AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Effective Malware Detection Approach Based on Deep Learning in Cyber-Physical Systems


Affiliations
1 Doctor of Philosophy, Information Technology, University of the Cumberlands, United States
 

Cyber-physical Systems based on advanced networks interact with other networks through wireless communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of privacy and security as third-party users should authenticate the network. If it fails, it will create extensive damage and threat to the established network and makes the hacker malfunction the network services efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyber-physical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps to improve the privacy and authentication of the network by improving the data accuracy and Qua lity of Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the network reliability and security compared to some of the existing SVM-based and Apriori-based detection approaches.

Keywords

Cyber-Physical System, Security, Deep learning, DoS, DDoS, Authentication.
User
Notifications
Font Size

  • Mitchell, R.,&Chen, I., (2014). A survey of intrusion detection techniques for cyber-physical systems. ACM Computing Surveys, 6(4), 1-26. https://doi.org/10.1145/2542049.

  • Zhao, L.,& Long, W., (2020). Co-Design of Dual Security Control and Communication for Nonlinear CPS UnderDoS Attack. IEEE Access, 8, 19271 – 19285.DOI: 10.1109/ACCESS.2020.2966281.

  • Lin, H., Hu, J., Ma, J., Xu, L., &Yu, Z., (2017). A Secure Collaborative Spectrum Sensing Strategy in Cyber-Physical Systems. IEEE Access. 5, 27679 – 27690.DOI: 10.1109/ACCESS.2017.2767701.

  • Isakovic, H.,Ratasich, D., Hirsch, C.,Platzer, M., Wally, B., Rausch, T.,Nickovic, D.,Krenn, W., Kappel, G.,Dustdar, S.,&Grosu, R., (2018). CPS/IoT Ecosystem: A Platform for Research and Education. In Proceedings of International Workshop on Design, Modeling, and Evaluation of Cyber -Physical Systems Workshop on Embedded Systems and Cyber-Physical Systems Education CyPhy (pp. 206-213). DOI: 20.500.12708/57632.

  • Wang, L., Qu, Z., Li, Y., Hu, K., Sun, J.,Xue, K., &Cu, M., (2020). Method for Extracting Patterns of Coordinated Network Attacks on Electric Power CPS Based on Temporal–Topological Correlation. IEEE Access, 8, 57260 – 57272. DOI: 10.1109/ACCESS.2020.2982057.

  • Pan, F., Pang,Z., Wen, H.,Luvisotto, M., Xiao, M., Liao, R., & Chen. J., (2019). Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS. IEEE Transactions on Industrial Informatics, 15(12), 6481 – 6491. DOI: 10.1109/TII.2019.2925418.

  • Cheng, B., Zhang, J.,Hancke, G. P., Karnouskos, S.,& Colombo, A. W., (2018). Industrial Cyberphysical Systems: Realizing Cloud-Based Big Data Infrastructures. IEEE Industrial Electronics Magazine, 12(1), 25 – 35. DOI: 10.1109/MIE.2017.2788850.

  • Li, L., (2018). China's manufacturing locus in 2025: With a comparison of "Made-in-China 2025" and "Industry 4.0. Technological Forecasting and Social Change, Elsevier, 135, 66 – 74. https://doi.org/10.1016/j.techfore.2017.05.028.

  • Bagula, A., Ajayi, O.,&Maluleke, H., (2021). Cyber-Physical Systems Dependability Using CPS-IoT Monitoring. Sensors, 21(8). DOI: 10.3390/s21082761.

  • Oyekanlu, E., (2018). Fault-Tolerant Real-Time Collaborative Network Edge Analytics for Industrial IoT and Cyber-Physical Systems with Communication Network Diversity. In Proceedings of IEEE 4th International Conference on Collaboration and Internet Computing (CIC).DOI: 10.1109/CIC.2018.00052.

  • Gressl, L.,Krisper, M., Steger, C.,&Neffe, U., (2020). Towards an Automated Exploration of Secure IoT/CPS Design-Variants. In Proceedings of International Conference on Computer Safety, Reliability, and Security SAFECOMP 2020: Computer Safety, Reliability, and Security, 372 – 386. DOI:10.1007/978-3-030-54549-9_25.

  • Tu, M., Lim,M. K. &Yang, M., (2018). IoT-based production logistics and supply chain system – Part 2: IoT-based cyber-physical system: a framework and evaluation. Industrial Management & Data Systems, Industrial Management & Data Systems, 118(1), 96-125. https://doi.org/10.1108/IMDS-11-2016-0504

  • Aslahi-Shahri,B.M.,(2016). A hybrid method consisting of GA and SVM for the intrusion detection system. Neural ComputAppl, 27, 1669-1676. https://doi.org/10.1007/s00521-015-1964-2.

  • Sun, S., Ye, Z., Yan, L., Su, J., &Wang, R., (2018). Wrapper feature selection based on lightning attachment procedure optimization and support vector machine for intrusion detection. In Proceedings of the IEEE 4th international symposium on wireless systems within the international conferences on intelligent data acquisition and advanced computing systems, IDAACS-SWS (pp. 41– 6). DOI: 10.1109/IDAACS-SWS.2018.8525742.

