TIM-8315: v1

Harel, Y., Gal, I. B., & Elovici, Y. (2017). Cyber Security and the Role of Intelligent Systems in Addressing its Challenges. ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Cyber Security and Regular Papers, 8(4), 1–12.
This journal article explains the role of systems, network dynamics, human behavior, and points of vulnerability in the organization's infrastructure and the implications of each in implementing an intelligent cybersecurity system.

Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., Chen, S., Liu, D., & Li, J. (2020). Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity. Energies (19961073), 13 (10), 2509.
This journal article provides a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks.

Rabbani, M., Wang, Y., Khoshkangini, R., Jelodar, H., Zhao, R., Bagheri Baba Ahmadi, S., Ayobi, S., Rosnes, E., i Amat, A. G., & Lin, H.-Y. (2021). A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies. Entropy, 23 (5), 529.
This journal article describes how network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities.

Miao, Y., Chen, C, Pan. L., Han, Q., Zhang, J., & Xiang, Y. (2021). Machine learning-based cyber attacks targeting on controlled information: A survey. ACM Computing Surveys, 54 (7), 1–36.
Optional Resource: This journal article explores the overarching attack methodology against controlled information and an ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information.

Al-Utaibi, K. A., El-Alfy, E.-S. M., Thampi, El-Alfy, Mitra, & Trajkovic. (2018). Intrusion detection taxonomy and data preprocessing mechanisms. Journal of Intelligent & Fuzzy Systems, 34 (3), 1369–1383.
Optional Resource: This journal article presents an extended taxonomy of the work related to intrusion detection and reviews the state-of-the-art techniques for data preprocessing.

Kumar, G., Thakur, K., & Ayyagari, M. R. (2020). MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review. Journal of Supercomputing, 76 (11), 8938–8971.
Optional Resource: This journal article provides a review of a wide variety of machine learning techniques that have been designed and integrated with IDS. Specifically, improvements to the approach using different ensemble methods in the field are analyzed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier.

Albulayhi, K., Smadi, A. A., Sheldon, F. T., & Abercrombie, R. K. (2021). IoT intrusion detection taxonomy, reference architecture, and analyses. Sensors (Basel, Switzerland), 21 (19).
This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in the Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and neutral reference architecture. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN).