IoT Cybersecurity Threats and Detection Mechanisms: A Review
DOI:
https://doi.org/10.31185/wjps.136Keywords:
machine-learning, deep-learning, IoT-networks, anomaly detectionAbstract
Now a day the internet of thing (IoT) grab the attention of many researchers and companies due to different direction of utilization. The cyber security of IoT become one of the aspects of the critical challenges. There are many intrusion detection systems (IDSs) to solve different issues of IoT-Cyber security threats. In this article, we review the state-of-the-art of IoT-IDS, focusing on the strategy that was devised and executed, the dataset that was utilized, the findings, and the assessment that was undertaken. Additionally, the surveyed articles undergo critical analysis and statements in order to give a thorough comparative review. Machine learning and deep learning methods, as well as new classification and feature selection methodologies, are studied and researched. Thus far, each technique has proved the capability of constructing very accurate intrusion detection models.
References
Smaha, Stephen E. "Haystack: An intrusion detection system." Fourth Aerospace Computer Security Applications Conference. Vol. 44. 1988.
Yassin, Warusia, et al. "Signature-Based Anomaly intrusion detection using Integrated data mining classifiers." 2014 International symposium on biometrics and security technologies (ISBAST). IEEE, 2014.
Blaise, Agathe, et al. "Detection of zero-day attacks: An unsupervised port-based approach." Computer Networks 180 (2020): 107391.
Patel, Keyur K., Sunil M. Patel, and P. Scholar. "Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges." International journal of engineering science and computing 6.5 (2016).
Chaabouni, Nadia, et al. "Network intrusion detection for IoT security based on learning techniques." IEEE Communications Surveys & Tutorials 21.3 (2019): 2671-2701.
Koroniotis, Nickolaos, et al. "Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset." Future Generation Computer Systems 100 (2019): 779-796.
Abbasi, Fereshteh, Marjan Naderan, and Seyed Enayatallah Alavi. "Anomaly detection in Internet of Things using feature selection and classification based on Logistic Regression and Artificial Neural Network on N-BaIoT dataset." 2021 5th International Conference on Internet of Things and Applications (IoT). IEEE, 2021.
M. Pahl and F. Aubet, “All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection,” IEEE Computer Society, pp. 72–80, 2018. [Online]. Available: http://ieeexplore.ieee.org/document/ 8584985.
A. W. Atamli and A. Martin, "Threat-Based Security Analysis for the Internet of Things," 2014 International Workshop on Secure Internet of Things, 2014, pp. 35-43, doi: 10.1109/SIoT.2014.10.
C. Karlof and D. Wagner, “Secure routing in wireless sensor networks: attacks and countermeasures,” in Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003., May 2003, pp. 113–127.
H. Bostani and M. Sheikhan, “Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach,” Computer Communications, vol. 98, no. Supplement C, pp. 52–71, Jan. 2017. [Online]. Available: http://www:sciencedirect:com/science/article/pii/S0140366416306387
L. Wallgren, S. Raza, and T. Voigt, “Routing Attacks and Countermeasures in the RPL-Based Internet of Things,” International Journal of Distributed Sensor Networks, vol. 9, no. 8, p. 794326, Aug. 2013. [Online]. Available: https://doi:org/10:1155/2013/794326
K. Chugh, A. Lasebae, J. Loo, Case study of a black hole attack on 6LoWPAN-RPL, In: Proceedings of the Sixth International Conference on Emerging Security Information, Systems and Technologies, Rome, Italy (2012) 157-162.
Microsoft, “The STRIDE Threat Model,” 2005. [Online]. Available: https://msdn:microsoft:com/fr-fr/en-en/enus/library/ee823878(v= cs:20):aspx
S. Prowell, R. Kraus, and M. Borkin, Seven Deadliest Network Attacks. Elsevier, 2010
S. Krushang and H. Upadhyay, “A Survey: DDOS Attack on Internet of Things,” International Journal of Engineering Research and Development, vol. Volume 10, no. Issue 11, pp. 58–63, Nov. 2014. [Online]. Available: www:ijerd:com
Alhowaide, Alaa, Izzat Alsmadi, and Jian Tang. "Ensemble detection model for IoT IDS." Internet of Things 16 (2021): 100435.
