AI-Empowered Malware Detection System for Iot
Abstract
The Internet of Things (IoT) has revolutionized the way we live and work. However, the increased connectivity of IoT devices has also made them a target for malware attacks. Traditional malware detection methods are not always effective against IoT malware, as they often rely on signatures that attackers can easily circumvent. In this paper, we propose an AI-powered malware detection system for IoT. Our system uses a hybrid deep learning approach that combines convolutional neural networks (CNN) and long short-term memory networks (LSTM). CNNs are used to extract features from IoT malware binary code, and LSTM networks are used to model the temporal relationships between these features. We evaluate the system against his three datasets of publicly available IoT malware. As a result, we found that our system achieved him a high accuracy of 99.98% in detecting his IoT malware. We also show that our system is effective against zero-day IoT malware, malware that has not yet been discovered by security researchers.
References
2. Almiani, M.; AbuGhazleh, A.; Al-Rahayfeh, A.; Atiewi, S.; Razaque, A. Deep recurrent neural network for IoT intrusion detection system. Simul. Model. Pract. Theory 2020, 101, 102031. [Google Scholar] [CrossRef]
3. Ali, S.S.; Choi, B.J. State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics 2020, 9, 1030. [Google Scholar]
4. Tahsien, S.M.; Karimipour, H.; Spachos, P. Machine learning based solutions for security of Internet of Things (IoT): A survey. J. Netw. Comput. Appl. 2020, 161, 102630.
5. Mahbub, M. Progressive researches on IoT security: An exhaustive analysis from the perspective of protocols, vulnerabilities, and preemptive architectonics. J. Netw. Comput. Appl. 2020, 168, 102761. [Google Scholar] [CrossRef]
6. Bagaa, M.; Taleb, T.; Bernabe, J.B.; Skarmeta, A. A Machine Learning Security Framework for Iot Systems. IEEE Access 2020, 8, 114066–114077.
7. Echeverría, A.; Cevallos, C.; Ortiz-Garces, I.; Andrade, R.O. Cybersecurity model based on hardening for secure internet of things implementation. Appl. Sci. 2021, 11, 3260. [Google Scholar]
8. Ahmad, Z.; Khan, A.S.; Nisar, K.; Haider, I.; Hassan, R.; Haque, M.R.; Tarmizi, S.; Rodrigues, J.J.P.C. Anomaly detection using deep neural network for IOT architecture. Appl. Sci. 2021, 11, 7050.
9. Djenna, A.; Harous, S.; Saidouni, D.E. Internet of things meet internet of threats: new concern cyber security issues of critical cyber infrastructure. Appl. Sci. 2021, 11, 4580.
10. Rezaei, H.; Faaljou, H.; Mansourfar, G. Intelligent Asset Allocation using Predictions of Deep Frequency Decomposition. Expert Syst. Appl. 2021, 186, 115715.
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