 48, No. 1, pp. 1-20, 2021.

  • P. Johri, “Improved Energy Efficient Wireless Sensor Networks using Multicast Particle Swarm Optimization”, Proceedings of International Conference on Innovative Advancement in Engineering and Technology, pp. 1-6, 2020.

  • Y. Meidan, M. Bohadana and Y. Mathov, “N-Baiot-Network-based Detection of IoT Botnet Attacks using Deep Autoencoders”, IEEE Pervasive Computing, Vol. 17, No. 3, pp. 12-22, 2018.

  • M. Elhoseny, K. Shankar and S.K. Lakshmanaprabu, “Hybrid Optimization with Cryptography Encryption for Medical Image Security in Internet of Things”, Neural Computing and Applications, Vol. 32, No. 15, pp. 1-15, 2018.

  • A. Al Shorman, H. Faris and I. Aljarah, “Unsupervised Intelligent System based on One Class Support Vector Machine and Grey Wolf Optimization for IoT Botnet Detection”, Journal of Ambient Intelligence and Humanized Computing, Vol. 11, No. 7, pp. 2809-2825, 2020.

  • A. Aldaej, “Enhancing Cyber Security in Modern Internet of Things (IoT) using Intrusion Prevention Algorithm for IoT (IPAI)”, IEEE Access, Vol. 8, pp. 1-9, 2019.

  • H. Sedjelmaci, S.M. Senouci and T. Taleb, “An Accurate Security Game for Low-Resource IoT Devices”, IEEE Transactions on Vehicular Technology, Vol. 66, No. 10, pp. 9381-9393, 2017.

  • M. Zhou, L. Han, H. Lu and C. Fu, “Intrusion Detection System for IoT Heterogeneous Perceptual Network”, Mobile Networks and Applications, Vol. 33, No. 1, pp. 1-14, 2020.

  • S.T. Bakhsh, S. Alghamdi, R.A. Alsemmeari and S.R. Hassan, “An Adaptive Intrusion Detection and Prevention System for Internet of Things”, International Journal of Distributed Sensor Networks, Vol. 15, No. 11, pp. 1-20, 2019.

  • A. Tabassum and W. Lebda, “Security Framework for IoT Devices against Cyber-Attacks”, Proceedings of International Conference on Internet of Things, pp. 1-18, 2019.

  • T.U. Sheikh, H. Rahman, H.S. Al-Qahtani and T.K. Hazra, “Countermeasure of Attack Vectors using Signature-Based IDS in IoT Environments”, Proceedings of IEEE 10th Annual Conference on Information Technology, Electronics and Mobile Communication, pp. 1130-1136, 2019.

  • N. Moustafa, B. Turnbull and K.K.R. Choo, “An Ensemble Intrusion Detection Technique based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things”, IEEE Internet of Things Journal, Vol. 6, No. 3, pp. 4815-4830, 2018.

  • L. Deng, D. Li, X. Yao and D. Cox, “Mobile Network Intrusion Detection for IoT System based on Transfer Learning Algorithm”, Cluster Computing, Vol. 22, No. 4, pp. 9889-9904, 2019.

  • L. Xiao, X. Wan, X. Lu and Y. Zhang, “IoT Security Techniques based on Machine Learning: How do IoT Devices use AI to Enhance Security?”, IEEE Signal Processing Magazine, Vol. 35, No. 5, pp. 41-49, 2018.

  • A. Azmoodeh, A. Dehghantanha, M. Conti and K.K.R. Choo, “Detecting Crypto-Ransomware in IoT Networks based on Energy Consumption Footprint”, Journal of Ambient Intelligence and Humanized Computing, Vol. 9, No. 4, pp. 1141-1152, 2018.

  • T.A. Mohamed, T. Otsuka and T. Ito, “Towards Machine Learning based IoT Intrusion Detection Service”, Proceedings of International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 580-585, 2018.

  • S. Rathore and J.H. Park, “Semi-Supervised Learning based Distributed Attack Detection Framework for IoT”, Applied Soft Computing, Vol. 72, pp. 79-89, 2018.

  • J. Li, Z. Zhao and R. Li, “AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks”, IEEE Internet of Things Journal, Vol. 6, No. 2, pp. 2093-2102, 2018.

  • M. Bagaa, T. Taleb and J.B. Bernabe, “A Machine Learning Security Framework for IoT Systems”, IEEE Access, Vol. 8, pp. 1-12, 2020.

  • E. Anthi, L. Williams, and P. Burnap, “A Supervised Intrusion Detection System for Smart Home IoT Devices”, IEEE Internet of Things Journal, Vol. 6, No. 5, pp. 9042-9053, 2019.

  • D. Zheng, Z. Hong, N. Wang and P. Chen, “An Improved LDA-based ELM Classification for Intrusion Detection Algorithm in IoT Application”, Sensors, Vol. 20, No. 6, pp. 1-19, 2020.

  • S. Fenanir, F. Semchedine and A. Baadache, “A Machine Learning-Based Lightweight Intrusion Detection System for the Internet of Things”, Revue d'IntelligenceArtificielle, Vol. 33, No. 3, pp.203-211, 2019.

