TIM-8315: v1

Kaloudi, N., & Jingyue Li. (2020). The AI-based cyber threat landscape: A survey. ACM Computing Surveys, 53(1), 1–34.
This journal article discusses how threat actors are constantly changing and improving their attack strategy with particular emphasis on the application of AI-driven techniques in the attack process, called AI-based cyber attacks, which can be used in conjunction with conventional attack techniques to cause greater damage. .

Ryu, S., Kim, J., Park, N., Seo, Y., & Mastorakis, N. (2021). Preemptive prediction-based automated cyberattack framework modeling. Symmetry (20738994), 13 (5), 793.
In this journal article, the authors describe a study involving the construction of a cyberattack framework based on preemptive prediction. In the study, the authors collect vulnerability information based on big data existing on the network and increase the accuracy by applying machine learning to the mapping of keywords frequently mentioned in attack strategies. An attack strategy prediction framework is proposed. .

Maymi, F., Bixler, R., Jones, R., & Lathrop, S. (2017). Towards a definition of cyberspace tactics, techniques and procedures. 2017 IEEE International Conference on Big Data (Big Data), Big Data (Big Data), 2017 IEEE International Conference On, 4674–4679.
Systems that use artificial intelligence (AI) and machine learning (ML) to address cybersecurity problems could better determine adversarial intent and future actions by connecting sequences of actions to threat actor intent. In this paper, the authors define TTPs to cybersecurity and present a model of TTPs for cyberspace operations that is useful to both humans and synthetic agents. .

Alrajhi, A. M. (2020). A survey of artificial intelligence techniques for cybersecurity improvement. International Journal of Cyber-Security and Digital Forensics, 9 (1), 34.
This paper describes an investigation into the development of cybersecurity tools, and several solutions have been proposed to protect computer systems from attacks. .

Bresniker, K., Gavrilovska, A., Holt, J., Milojicic, D., & Tran, T. (2019). Grand challenge: applying artificial intelligence and machine learning to cybersecurity. Computer (00189162), 52 (12), 45–52.
In this journal article, the authors explain how artificial intelligence (AI) and machine learning (ML) can help detect threats and provide recommendations to cyber analysts. Advancing the adoption of AI/ML applied to cybersecurity requires partnering of industry, academia, and government on a global scale.

Jaw, E., & Wang, X. (2021). Feature selection and ensemble-based intrusion detection system: an efficient and comprehensive approach. Symmetry (20738994), 13 (10), 1764.
Optional Resource: In this journal article, the author proposes a promising hybrid feature selection (HFS) system with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. .

Ali, S. (2019). Cybersecurity support of insider threat operations: DoD regulation and constitutional compliance. George Mason University Civil Rights Law Journal, 30 (1), 1–64.
Optional Resource: The article discusses the regulations about the use of artificial intelligence and machine learning to enhance cybersecurity of the U.S. Department of Defense's cyberspace network. Topics mentioned include the implementation of constitutional privacy and civil liberties, the nature of cyberspace, and the classification of technologies involved in detecting threats in cyberspace networks.

Whyte, C. (2020). Poison, persistence, and cascade effects: AI and cyber conflict. Strategic Studies Quarterly, 14 (4), 18–46.
Optional Resource: This journal article discusses the rise of AI-augmented cyber defenses incorporated into national defense postures that will likely be vulnerable to "poisoning" attacks that predict, manipulate, and subvert the functionality of defensive algorithms. .