Submit your paper : editorIJETjournal@gmail.com Paper Title : Cyber Attacks Detection and Attribution in Iot-Based Cyber Physical Systems ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.7239134 MLA Style: -Dr.P.Shanmuga priya, A.Vasavi, G.SriHarshini, G.Mahalakshmi Cyber Attacks Detection and Attribution in Iot-Based Cyber Physical Systems , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -Dr.P.Shanmuga priya, A.Vasavi, G.SriHarshini, G.Mahalakshmi Cyber Attacks Detection and Attribution in Iot-Based Cyber Physical Systems , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract Recent scenario says that there are many challenges for Internet of Things (IoT)-enabled cyber-physical systems (CPS) related to Industry 4.0 such as data protection and data security, lack of benefit quantification and prioritization by top management and so on. Thus, this paper presents a way to identify attack detection and attribution framework w h i c h i s designed for CPS, and more specifically in an industrial control system (ICS). It has a two-step ensemble attack detection and attribution framework. At the first step, a decision tree is used to differentiate the attacked and un-attacked data from the dataset. At the second step, using Deep Neural Network (DNN) models the accurate attack type in CPS is predicted. Reference 1.K. Graves, Ceh: Official certified ethical hacker review guide: Exam 312-50. John Wiley & Sons, 2007. 2.R. Christopher, “Port scanning techniques and the defense against them,” SANS Institute, 2001. 3.. S. Stanford, J. A. Hoagland, and J. M. McAlerney, “Practical automated detection of stealthy portscans,” Journal of Computer Security, vol. 10, no. 1-2, pp. 105–136, 2002. 4. S. Robertson, E. V. Siegel, M. Miller, and S. J. Stolfo, “Surveillance detection in high bandwidth environments,” in DARPA Information Survivability Conference and Exposition, 2003. Proceedings, vol. 1. IEEE, 2003, pp. 130–138. 5.K. Ibrahimi and M. Ouaddane, “Management of intrusion detection systems based-kdd99: Analysis with lda and pca,” in Wireless Networks and Mobile Communications (WINCOM), 2017 International Conference on. IEEE, 2017, pp. 1–6. 6.N. Moustafa and J. Slay, “The significant features of the unsw-nb15 and the kdd99 data sets for network intrusion detection systems,” in Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), 2015 4th International Workshop on. IEEE, 2015, pp. 25–31. 7.L. Sun, T. Anthony, H. Z. Xia, J. Chen, X. Huang, and Y. Zhang, “Detection and classification of malicious patterns in network traffic using benford’s law,” in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017. IEEE, 2017, pp. 864–872. 8.S. M. Almansob and S. S. Lomte, “Addressing challenges for intrusion detection system using naive bayes and pca algorithm,” in Convergence in Technology (I2CT), 2017 2nd International Conference for. IEEE, 2017, pp. 565–568. 9. M. C. Raja and M. M. A. Rabbani, “Combined analysis of support vector machine and principle component analysis for ids,” in IEEE International Conference on Communication and Electronics Systems, 2016, pp. 1–5. 10.1.S. Aljawarneh, M. Aldwairi, and M. B. Yassein, “Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model,” Journal of Computational Science, vol. 25, pp. 152–160, 2018. 11.I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization.” in ICISSP, 2018, pp. 108– 116. 12.D. Aksu, S. Ustebay, M. A. Aydin, and T. Atmaca, “Intrusion detection with comparative analysis of supervised Keywords — Cyber Attacks Detection and Attribution in Iot-Based Cyber Physical Systems |