Published June 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Artificial Intelligence in IoT Security: Review of Advancements, Challenges, and Future Directions

  • 1. Department of Computer Science, University of Massachusetts Amherst, Sunnyvale, United States.

Description

Abstract: The Internet of Things (IoT) has revolutionized various industries, but its rapid expansion has also exposed a vast attack surface, making it vulnerable to cyber threats. Traditional cybersecurity measures often struggle to keep pace with the dynamic and diverse nature of IoT devices. Artificial Intelligence (AI) has emerged as a powerful tool in cybersecurity, offering the potential to revolutionize threat detection, anomaly detection, intrusion prevention, and secure authentication in IoT environments. This review paper explores the latest advancements in AI techniques for IoT security, discusses the challenges and limitations of existing approaches, and highlights future research directions. By examining the intersection of AI and IoT security, this review aims to contribute to developing more effective and resilient cybersecurity solutions for the ever-expanding IoT landscape.

Files

G991113070624.pdf

Files (338.6 kB)

Name Size Download all
md5:76b9685e09b99ac4c896e7ab0e099bbf
338.6 kB Preview Download

Additional details

Identifiers

Dates

Accepted
2024-06-15
Manuscript received on 31 May 2024 | Revised Manuscript received on 09 June 2024 | Manuscript Accepted on 15 June 2024 | Manuscript published on 30 June 2024.

