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1
Academic Journal

Alternate Title: Możliwości technologii elektrycznych w kontroli parametrów jakościowych materiałów światłoczułych. (Polish)

Superior Title: Przeglad Elektrotechniczny; 2024, Vol. 2024 Issue 3, p285-288, 4p

2
Dissertation/ Thesis

Contributors: Стадник, Марія Андріївна, Stadnyk, Mariia, Приймак, Микола Володимирович, Pryimak, Mykola, Тернопільський національний технічний університет імені Івана Пулюя

Subject Geographic: Тернопіль, UA

Relation: 1. Srilatha Chebrolua, Ajith Abrahama, Johnson P. Thomasa,. (2005). Feature deduction and ensemble design of intrusion detection systems. ELSEVIER, Pp. 295–307; 2. V. Jyothsna, V. V. Rama Prasad, K. Munivara Prasad. (2011). A Review of Anomaly based Intrusion Detection Systems. International Journal of Computer Applications, pp. 26-36; 3. M. Denis, C. Zena, and T. Hayajneh, “Penetration testing: Concepts, attack methods, and defense strategies,” 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), 2016; 4. Shirazi, H. M. (2009). ”Anomaly Intrusion Detection System using Information Theory, K-NN and KMC Algorithms. Australian Journal of Basic and Applied Sciences, pp. 2581-2597.; 5. Wang. K and Stolfo.S.J. (2004). Anomalous Payloadˇbased Network Intrusion Detection. 7th Symposium on Recent Advances in Intrusion Detection (pp. pp. 203–222). USA: LNCS Springer-Verlag.; 6. Brox, A. (2002, May 01st). THE CYBER SECURITY SOURCE. Retrieved December 20th, 2016, from SC Magazine US: https://www.scmagazine.com/ signature-based-or-anomaly-based-intrusionˇdetection-the-practice-and-pitfalls/article/548733; 7. Asmaa Shaker Ashoor, Prof. Sharad Gore. (2005). Importance of Intrusion Detection System (IDS). International Journal of Scientific Engineering Research, pp. 1-7; 8. Anomaly-based intrusion detection system. (2016, July 16th). Retrieved December 20th, 2016, from Wikipedia Encyclopedia: https://en.wikipedia.org/ wiki/Anomalybased_intrusion_detection_system; 9. Mark Handley, Vern Paxson and Christian Kreibich. (2001). Network Intrusion Detection: Evasion, Traffic Normalization, and End-to-End Protocol Semantics. Berkeley, CA 94704 USA: International Computer Science Institute.; 11. Leila Mohammadpour, Mehdi Hussain, Alihossein Aryanfar, Vahid Maleki Raee and Fahad Sattar. (2015). Evaluating Performance of Intrusion Detection System using Support Vector Machines: Review. International Journal of Security and Its Applications, pp.225-234; 12. Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid KPCA and SVM with GA model for intrusion detection. Applied Soft Computing, pp. 178-184; 13. The NSS Group. (2001, March 23rd). Intrusion Detection Systems Group Test (edition 2). Retrieved from NSS Group: http://www.nss.co.uk; 14. TESTIMON @ NTNU, Synthetic Financial Datasets for Fraud Detection, Kaggle, retrieved from https://www.kaggle.com/ntnu-testimon/paysim1; 15. Phua et.al., Minority Report in Fraud Detection: Classification of Skewed Data. ACM SIGKDD Explorations Newsletter 2004; 6: 50-59.; 16. Albashrawi et.al., Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015, Journal of Data Science 14(2016), 553-570; 17. Dharwa et.al., A Data Mining with Hybrid Approach Based Transaction Risk Score Generation Model (TRSGM) for Fraud Detection of Online Financial Transaction, International Journal of Computer Applications 2011; 16: 18-25.; 18. Sorournejad et.al., A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective, 2016; 19. Wedge et.al., Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering, Machine Learning and Knowledge Discovery in Databases, pp 372-388, 2018; 20. Albashrawi et.al., Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015, Journal of Data Science 14(2016), 553-570; http://elartu.tntu.edu.ua/handle/lib/38364