2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 2 , PP: 183-190, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Razan Abdulhammed 1 , Shaima Miqdad Mohamed Najeeb 2 , Rabei Raad Ali 3 * , Mohammed Ahmed Jubair 4
Doi: https://doi.org/10.54216/JISIoT.150213
Internet of Things (IoT) has emerged as a new paradigm for integrating internet resources and physical objects. It provides a better standard of living in different domains, like industrial processes, home automation, and environmental monitoring. The growth of IoT depends on the need to connect more devices via the Internet. However, anywhere internet connectivity is involved, security poses as an enormous challenge. Intrusion Detection Systems (IDS) can protect IoTs by applying rules related to IoTs operation. This paper reviews some of the mechanisms of IoT-related IDS, which protect IoT devices against various attacks. The paper includes a summary of the recent developments of IDS against many security threats. A review is presented regarding various IDS designs developed in the last decade with different methods, ideas, and approaches toward a better understanding of suitable IDS platforms that provide security against the global growth of attacks and intruders. It also involves the examination of the IDS basics, types, and components of the previously proposed systems, as well as discussing the pros and disadvantages of each. We organize the taxonomy of investigated IDS approaches using the detection approaches. This work aims to provide a thorough summary of the existing IDS designs and issues to empower research and development for IDS about IoTs.
VANETs , DTDF-TARP , UAVs , routing protocol , CLO-MFG , RDJ-EDC
[1] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, pp. 1645–1660, 2013.
[2] K. K. Patel, S. M. Patel, and P. Scholar, “Internet of Things-IOT: Definition, Characteristics, Architecture, Enabling Technologies, Application & Future Challenges,” 2016.
[3] A. R. Khan, “Zero Trust-Based Blockchain-Based IoT Security with Consensus and Access Control Framework,” Journal of Intelligent Systems & Internet of Things, vol. 12, no. 1, 2024.
[4] R. Jabbar et al., “Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety,” arXiv preprint arXiv: 2003.07672, Mar. 2020. [Online]. Available: https://arxiv.org/abs/2003.07672.
[5] R.-Y. Ju and W. Cai, “Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm,” arXiv preprint arXiv: 2304.05071, Apr. 2023. [Online]. Available: https://arxiv.org/abs/2304.05071.
[6] N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network Intrusion Detection for IoT Security Based on Learning Techniques,” IEEE Communications Surveys & Tutorials, vol. 21, pp. 2671–2701, 2019.
[7] M. F. Elrawy, A. I. Awad, and H. F. Hamed, “Intrusion Detection Systems for IoT-Based Smart Environments: A Survey,” Journal of Cloud Computing, vol. 7, 2018.
[8] J. Asharf et al., “A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions,” Electronics, 2020.
[9] A. Boyanapalli and A. Shanthini, “A Comparative Study of Techniques, Datasets and Performances for Intrusion Detection Systems in IoT,” 2020.
[10] B. B. Zarpelão, R. S. Miani, C. T. Kawakani, and S. C. Alvarenga, “A Survey of Intrusion Detection in Internet of Things,” Journal of Network and Computer Applications, vol. 84, pp. 25–37, 2017.
[11] E. Benkhelifa, T. Welsh, and W. Hamouda, “A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems,” IEEE Communications Surveys & Tutorials, vol. 20, pp. 3496–3509, 2018.
[12] H. Wu, H. Han, X. Wang, and S. Sun, “Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey,” IEEE Access, vol. 8, pp. 153826–153848, 2020.
[13] Kamaldeep, M. Dutta, and J. Granjal, “Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms,” IEEE Access, vol. 8, pp. 127272–127312, 2020.
[14] M. S. Mahmoud and A. A. Mohamad, “A Study of Efficient Power Consumption Wireless Communication Techniques/Modules for Internet of Things (IoT) Applications,” IoT, 2016.
[15] H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, and K. Choo, “A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks,” IEEE Transactions on Emerging Topics in Computing, vol. 7, pp. 314–323, 2019.
[16] B. A. Baalbaki, J. Pacheco, C. Tunc, S. Hariri, and Y. B. Al-Nashif, “Anomaly Behavior Analysis System for ZigBee in Smart Buildings,” in IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), 2015, pp. 1–4.
[17] H. R. Alipour, Y. B. Al-Nashif, and S. Hariri, “IEEE 802.11 Anomaly-Based Behavior Analysis,” in International Conference on Computing, Networking and Communications (ICNC), 2013, pp. 369–373.
[18] D. Airehrour, J. Gutiérrez, and S. K. Ray, “Securing RPL Routing Protocol from Blackhole Attacks Using a Trust-Based Mechanism,” in 26th International Telecommunication Networks and Applications Conference (ITNAC), 2016, pp. 115–120.
[19] J. Granjal, J. M. Silva, and N. Lourenço, “Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection,” Sensors (Basel, Switzerland), vol. 18, 2018.
[20] A. Le, J. K. Loo, K. K. Chai, and M. Aiash, “A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology,” Information, vol. 7, p. 25, 2016.
[21] L. Wallgren, S. Raza, and T. Voigt, “Routing Attacks and Countermeasures in the RPL-Based Internet of Things,” International Journal of Distributed Sensor Networks, 2013.
[22] B. A. Baalbaki, J. Pacheco, C. Tunc, S. Hariri, and Y. B. Al-Nashif, “Anomaly Behavior Analysis System for ZigBee in Smart Buildings,” in IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), 2015, pp. 1–4.
[23] A. Mondal, S. Paul, A. Mitra, and B. Gope, “Automated Signature Generation for Polymorphic Worms Using Substrings Extraction and Principal Component Analysis,” in IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2015, pp. 1–4.
[24] D. Kshirsagar and S. Kumar, “An Ontology Approach for Proactive Detection of HTTP Flood DoS Attack,” International Journal of System Assurance Engineering and Management, vol. 14, pp. 840–847, 2021.
[25] A. M. Cardoso, R. F. Lopes, A. S. Teles, and F. B. Magalhães, “Poster Abstract: Real-Time DDoS Detection Based on Complex Event Processing for IoT,” in International Conference on Internet-of-Things Design and Implementation, 2018.
[26] S. Raza, L. Wallgren, and T. Voigt, “SVELTE: Real-Time Intrusion Detection in the Internet of Things,” Ad Hoc Networks, vol. 11, pp. 2661–2674, 2013.
[27] H. Sedjelmaci, S. Senouci, and T. Taleb, “An Accurate Security Game for Low-Resource IoT Devices,” IEEE Transactions on Vehicular Technology, vol. 66, pp. 9381–9393, 2017.
[28] J. Krimmling and S. Peter, “Integration and Evaluation of Intrusion Detection for CoAP in Smart City Applications,” in IEEE Conference on Communications and Network Security, 2014, pp. 73–78.
[29] S. T. Zargar, J. B. Joshi, and D. Tipper, “A Survey of Defense Mechanisms against Distributed Denial of Service (DDoS) Flooding Attacks,” IEEE Communications Surveys & Tutorials, vol. 15, pp. 2046–2069, 2013.
[30] J. Sengupta, S. Ruj, and S. D. Bit, “A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT,” Journal of Network and Computer Applications, vol. 149, 2020.
[31] A. Rghioui, A. Khannous, and M. Bouhorma, “Denial-of-Service Attacks on 6LoWPAN-RPL Networks: Threats and an Intrusion Detection System Proposition,” 2014.