  • Raza, S., Wang, S., Ahmed, M., &Rizwan Anwar, M., (2019). A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions, Hindawi Wireless Communications and Mobile Computing, 3159762. https://doi.org/10.1155/2019/3159762.

  • El-Naqa, I., Yang, Y.,Wernick, M.N.,Galatsanos, N.P., & Nishikawa, R.M., (2002). A Support Vector Machine Approach for Detection of Micro calcifications. IEEE Transactions on Medical Imaging. 21(12), 1552 – 1563.DOI: 10.1109/TMI.2002.806569.

  • Li, S., (2018). A Multiple Linear Regression Approach For Estimating the Market Value of Football Players in Forwarding Position. The Frontiers of Society, Science and Technology. 2(15). 132-143. DOI: 10.25236/FSST.2020.021516.

  • Goldstein,B. A., Hubbard, A.N., Cutler, A., &Barcellos, L.F.,(2010). An Application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings. BMC Genetics.DOI: 10.1186/1471-2156-11-49.

  • Li, L.,Ota, K., &Dong, M., (2017). When Weather Matters: IoT-Based Electrical Load Forecasting for Smart Grid. IEEE Communications Magazine. 55(10). 46-51. DOI: 10.1109/MCOM.2017.1700168.

  • Yi, S., Hao, Z., Qin, Z., &Li, Q., (2015). Fog computing: Platform and applications. In Proceedings 3rd IEEE Workshop Hot Topics Web Syst. Technol. (HotWeb), (pp. 73–78).DOI: 10.1109/HotWeb.2015.22.

  • Jagadish, H.V.,Ooi, B.C., Tan, K.-L., Yu, C., &Zhang, R., (2005). I distance an adaptive bþ-tree based indexing method for nearest neighbour, ACM Trans. Database Syst. (TODS), 30 (2). 364–397. https://doi.org/10.1145/1071610.1071612.

  • Mohammadi,M., Al-Fuqaha, A.,Sorour,S., &Guizani, M., (2018). Deep Learning for IoT Big Data and Streaming Analytics- A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960. DOI: 10.1109/COMST.2018.2844341.

  • Ananda M. Ghosh&Grolinger, K., (2019). Deep Learning: Edge-Cloud Data Analytics for IoT, IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). DOI: 10.1109/CCECE.2019.8861806.

  • Li, L.,Ota, K., &Dong, M., (2018). Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing, IEEE Network, 32(1): 96-101.DOI: 10.1109/MNET.2018.1700202.

  • Mavrogiorgou, A.,Kiourtis, A., &Kyriazis, D., (2017). A Comparative Study of Classification Techniques for Managing IoT Devices of Common Specifications, In Proceedings of International Conference on the Economics of Grids, Clouds, Systems, and Services, (pp. 67-77). https://doi.org/10.1007/978-3-319-68066-8_6.

  • Amanullah, M.A., Habeeb, R.A.A.,Nasaruddin, F.H.,Gani, F., Ahmed, E., Nainar, A.S.M., Akim, N.M., & Imran, M., (2020). Deep learning and big data technologies for IoT security, Computer Communications, 151, 495-517. https://doi.org/10.1016/j.comcom.2020.01.016.

  • Pramanik, P.K.D., &Choudhury, P., (2018). IoT Data Processing: The Different Archetypes and Their Security and Privacy Assessment, Internet of Things Security.DOI:10.1201/9781003338642-3.

  • Sharma, P., Dash, B., & Ansari, M. F. (2022). Anti-phishing techniques – a review of Cyber Defense Mechanisms. IJARCCE, 11(7). https://doi.org/10.17148/ijarcce.2022.11728

  • Ansari, M. F., Sharma, P. K., & Dash, B. (2022). Prevention of phishing attacks using AI-based Cybersecurity Awareness Training. International Journal of Smart Sensor and Adhoc Network., 61– 72.https://doi.org/10.47893/ijssan.2022.1221

  • Ansari, M. F., & Dash, B. (2022). Self-service analytics for data-driven decision making during COVID-19 pandemic: An organization's best defense. Academia Letters. https://doi.org/10.20935/al4978


Abstract Views: 151

PDF Views: 85




  • Effective Malware Detection Approach Based on Deep Learning in Cyber-Physical Systems

Abstract Views: 151  |  PDF Views: 85

Authors

Srinivas Aditya Vaddadi
Doctor of Philosophy, Information Technology, University of the Cumberlands, United States
Pandu Ranga Rao Arnepalli
Doctor of Philosophy, Information Technology, University of the Cumberlands, United States
Ramya Thatikonda
Doctor of Philosophy, Information Technology, University of the Cumberlands, United States
Adithya Padthe
Doctor of Philosophy, Information Technology, University of the Cumberlands, United States

Abstract


Cyber-physical Systems based on advanced networks interact with other networks through wireless communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of privacy and security as third-party users should authenticate the network. If it fails, it will create extensive damage and threat to the established network and makes the hacker malfunction the network services efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyber-physical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps to improve the privacy and authentication of the network by improving the data accuracy and Qua lity of Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the network reliability and security compared to some of the existing SVM-based and Apriori-based detection approaches.

Keywords


Cyber-Physical System, Security, Deep learning, DoS, DDoS, Authentication.

References