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MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

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Abstract

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves \(99.5 \%\) detection and \(0.5 \%\) false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the Editor for their valuable feedback. This work is supported in part by PR of China Ministry of Education Distinguished Possessor Grant given to Prof. Obaidat under number: MS2017BJKJ003. This work was also partially supported in part by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020; and in part by Brazilian National Council for Scientific and Technological Development (CNPq) via Grant No. 309335/2017-5.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248 002, India

    Sumit Pundir, Mohammad Wazid & Devesh Pratap Singh

  2. College of Computing and Informatics, University of Sharjah, UAE, with King Abdullah II School of Information Technology, The University of Jordan, Amman, 11942, Jordan

    Mohammad S. Obaidat

  3. University of Science and Technology Beijing, Beijing, 100083, China

    Mohammad S. Obaidat

  4. Center for Security, Theory and Algorithmic Research, International Institute of Information Technology, Hyderabad, 500 032, India

    Ashok Kumar Das

  5. Federal University of Piauí (UFPI), Teresina, 64049-550, PI, Brazil

    Joel J. P. C. Rodrigues

  6. Instituto de Telecomunicações, Covilhã, 6201-001, Portugal

    Joel J. P. C. Rodrigues

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  1. Sumit Pundir
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Correspondence to Joel J. P. C. Rodrigues.

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Pundir, S., Obaidat, M.S., Wazid, M. et al. MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach. Multimedia Systems 29, 1785–1797 (2023). https://doi.org/10.1007/s00530-020-00743-9

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