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Journal of Cybersecurity and Information Management
Volume 11 , Issue 1, PP: 38-46 , 2023 | Cite this article as | XML | Html |PDF


A Deep Learning Framework for Securing IoT Against Malwares

Authors Names :   Mustafa El-Taie   1 *     Aaras Y.Kraidi   2  

1  Affiliation :  Digital Charging Solutions GmbH, Germany

    Email :  Mustafa.iessa@gmail.com

2  Affiliation :  University of Technology and Applied Science, Shinas, Oman

    Email :  aaras.kraidi@shct.edu.om

Doi   :   https://doi.org/10.54216/JCIM.110104

Received: October 23, 2022 Revised: December 26, 2022 Accepted: January 08, 2023

Abstract :

The proliferation of Internet of Things (IoT) devices has led to an increase in the number of malware attacks targeting these devices. Traditional security mechanisms such as firewalls and antivirus software are often inadequate in protecting IoT devices from malware attacks due to their limited resources and the heterogeneity of IoT networks. In this paper, we propose DeepSecureIoT, a deep learning-based framework for securing IoT against malware attacks. Our proposed framework uses a deep convolutional neural network (CNN) to extract features from network traffic and classify it as normal or malicious. The CNN is trained using a large dataset of network traffic to accurately identify malware attacks and reduce false positives. We evaluate the performance of DeepSecureIoT using a benchmark dataset of real-world IoT malware attacks. The results show that our proposed framework achieves an accuracy of 0.961 in detecting and classifying malware attacks, outperforming state-of-the-art intrusion detection systems. Moreover, DeepSecureIoT has low computational overhead and can be deployed on resource-constrained IoT devices.

Keywords :

Secure IoT; Malwares; Deep learning;  Convolutional Neural Network

References :

[1]. Kumar, A. and Lim, T.J., 2019, April. EDIMA: Early detection of IoT malware network activity using machine learning techniques. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) (pp. 289-294). IEEE.

[2]. Nakhodchi, Sanaz, Aaruni Upadhyay, and Ali Dehghantanha. "A comparison between different machine learning models for iot malware detection." Security of Cyber-Physical Systems: Vulnerability and Impact (2020): 195-202.

[3]. Nguyen, K.D.T., Tuan, T.M., Le, S.H., Viet, A.P., Ogawa, M. and Le Minh, N., 2018, November. Comparison of three deep learning-based approaches for IoT malware detection. In 2018 10th international conference on Knowledge and Systems Engineering (KSE) (pp. 382-388). IEEE.

[4]. Shobana, M. and Poonkuzhali, S., 2020, February. A novel approach to detect IoT malware by system calls using Deep learning techniques. In 2020 International Conference on Innovative Trends in Information Technology (ICITIIT) (pp. 1-5). IEEE.

[5]. Bendiab, G., Shiaeles, S., Alruban, A. and Kolokotronis, N., 2020, June. IoT malware network traffic classification using visual representation and deep learning. In 2020 6th IEEE Conference on Network Softwarization (NetSoft) (pp. 444-449). IEEE.

[6]. Ren, Z., Wu, H., Ning, Q., Hussain, I. and Chen, B., 2020. End-to-end malware detection for android IoT devices using deep learning. Ad Hoc Networks, 101, p.102098.

[7]. Xiao, L., Wan, X., Lu, X., Zhang, Y. and Wu, D., 2018. IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?. IEEE Signal Processing Magazine, 35(5), pp.41-49.

[8]. Ding, F., Li, H., Luo, F., Hu, H., Cheng, L., Xiao, H. and Ge, R., 2020, October. DeepPower: Non-intrusive and deep learning-based detection of IoT malware using power side channels. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (pp. 33-46).

[9]. Tien, C.W., Chen, S.W., Ban, T. and Kuo, S.Y., 2020. Machine learning framework to analyze iot malware using elf and opcode features. Digital Threats: Research and Practice, 1(1), pp.1-19. [10]. Saad, S., Briguglio, W. and Elmiligi, H., 2019. The curious case of machine learning in malware detection. arXiv preprint arXiv:1905.07573.

[11]. HaddadPajouh, H., Dehghantanha, A., Khayami, R. and Choo, K.K.R., 2018. A deep recurrent neural network based approach for internet of things malware threat hunting. Future Generation Computer Systems, 85, pp.88-96.

[12]. Xiao, F., Lin, Z., Sun, Y. and Ma, Y., 2019. Malware detection based on deep learning of behavior graphs. Mathematical Problems in Engineering, 2019, pp.1-10.