References

  • Xu, L., He, W., & Li, S. (2014). Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics, 10, 2233-2243. https://doi.org/10.1109/TII.2014.2300753
  • Hernandez-Ramos, J. L., Martinez, J. A., Savarino, V., Angelini, M., Napolitano, V., Skarmeta, A. F., & Baldini, G. (2021). Security and Privacy in Internet of Things-Enabled Smart Cities: Challenges and future Directions. IEEE Security & Privacy, 19(1), 12–23. https://doi.org/10.1109/msec.2020.3012353
  • Deep, S., Zheng, X., Jolfaei, A., Yu, D., Ostovari, P., & Bashir, A. K. (2020). A survey of security and privacy issues in the Internet of Things from the layered context. Transactions on Emerging Telecommunications Technologies, 33(6). https://doi.org/10.1002/ett.3935
  • Meneghello, F., Calore, M., Zucchetto, D., Polese, M., & Zanella, A. (2019). IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices. IEEE Internet of Things Journal, 6, 8182-8201. https://doi.org/10.1109/JIOT.2019.2935189
  • Sarker, I. H., Kayes, A. S. M., Badsha, S., AlQahtani, H., Watters, P. A., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00318-5
  • Messaoud, S., Bradai, A., Bukhari, S. H. R., Quang, P. T. A., Ahmed, O. B., & Atri, M. (2020). A survey on machine learning in Internet of Things: Algorithms, strategies, and applications. Internet of Things, 12, 100314. https://doi.org/10.1016/j.iot.2020.100314
  • N. Manchanda, G. Kaur, S. Chauhan and N. Kaur, "Artificial Intelligence Based Techniques for Anomaly Detection in IoT: A Comparative Analysis," 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2023, pp. 87-92, doi: https://doi.org/10.1109/ICTACS59847.2023.10389873
  • Aruna, S., Mohana Priya, S., Reshmeetha, K., Salai Sudhayini, E., & Ajay Narayanan, A. (2023). Blockchain Integration with Artificial Intelligence and Internet of Things Technologies. 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), 688-694. https://doi.org/10.1109/ICICCS56967.2023.10142527
  • Zikria, Y.B., Ali, R., Afzal, M.K., & Kim, S.W. (2021). Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions. Sensors (Basel, Switzerland), 21. https://doi.org/10.3390/s21041174
  • Dutta, I.K., Ghosh, B., Carlson, A.H., Totaro, M.W., & Bayoumi, M.A. (2020). Generative Adversarial Networks in Security: A Survey. 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 0399-0405. https://doi.org/10.1109/UEMCON51285.2020.9298135
  • Gohel, P., Singh, P., & Mohanty, M. (2021). Explainable AI: current status and future directions. ArXiv, abs/2107.07045.
  • Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., & Bengio, Y. (2014). Generative Adversarial Nets. Neural Information Processing Systems.
  • Hu, W., & Tan, Y. (2017). Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN. ArXiv, abs/1702.05983.
  • Xiao, C., Li, B., Zhu, J., He, W., Liu, M., & Song, D.X. (2018). Generating Adversarial Examples with Adversarial Networks. ArXiv, abs/1801.02610. https://doi.org/10.24963/ijcai.2018/543
  • Huang, A., Al-Dujaili, A., Hemberg, E., & O'Reilly, U. (2018). Adversarial Deep Learning for Robust Detection of Binary Encoded Malware. 2018 IEEE Security and Privacy Workshops (SPW), 76-82. https://doi.org/10.1109/SPW.2018.00020
  • Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2019). A Survey of Network-based Intrusion Detection Data Sets. Comput. Secur., 86, 147-167. https://doi.org/10.1016/j.cose.2019.06.005
  • Wang, D., Li, C., Wen, S., Nepal, S., & Xiang, Y. (2018). Defending Against Adversarial Attack Towards Deep Neural Networks Via Collaborative Multi-Task Training. IEEE Transactions on Dependable and Secure Computing, 19, 953-965. https://doi.org/10.1109/TDSC.2020.3014390
  • Zixu, T., Liyanage, K.S., & Mohan, G. (2020). Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks. GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 1-7. https://doi.org/10.1109/GLOBECOM42002.2020.9348244
  • Sagduyu, Y.E., Shi, Y., & Erpek, T. (2019). IoT Network Security from the Perspective of Adversarial Deep Learning. 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 1-9. https://doi.org/10.1109/SAHCN.2019.8824956
  • Usama, M., Asim, M., Latif, S., & Qadir, J. (2019, June). Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems. In 2019 15th international wireless communications & mobile computing conference (IWCMC) (pp. 78-83). IEEE. https://doi.org/10.1109/IWCMC.2019.8766353
  • Sutton, R.S., & Barto, A.G. (1998). Reinforcement Learning: An Introduction. IEEE Trans. Neural Networks, 9, 1054-1054. https://doi.org/10.1109/TNN.1998.712192
  • Adawadkar, A.M., & Kulkarni, N. (2022). Cyber-security and reinforcement learning - A brief survey. Eng. Appl. Artif. Intell., 114, 105116. https://doi.org/10.1109/COMST.2021.3073036
  • Chen, W., Qiu, X., Cai, T., Dai, H., Zheng, Z., & Zhang, Y. (2021). Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 23, 1659-1692. https://doi.org/10.1109/COMST.2021.3073036
  • Wang, X., Wang, C., Li, X., Leung, V.C., & Taleb, T. (2020). Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching. IEEE Internet of Things Journal, 7, 9441-9455. https://doi.org/10.1109/JIOT.2020.2986803
  • Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529-533. https://doi.org/10.1038/nature14236
  • Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016). Resource Management with Deep Reinforcement Learning. Proceedings of the 15th ACM Workshop on Hot Topics in Networks. https://doi.org/10.1145/3005745.3005750
  • Nguyen, T.T., & Reddi, V.J. (2019). Deep Reinforcement Learning for Cyber Security. IEEE Transactions on Neural Networks and Learning Systems, 34, 3779-3795. https://doi.org/10.1109/TNNLS.2021.3121870
  • Alavizadeh, H., Jang, J., & Alavizadeh, H. (2021). Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection. Comput., 11, 41. https://doi.org/10.3390/computers11030041
  • Al-amri, R., Murugesan, R.K., Man, M.B., Abdulateef, A.F., Al-Sharafi, M.A., & Alkahtani, A.A. (2021). A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data. Applied Sciences. https://doi.org/10.3390/app11125320
  • Li, Y. (2017). Deep Reinforcement Learning: An Overview. ArXiv, abs/1701.07274.
  • Liang, E., Liaw, R., Nishihara, R., Moritz, P., Fox, R., Gonzalez, J., Goldberg, K., & Stoica, I. (2017). Ray RLLib: A Composable and Scalable Reinforcement Learning Library. ArXiv, abs/1712.09381.
  • Tharewal, S., Ashfaque, M.W., Banu, S.S., Uma, P., Hassen, S.M., & Shabaz, M. (2022). Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/9023719
  • Tharewal, S., Ashfaque, M.W., Banu, S.S., Uma, P., Hassen, S.M., & Shabaz, M. (2022). Intrusion Detection System for Industrial Internet of Things Based on Deep Reinforcement Learning. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/9023719
  • Hüttenrauch, M., Šošić, A., & Neumann, G. (2018). Deep Reinforcement Learning for Swarm Systems. J. Mach. Learn. Res., 20, 54:1-54:31.
  • Huang, S.H., Papernot, N., Goodfellow, I.J., Duan, Y., & Abbeel, P. (2017). Adversarial Attacks on Neural Network Policies. ArXiv, abs/1702.02284.
  • Samek, W., & Müller, K. (2019). Towards Explainable Artificial Intelligence. ArXiv, abs/1909.12072. https://doi.org/10.1007/978-3-030-28954-6_1
  • Ribeiro, M., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939778
  • Lundberg, S.M., & Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. Neural Information Processing Systems
  • Wachter, S., Mittelstadt, B.D., & Russell, C. (2017). Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR. Cybersecurity. https://doi.org/10.2139/ssrn.3063289
  • Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052
  • Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv: Machine Learning.
  • R. Prabha, Balakrishnan S, S. Deivanayagi, V.K.G. Kalaiselvi, D. Pushgara rani , Aswin G, A Review of Classification Algorithms in Machine Learning for Medical IoT, International Journal of Pharmaceutical Research. Jan - Mar 2021, Vol. 13, Issue 1, pp. 3000 – 3007. https://doi.org/10.31838/ijpr/2021.13.01.448
  • Sridhar, P. K., Srinivasan, N., Arun Kumar, A., Rajendran, G., & Perumalsamy, K. K. (2024). A Case Study on the Diminishing Popularity of Encoder-Only Architectures in Machine Learning Models. In International Journal of Innovative Technology and Exploring Engineering (Vol. 13, Issue 4, pp. 22–27). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijitee.d9827.13040324
  • Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4. https://doi.org/10.1126/scirobotics.aay7120
  • Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys (CSUR), 51, 1 - 42. https://doi.org/10.1145/3236009
  • S. Vasu, A.K. Puneeth Kumar, T. Sujeeth, Dr.S. Balakrishnan, "A Machine Learning Based Approach for Computer Security", Jour of Adv Research in Dynamical & Control Systems. Vol.10, 11-Special issue, 2018, pp. 915- 919.
  • Rajendran, G., Arun Kumar, A., Sridhar, P. K., Perumalsamy, K. K., & Srinivasan, N. (2024). A Comprehensive Approach for Enhancing OSINT through Leveraging LLMs. International Refereed Journal of Engineering and Science (IRJES), 13(2), 61–66. https://www.irjes.com/Papers/vol13-issue2/H13026166.pdf
  • Barocas, S., & Selbst, A.D. (2016). Big Data's Disparate Impact. California Law Review, 104, 671. https://doi.org/10.2139/ssrn.2477899
  • Papernot, N., Mcdaniel, P., Goodfellow, I.J., Jha, S., Celik, Z.B., & Swami, A. (2016). Practical Black-Box Attacks against Machine Learning. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. https://doi.org/10.1145/3052973.3053009
  • S. Balakrishnan, Taxonomy and Architecture of Internet of Things: An overview of Disruptive Technology, CSI Communications magazine, Volume No. 44, Issue No. 1, April 2020, pp. 8-10.
  • Srinivasan, N., Perumalsamy, K. K., Sridhar, P. K., Rajendran, G., & Arun Kumar, A. (2024). Comprehensive Study on Bias In Large Language Models. International Refereed Journal of Engineering and Science (IRJES), 13(2), 77–82. https://www.irjes.com/Papers/vol13-issue2/J13027782.pdf
  • Cyber Security Affairs in Empowering Technologies. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10S, pp. 1–7). https://doi.org/10.35940/ijitee.j1001.08810s19
  • Gupta, S., Sabitha, A. S., & Punhani, R. (2019). Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 6133–6140). https://doi.org/10.35940/ijrte.c5675.098319
  • Ringsia, S., & G, S. (2020). A Policy Based Data Security and Key Management System. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 227–229). https://doi.org/10.35940/ijeat.e9700.069520