Shafiq, Muhammad, et al. "CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques." IEEE Internet of Things Journal 8.5 (2020): 3242-3254.
Yanping Shen, Kangfeng Zheng, Chunhua Wu, Mingwu Zhang, Xinxin Niu, Yixian Yang, An Ensemble Method based on Selection Using Bat Algorithm for Intrusion Detection, The Computer Journal, Volume 61, Issue 4, April 2018, Pages 526–538
Midi, Daniele, et al. "Kalis—A system for knowledge-driven adaptable intrusion detection for the Internet of Things." 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017.
Saheed, Yakub Kayode, et al. "A machine learning-based intrusion detection for detecting internet of things network attacks." Alexandria Engineering Journal 61.12 (2022): 9395-9409.
Amouri, Amar, Vishwa T. Alaparthy, and Salvatore D. Morgera. "A machine learning based intrusion detection system for mobile Internet of Things." Sensors 20.2 (2020): 461.
Amin, Ruhul, et al. "A light weight authentication protocol for IoT-enabled devices in distributed Cloud Computing environment." Future Generation Computer Systems 78 (2018): 1005-1019.
Gope, Prosanta, et al. "Lightweight and privacy-preserving RFID authentication scheme for distributed IoT infrastructure with secure localization services for smart city environment." Future Generation Computer Systems 83 (2018): 629-637.
Barcelo, Marc, et al. "IoT-cloud service optimization in next generation smart environments." IEEE Journal on Selected Areas in Communications 34.12 (2016): 4077-4090.
Onah, John Oche, et al. "Genetic Algorithm based feature selection and Naïve Bayes for anomaly detection in fog computing environment." Machine Learning with Applications (2021): 100156.
Moustafa, Nour, Benjamin Turnbull, and Kim-Kwang Raymond 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 6.3 (2018): 4815-4830.
Liu, Liqun, et al. "An intrusion detection method for the internet of things based on suppressed fuzzy clustering." EURASIP Journal on Wireless Communications and Networking 2018.1 (2018): 1-7.
Kiran, KVVNL Sai, et al. "Building a intrusion detection system for iot environment using machine learning techniques." Procedia Computer Science 171 (2020): 2372-2379.
Verma, Abhishek, and Virender Ranga. "Machine learning based intrusion detection systems for IoT applications." Wireless Personal Communications 111.4 (2020): 2287-2310.
Ge, Mengmeng, et al. "Deep learning-based intrusion detection for IoT networks." 2019 IEEE 24th pacific rim international symposium on dependable computing (PRDC). IEEE, 2019
Otoum, Yazan, Dandan Liu, and Amiya Nayak. "DL‐IDS: a deep learning–based intrusion detection framework for securing IoT." Transactions on Emerging Telecommunications Technologies 33.3 (2022): e3803.
Tian, Zhihong, et al. "A distributed deep learning system for web attack detection on edge devices." IEEE Transactions on Industrial Informatics 16.3 (2019): 1963-1971.
Khan, Muhammad Almas, et al. "A deep learning-based intrusion detection system for mqtt enabled iot." Sensors 21.21 (2021): 7016.
Ferrag, Mohamed Amine, et al. "Deep learning-based intrusion detection for distributed denial of service attack in Agriculture 4.0." Electronics 10.11 (2021): 1257.
Lopez-Martin, Manuel, et al. "Shallow neural network with kernel approximation for prediction problems in highly demanding data networks." Expert Systems with Applications 124 (2019): 196-208.
