Improving Data Protection in Industrial Control System Networks Using Machine Learning Technique

References

[1] Almalawi, A, Yu, X, and Tari Z, (2014), “An unsupervised anomaly-based detection approach for integrity attacks on SCADA” systems. ComputSecur 46:94–110

[2] Asogwa, T.C and Ituma, C. (2018); “real time facial recognition system for digital crime management” International Journal of Computer Applications (0975 – 8887) Volume 94; No 12. Computer science; Ebonyi State, University, Abakaliki, Nigeria

[3] Bailey D, and Wright E. (2013), Practical SCADA for Industry, IDC Technologies, 2013.

[4] Berge J., (2012), Fieldbuses for Process Control: Engineering, Operation, and Maintenance, ISA, 2012.IEEE Control Systems Magazine, http://www.ce.cmu.edu/~hsm/im2004/readings/CIIRinaldi.pdf

[5] Boyer, Stuart, S. (2017) “SCADA Supervisory Control and Data Acquisition”, 2nd Edition, ISA, 2017.

[6] Chukwuemeka O. A. (2016) “A model of hybrid agent software system for combating indigenous spam on GSM platform”; Ebonyi; International Journal of Computer Trend and Technology (IJCTT)

[7] Erez N, Wool A (2015) Control variable classification, modeling and anomaly detection in Modbus / TCP SCADA systems. Int J CritInfrastructProt 10:59–70.

[8] Erickson, Kelvin, and Hedrick, John, Plant Wide Process Control, Wiley & Sons, 2019.

[9] Falco, Joe, et al., IT Security for Industrial Control Systems, NIST IR 6859, 2013, http://www.isd.mel.nist.gov/documents/falco/ITSecurityProcess.pdf.

[10] Frazer, 2011 Process Measurement and Control – Introduction to Sensors, Communication Adjustment, and Control, Prentice-Hall, Inc., 2011.

[11] Hadziosmanovic D, Sommer, R, Zambon, E, andHartel P (2014) “Through the Eye of the PLC: Towards Semantic Security Monitoring for Industrial Control Systems,” ACSAC '14 Proceedings of the 30th Annual Computer Security Applications Conference

[12] Jiang, J, and Yasakethu, L (2013) Anomaly detection via one class SVM for protection of SCADA systems. Proc - 2013 IntConf Cyber-Enabled DistribComputKnowlDiscovCyberC 2013 82–88.

[13] Li, T., Fredrich, J., Fistruc, N. Pavefisic. and Y. Davies (2010) “The Complete Gabor-Fisher Classifier for Robust Face Recognition”. EURASIP Journal on Advances in Signal Processing, vol.2, article ID 847680.

[14] Lakhina, A., Crovella, M., and Diot, C., (2004). Characterization of network-wide anomalies in traffic flows. In Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, IMC ’04, pages 201–206, New York, NY, USA, 2004. ACM.

[15] Linda, O (2019) Neural Network based Intrusion Detection System for critical infrastructures. International Joint Conference on Neural Networks.

[16] Maglara LA, and Jiang J (2014) “Intrusion detection in SCADA systems using machine learning techniques”. 2014 Sci Inf Conf 626–631.

[17] Moya JM, Araujo Á, Banković Z, et al (2009) Improving security for SCADA sensor networks with reputation systems and self-organizing maps. Sensors 9:9380–9397.

[18] Nader, P (2013), “Intrusion detection in scada systems using one-class classification,” roc 21st European Signal Processing Conference (EUSIPCO 2013) 1–5

[19] Nader P, Honeine P, Beauseroy P (2014) lp-norms in One-Class Classification for Intrusion Detection in SCADA Systems. Proc IEEE Transactions on Industrial Informatics, 10:4.

[20] Peerenboom, James (2015), Infrastructure Interdependencies: Overview of Concepts and Terminology, Argonne National Laboratory, http://www.pnwer.org/pris/peerenboom_pdf.pdf.

[21] Rinaldi, et al., Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies,

[22] Rrushi, J, and Kang KD (2009), “Detecting anomalies in process control networks”. IFIP

[23] Russell, R, P., Sinha, B.and Y. Ostrovsky (2006), “Face Recognition by Humans”: Nineteen Results All Computer Vision Researchers Should Know About. Proceedings of the IEEE, 94(11)

[24] Yang, D, Usynin A, A, and Hines, J (2005),” Anomaly-based intrusion detection for SCADA systems”, 5th Intl Top Meet Nucl Plant Instrumentation, Control Hum Mach Interface Technol (NPIC&HMIT 05) 12–16

[25] Yasakethu SLP, Jiang J (2013) Intrusion Detection via Machine Learning for SCADA System Protection. 1st IntSymp ICS SCADA Cyber Secur Res 101–105

[26] Yong,zhoug (2013). Survey of techniques for face detection, recognition and feature extraction approaches”; International Journal of Multidisciplinary Educational Research; vol. (3), Nehal

[27] Yulmez, G. and kmen,G (2017), “Design and implementation of face recognition system using Face thermal detection hardware” ; 2nd International Conference on Advanced Signal, Image and Video Processing; Spain. 2(3) ; pp 555-677; 1277-2343.

[28] Zahraa, Q J (2016) “Design and Implementation of Real Time Face Recognition System (RTFRS)”; International Journal of Computer Applications (0975 – 8887) Volume 94 – No 12. Computer Engineering Department, College of Engineering, University of Baghdad Al-Jadriyah, Baghdad, Iraq

[29] Zhang, L., Yu, S., Wu, D., and Watters, P., (2011). “A Survey on Latest Botnet Attack and Defense,” Proc. of 10th Intl’ Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), IEEE, pp. 53-60, November 2011.

[30] Zeechan, M. (2009); “Face recognition using PCA and SVM"(2011); 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, (ASID 2009), pp. 97 – 101. Hong Kong, 20-22.

[31] Zhou, C, Huang, S, Xiong, N, et al (2015), “Design and Analysis of Multimodel- Based Anomaly Intrusion Detection Systems in Industrial Process Automation, IEEE Trans Syst Man, Cyber System 45:1345–1360.

[32] Zimmer C, Bhat B, and Mueller, F (2009), “Time-Based Intrusion Detection in Cyber-Physical Systems”. 30th IEEE Real-Time SystSymp 89–92

@article{1703291,
      author = {Jude Chinedu Akamadu, Prof. James Eke, Emetu Chukwuma Kalu},
      title = {Improving Data Protection in Industrial Control System Networks Using Machine Learning Technique},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {9},
      pages = {374-381},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703291.pdf},
      abstract = {This work was embarked on to combat the security vulnerabilities trending on the present-day Industrial Control Systems (ICS) Supervisory Control and Data Acquisition (SCADA) system infrastructure. This was observed after series of literatures were discussed and research gap was established. The study proposed to solve the problem using machine learning. Data of the ICS Denial of Service Attack was collected and then develop a neural network based algorithm with it to detect threat on ICS. The training was done using back propagation algorithm. The system was implemented using Mathlab and neural network toolbox. The model was simulated and the result showed good threat detection performance with regression value of 0.973 and detection response time of 12ms which is very good. The percentage improvement when compared with the characterized test bed is 13.3% which is very good.},
      keywords = {Data protection, control system networks, and machine learning},
      month = {March},
  }