[13]. Abusnaina, A., Khormali, A., Alasmary, H., Park, J., Anwar, A. and Mohaisen, A., 2019, July. Adversarial learning attacks on graph-based IoT malware detection systems. In 2019 IEEE 39th international conference on distributed computing systems (ICDCS) (pp. 1296-1305). IEEE.

[14]. Peters, W., Dehghantanha, A., Parizi, R.M. and Srivastava, G., 2020. A comparison of state-of-the-art machine learning models for OpCode-based IoT malware detection. Handbook of Big Data Privacy, pp.109-120.

[15]. Karbab, E.B., Debbabi, M., Derhab, A. and Mouheb, D., 2018. MalDozer: Automatic framework for android malware detection using deep learning. Digital Investigation, 24, pp.S48-S59. [16]. Naeem, H., Ullah, F., Naeem, M.R., Khalid, S., Vasan, D., Jabbar, S. and Saeed, S., 2020. Malware detection in industrial internet of things based on hybrid image visualization and deep learning model. Ad Hoc Networks, 105, p.102154.

[17]. Karanja, E.M., Masupe, S. and Jeffrey, M.G., 2020. Analysis of internet of things malware using image texture features and machine learning techniques. Internet of Things, 9, p.100153. [18]. Su, J., Vasconcellos, D.V., Prasad, S., Sgandurra, D., Feng, Y. and Sakurai, K., 2018, July. Lightweight classification of IoT malware based on image recognition. In 2018 IEEE 42Nd annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 664-669). IEEE.

[19]. Amin, M., Shehwar, D., Ullah, A., Guarda, T., Tanveer, T.A. and Anwar, S., 2020. A deep learning system for health care IoT and smartphone malware detection. Neural Computing and Applications, pp.1-12.

[20]. Jeon, J., Park, J.H. and Jeong, Y.S., 2020. Dynamic analysis for IoT malware detection with convolution neural network model. IEEE Access, 8, pp.96899-96911.

[21]. Vasan, D., Alazab, M., Venkatraman, S., Akram, J. and Qin, Z., 2020. MTHAEL: Cross-architecture IoT malware detection based on neural network advanced ensemble learning. IEEE Transactions on Computers, 69(11), pp.1654-1667.

[22]. Sharma, K. and Nandal, R., 2019, April. A literature study on machine learning fusion with IOT. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1440-1445). IEEE.

[23]. Ficco, M., 2019, June. Detecting IoT malware by Markov chain behavioral models. In 2019 IEEE International Conference on Cloud Engineering (IC2E) (pp. 229-234). IEEE.

[24]. Darabian, H., Dehghantanha, A., Hashemi, S., Homayoun, S. and Choo, K.K.R., 2020. An opcode‚Äźbased technique for polymorphic Internet of Things malware detection. Concurrency and Computation: Practice and Experience, 32(6), p.e5173.

[25]. Jahromi, A.N., Hashemi, S., Dehghantanha, A., Choo, K.K.R., Karimipour, H., Newton, D.E. and Parizi, R.M., 2020. An improved two-hidden-layer extreme learning machine for malware hunting. Computers & Security, 89, p.101655.

[26]. Agarap, A. F. (2017). Towards building an intelligent anti-malware system: a deep learning approach using support vector machine (SVM) for malware classification. arXiv preprint arXiv:1801.00318.

Cite this Article as :
Style #
MLA Mustafa El-Taie ,Aaras Y.Kraidi. "A Deep Learning Framework for Securing IoT Against Malwares." Journal of Cybersecurity and Information Management, Vol. 11, No. 1, 2023 ,PP. 38-46.
APA Mustafa El-Taie ,Aaras Y.Kraidi. (2023). A Deep Learning Framework for Securing IoT Against Malwares. Journal of Cybersecurity and Information Management, 11 ( 1 ), 38-46.
Chicago Mustafa El-Taie ,Aaras Y.Kraidi. "A Deep Learning Framework for Securing IoT Against Malwares." Journal of Cybersecurity and Information Management, 11 no. 1 (2023): 38-46.
Harvard Mustafa El-Taie ,Aaras Y.Kraidi. (2023). A Deep Learning Framework for Securing IoT Against Malwares. Journal of Cybersecurity and Information Management, 11 ( 1 ), 38-46.
Vancouver Mustafa El-Taie ,Aaras Y.Kraidi. A Deep Learning Framework for Securing IoT Against Malwares. Journal of Cybersecurity and Information Management, (2023); 11 ( 1 ): 38-46.
IEEE Mustafa El-Taie,Aaras Y.Kraidi, A Deep Learning Framework for Securing IoT Against Malwares, Journal of Cybersecurity and Information Management, Vol. 11 , No. 1 , (2023) : 38-46 (Doi   :  https://doi.org/10.54216/JCIM.110104)