Authors: Tinatin Mshvidobadze
Affiliation: Gori State University


Keywords: cyber incidents, cyber security, data mining, machine learning
ABSTRACT. The paper presents methods related to cyber incidents by various researchers. Machine learning algorithms (DM-ML) play an important role in the prediction and detection of cyber incidents (SCI) in the field of cyber security. The paper presents well-known ML classifiers for data classification. The data set is taken from a report by the Center for Strategic and International Studies (CSIS). A centralized classifier approach based on data from six continents of the world is discussed. Based on the comparison of classifiers in the paper, it is predicted with high accuracy which type of SCI may occur and in which part of the world.


1. Li Y., and Liu Q., 2021, “A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments,” Energy Reports, vol. 7, pp. 8176–8186;
2. Hejase H., Kazan H., Hejase A, and Moukadem I, 2021, “Hejase et al. Cyber Security paper,” Computer and Information Science, vol. Vol. 14, pp. 10–25, doi: 10.5539/cis. v14n2p10;
3. Hodgson Q,, Clark-Ginsberg A., Haldeman Z., Lauland, A and Mitch I.,2022, Managing Response to Significant Cyber Incidents: Comparing Event Life Cycles and Incident Response Across Cyber and Non Cyber Events. Santa Monica, CA: RAND Corporation, doi: 10.7249/RRA1265-4;
4. Handa A., Sharma A., and Shukla S., 2019, “Machine learning in cybersecurity: A review,” WIREs Data Mining and Knowledge Discovery, vol. 9, no. 4, p. e1306, doi: 10.1002/widm.1306;
5. Mumtaz G., Akram S., Waseem M., Iqbal M., Ashraf U., Almarhabi K., Mohammed A., and Adel A., 2017, “Classification and Prediction of Significant Cyber Incidents (SCI) using Data Mining and Machine Learning (DM-ML)“.
6. Alqahtani H,. Sarker I., Kalim, A., Minhaz Hossain M., Ikhlaq S., and Hossain 2020, “Cyber Intrusion Detection Using Machine Learning Classification Techniques,” in Computing Science, Communication and Security, Singapore, pp. 121–131.
7. Bhusal N., Gautam M., and Benidris M., 2021, “Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning,” IEEE Access, vol. 9, pp. 40402–40416, doi: 10.1109/ACCESS.2021.3064689
8. Bapat R., et al., 2018, “Identifying malicious botnet traffic using logistic regression,” in Systems and Information Engineering Design Symposium (SIEDS), pp. 266–271. doi: 10.1109/SIEDS.2018.8374749;
9. Ustebay S., Turgut Z., and Aydin M., “Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier,” in 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Dec. 2018, pp. 71–76. doi: 10.1109/IBIGDELFT.2018.8625318.;
10. Chayal N., and Patel N., 2021, “Review of Machine Learning and Data Mining Methods to Predict Different Cyberattacks,” in Data Science and Intelligent Applications, Singapore, pp. 43–51;
11. Biswas B., Mukhopadhyay A., Bhattacharjee S., Kumar A., and Delen D., 2022, “A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums,” Decision Support Systems, vol. 152, p. 113651, doi: 10.1016/j.dss.2021.113651;
12. Souri A., and Hosseini R., 2018, “A state-of-the-art survey of malware detection approaches using data mining techniques,” Hu-man-centric Computing and Information Sciences, vol. 8, no. 1, p. 3, doi: 10.1186/s13673-018-0125-x;
13.Fang X., Xu M., and Zhao P., 2019, “A deep learning framework for predicting cyber-attacks rates,” EURASIP Journal on Information Security, doi: 10.1186/s13635-019-0090-6;
14. “Significant Cyber Incidents (SCIs).” [Online]. Available:;
15. Xu S., 2018, “Bayesian Naïve Bayes classifiers to text classification,” J. Inf. Sci., vol. 44, no. 1, pp. 48–59, doi: 10.1177/0165551516677946.