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@article{204056,
        author = {Sidharth and Ms. Versha},
        title = {Intelligent Cyber Attack Identification Using Machine Learning Techniques : A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {638-645},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204056},
        abstract = {Fast-paced evolution of various technologies like digital communication, cloud computing, IoT, and others brings new cyber risks around the world. Various organizations working in different industries like banking, healthcare, education, defence, e-commerce, and governmental bodies face new cyber risks due to cyber-attacks trying to steal confidential data, damage network infrastructure, and access critical assets. Modern cyber-attacks in the international world cannot in any way be identified using traditional security measures such as firewalls and signature-based intrusion detection systems due to the constantly changing attack styles adopted by attackers as well as the use of zero-day vulnerabilities.
Machine Learning (ML) has proved to be an intelligent solution that helps detect abnormal behaviour, identify malicious patterns, and detect known and unknown cyber-attacks. In this paper, an approach to classify both benign and malicious activities on a network by using selection tree, random forest, help vector method, and K-nearest neighbour methods with the aid of some datasets from cybersecurity is proposed.
The research will provide detailed information about data preprocessing methods, features selection techniques, attack classification strategies, classifiers training procedures, and performance evaluation. Through experimentation, it will become evident that random forests perform better than other classifiers in terms of accuracy, precision, recall, and F1-scores.},
        keywords = {Cybersecurity, Machine Learning, Intrusion Detection System, Artificial Intelligence, Cyber Attack Detection, Random Forest, Network Security, Intelligent Systems},
        month = {June},
        }