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AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions
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Abstract
Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today’s various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on “AI-driven Cybersecurity” that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper’s ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
(1996) · Cited by 20,179
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Research works printed but not linked: 18
- Alsayed A, Bilgrami A. E-banking security: internet hacking, phishing attacks, analysis and prevention of fraudulent activities. Int J Emerg Technol Adv Act. 2017;7(1):109–15.
- Eggers S. A novel approach for analyzing the nuclear supply chain cyber-attack surface. Nucl Eng Technol. 2021;53(3):879–887 · doi:10.1016/j.net.2020.08.021
- Hong S. Survey on analysis and countermeasure for hacking attacks to cryptocurrency exchange. J Korea Converg Soc. 2019;10(10):1–6.
- Shankar VG, Jangid M, Devi B, Kabra S. Mobile big data: malware and its analysis. In: Proceedings of first international conference on smart system, innovations and computing. Springer; 2018. p. 831–842. · doi:10.1007/978-981-10-5828-8_79
- Xue Y, Meng G, Liu Y, Tan TH, Chen H, Sun J, Zhang J. Auditing anti-malware tools by evolving android malware and dynamic loading technique. IEEE Trans Inf Forensics Secur. 2017;12(7):1529–44. · doi:10.1109/tifs.2017.2661723
- Irfan M, Abbas H, Sun Y, Sajid A, Pasha M. A framework for cloud forensics evidence collection and analysis using security information and event management. Secur Commun Netw. 2016;9(16):3790–807. · doi:10.1002/sec.1538
- Ragsdale DJ, Carver CA, Humphries JW, Pooch UW. Adaptation techniques for intrusion detection and intrusion response systems. In: Smc 2000 conference proceedings. 2000 IEEE international conference on systems, man and cybernetics.’cybernetics evolving to systems, humans, organizations, and their complex interactions’(cat. no. 0) vol. 4. IEEE; 2000. p. 2344–2349.
- Microsoft malware classification (big 2015). arXiv:1802.10135. Accessed 20 Oct 2019. · arXiv
- Sharifi AM, Amirgholipour SK, Pourebrahimi A. Intrusion detection based on joint of k-means and knn. J Converg Inf Technol. 2015;10(5):42.
- Dada EG. A hybridized svm-knn-pdapso approach to intrusion detection system. In: Proceedings of Facility Seminar Ser. 2017. p. 14–21.
- Rai K, Syamala Devi M, Guleria A. Decision tree based algorithm for intrusion detection. Int J Adv Netw Appl. 2016;7(4):2828.
- Balogun AO, Jimoh RG. Anomaly intrusion detection using an hybrid of decision tree and k-nearest neighbor. In: A Multidisciplinary Journal Publication of the Faculty of Science, Adeleke University, Ede, Nigeria, 2015; vol 2.
- MacQueen J. Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley symposium on mathematical statistics and probability, vol. 1. 1967.
- Sorensen T. Method of establishing groups of equal amplitude in plant sociology based on similarity of species. Biol Skr. 1948;5:1–34.
- Kidmose E, Stevanovic M, Pedersen JM. Detection of malicious domains through lexical analysis. In: 2018 international conference on cyber security and protection of digital services (cyber security). IEEE; 2018. p. 1–5. · doi:10.1109/cybersecpods.2018.8560665
- L’Huillier G, Hevia A, Weber R, Rios S. Latent semantic analysis and keyword extraction for phishing classification. In: 2010 IEEE international conference on intelligence and security informatics. IEEE; 2010. p. 129–131. · doi:10.1109/isi.2010.5484762
- Pereira T, Santos H. An ontology based approach to information security. In: Research conference on metadata and semantic research. Springer; 2009. p. 183–192. · doi:10.1007/978-3-642-04590-5_17
- Zadeh LA. Fuzzy logic—a personal perspective. Fuzzy Sets Syst. 2015;281:4–20. · doi:10.1016/j.fss.2015.05.009
Cited by
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
“Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [95].”
Self-citationSarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00815-1 · Cited by 2,678
Self-citationSarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
Self-citationSarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570
Self-citationExplainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450
Metadata
- Authors
- Sarker, I. H., Furhad, M. H., & Nowrozy, R.
- Venue
- SN Computer Science
- Published
- 2021-03-26
- Volume
- 2
- Issue
- 3
- ISSN
- 2661-8907 (ISSN-L)
- 2662-995X
- OpenAlex
- W3149084432
- Cited by
- 543
- References
- 160
- FWCI
- 38.55
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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