Nimbalkar, Pushparaj, and Deepak Kshirsagar. "Feature selection for intrusion detection system in Internet-of-Things (IoT)." ICT Express 7.2 (2021): 177-181.
Parimala, G., and R. Kayalvizhi. "An effective intrusion detection system for securing IoT using feature selection and deep learning." 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021.
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, no. 3, pp. 2671-2701, 2019.
J. Pang, Y. Huang, Z. Xie, Q. Han, and Z. Cai, "Realizing the heterogeneity: A self-organized federated learning framework for IoT," IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3088-3098, 2020.
A. A. Cook, G. Mısırlı, and Z. Fan, "Anomaly detection for IoT time-series data: A survey," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481-6494, 2019.
W. Liang, W. Huang, J. Long, K. Zhang, K.-C. Li, and D. Zhang, "Deep reinforcement learning for resource protection and real-time detection in IoT environment," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6392-6401, 2020.
H. Tyagi and R. Kumar, "Cloud computing for iot," in Internet of Things (IoT): Springer, 2020, pp. 25-41.
D. Zorbas, C. Caillouet, K. Abdelfadeel Hassan, and D. Pesch, "Optimal Data Collection Time in LoRa Networks-A Time-Slotted Approach," Sensors (Basel), vol. 21, no. 4, Feb 8 2021, doi: 10.3390/s21041193.
L. D. X. Shancang Li, and Xinheng Wang, "Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things," IEEE Transactions on Industrial Informatics vol. Volume: 9, no. Issue: 4, pp. Page(s): 2177 - 2186, November 2013, doi: 10.1109/TII.2012.2189222.
N. C. Luong, D. T. Hoang, P. Wang, D. Niyato, D. I. Kim, and Z. Han, "Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey," IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2546-2590, 2016.
Z. H. Ali, H. A. Ali, and M. M. Badawy, "Internet of Things (IoT): definitions, challenges and recent research directions," International Journal of Computer Applications, vol. 128, no. 1, pp. 37-47, 2015.
J. Kiljander et al., "Semantic interoperability architecture for pervasive computing and internet of things," IEEE access, vol. 2, pp. 856-873, 2014.
M. Noura, M. Atiquzzaman, and M. Gaedke, "Interoperability in internet of things: Taxonomies and open challenges," Mobile networks and applications, vol. 24, no. 3, pp. 796-809, 2019.
M. Ganzha, M. Paprzycki, W. Pawłowski, P. Szmeja, and K. Wasielewska, "Towards semantic interoperability between Internet of Things platforms," in Integration, interconnection, and interoperability of IoT systems: Springer, 2018, pp. 103-127.
J. Kim, J. Jeong, and J. Shin, "M2m: Imbalanced classification via major-to-minor translation," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 13896-13905.
H. Shi, X. Yang, Q. Zhou, and Q. Lian, "SAR Slow Moving Target Imaging Based on Over‐Sampling Smooth Algorithm," Chinese Journal of Electronics, vol. 26, no. 4, pp. 876-882, 2017.
Y. Liu, T. Dillon, W. Yu, W. Rahayu, and F. Mostafa, "Missing value imputation for industrial IoT 5 data with large gaps," IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6855-6867, 2020.
W.-C. Lin and C.-F. Tsai, "Missing value imputation: a review and analysis of the literature (2006–2017)," Artificial Intelligence Review, vol. 53, no. 2, pp. 1487-1509, 2020.
S. Khare and M. Totaro, "Big data in IoT," in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019: IEEE, pp. 1-7.
G. Chandrashekar and F. Sahin, "A survey on feature selection methods," Computers & Electrical Engineering, vol. 40, no. 1, pp. 16-28, 2014.
M. Mafarja, A. A. Heidari, M. Habib, H. Faris, T. Thaher, and I. Aljarah, "Augmented whale feature selection for IoT attacks: Structure, analysis and applications," Future Generation Computer Systems, vol. 112, pp. 18-40, 2020.
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