  • H. Hindy, E. Bayne and M. Bures, “Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study”, Proceedings of International Conference on Network, pp.1-14, 2020.

  • A.A. Diro and N. Chilamkurti, “Distributed Attack Detection Scheme using Deep Learning Approach for Internet of Things”, Future Generation Computer Systems, Vol. 82, pp. 761-768, 2018.

  • D. Li, L. Deng, M. Lee and H. Wang, “IoT Data Feature Extraction and Intrusion Detection System for Smart Cities based on Deep Migration Learning”, International Journal of Information Management, Vol. 49, pp. 533-545, 2019.

  • B.A. Tama and K.H. Rhee, “An Integration of PSO-Based Feature Selection and Random Forest for Anomaly Detection in IoT Network”, Proceedings of International Conference on Web Technologies, pp. 1-6, 2018.

  • S. Li, F. Bi and W. Chen, “An Improved Information Security Risk Assessments Method for Cyber-Physical-Social Computing and Networking”, IEEE Access, Vol. 6, pp. 10311-10319, 2018.

  • Y. Otoum, D. Liu and A. Nayak, “DL‐IDS: A Deep Learning-based Intrusion Detection Framework for Securing IoT”, Proceedings of International Conference on Transactions on Emerging Telecommunications Technologies, pp.1-16, 2019.

  • E. Hodo, X. Bellekens and A. Hamilton, “Threat Analysis of IoT Networks using Artificial Neural Network Intrusion Detection System”, Proceedings of International Symposium on Networks, Computers and Communications, pp. 1-6, 2016.

  • A. Wani and S. Revathi, “Analyzing threats of IoT Networks using SDN based Intrusion Detection System (SDIoT-IDS)”, Proceedings of International Conference on Next Generation Computing Technologies, pp. 536-542, 2017.

  • M. Ge, X. Fu, N. Syed and Z. Baig, “Deep Learning-Based Intrusion Detection for IoT Networks”, Proceedings of International Conference on Dependable Computing, pp. 256-265, 2019.

  • I. Idrissi, M. Boukabous, M. Azizi, O. Moussaoui and H. El Fadili, “Toward A Deep Learning-Based Intrusion Detection System for IoT Against Botnet Attacks”, IAES International Journal of Artificial Intelligence, Vol. 10, No. 1, pp. 1-13, 2021.

  • C. Liang, B. Shanmugam, S. Azam and A. Karim, “Intrusion Detection System for the Internet of Things based on Blockchain and Multi-Agent Systems”, Electronics, Vol. 9, No. 7, pp. 1-27, 2020.

  • A. Dushimimana, T. Tao and R. Kindong, “Bi-Directional Recurrent Neural Network for Intrusion Detection System (IDS) in the Internet of Things (IoT)”, International Journal of Advanced Engineering Research and Science, Vol. 7, No. 3, pp. 524-539, 2020.

  • H. Larijani, J. Ahmad and N. Mtetwa, “A Heuristic Intrusion Detection System for Internet-of-Things (IoT)”, Proceedings of International Conference on Intelligent Computing, pp. 86-98, 2019.


Abstract Views: 443

PDF Views: 1




  • A Comparative Study On Intrusion Detection Systems for Secured Communication in Internet of Things

Abstract Views: 443  |  PDF Views: 1

Authors

R. Anushiya
Department of Computer Science, P.K.R. Arts College for Women, India
V. S. Lavanya
Department of Computer Science, P.K.R. Arts College for Women, India

Abstract


The virtual and physical worlds are bridged using the largest digital mega-trend called the Internet of Things (IoT). Between mankind, new interactions and new business models are emerging due to the incremental growth in the Internet, machines, objects, and people connectivity. Secured communication is a typical challenge that is raised due to IoT high diversity, restricted computational resources, and protocols and standards. Because of the huge attack surface in IoT networks, they are highly vulnerable to various attacks, even with some security measures. So, for detecting attacks, it is necessary to design defense mechanisms. In IoT environments, it is highly crucial to have security defense measures like Intrusion Detection Systems (IDS). Hence, authentication and encryption traditional security countermeasures are not sufficient. At network level, to solve those issues and to protect Internet-connected frameworks, major solutions are provided by IDS. Highly unique challenges are faced by IoT specific characteristics like malware detection, ransomware, processor architecture heterogeneity, and the gap in security design. However, as in literature, various problems are raised in traditional IDS, like the high false alarm rate. In IoT, for intrusion detection, a detailed study of traditional Deep Learning (DL) and Machine Learning (ML) techniques and recent technologies is presented in this review. For presenting every selected work objective and methodology, they are analysed and this review work discusses their results. IoT systems cannot be secured by applying traditional security techniques directly due to their computational constraints and intrinsic resources. In real time, on IoT devices, unknown and known attacks are detected using ML techniques in IDS. An IDS is presented in this review and its working is independent of network structure and IoT protocols. This IDS do not require any prior knowledge of security threats. Therefore, for providing security as a service to IoT networks, an artificially intelligent IDS is developed. This review paper provides a clear discussion of various attack detection techniques, along with their benefits and drawbacks.

Keywords


Genetic Algorithms (GA), Deep Learning (DL), Intrusion Detection Systems (IDS), Internet of Things (IoT).

References