Leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to detect Cyber Attacks: A Comparative Study

Md Nurul Islam; S M K Quadri

doi:10.36948/ijfmr.2025.v07i05.58758

Leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to detect Cyber Attacks: A Comparative Study

Author(s)	Dr. Md Nurul Islam, Prof. Dr. S M K Quadri
Country	India
Abstract	The increasing sophistication and complexity of cyberattacks have heightened their frequency, thereby amplifying the need for robust and effective mechanisms for detecting these threats. The study reviews the state-of-the-art of machine learning and artificial intelligence techniques for detecting cyber intrusions across the organizational networks and cloud environments. In this article we have explored supervised, unsupervised, and hybrid models of machine learning techniques. The technique used in this study includes classical algorithms such as Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting Machines (GBM), and Convolutional Neural Networks (CNNs) for malware/intrusion detection. And for anomaly detection we have used Recurrent Neural Networks (RNNs). The overall output is analyzed on the basis of the performance measured by the different algorithms over the recently available dataset on kaggle.com. The assessment criteria are based on the accuracy, precision, and recall analysis; F1 score evaluation; and confusion matrix generation, etc. of these algorithms. Hybrid and federated techniques have been used to improve the resilience to the most devastating attacks, often with success for attackers, i.e., zero-day attacks. The finding of this study provides cybersecurity teams valuable insight to take prompt decisions for the suitability of such ML and AI approaches for cyberattacks detection. It entails the cybersecurity professional and researchers making informed decisions to strengthen the organization’s security posture by designing and deploying effective defense mechanisms.
Keywords	Cybersecurity; Cyber-attack; Machine Learning (ML); Cyber threat; Anomaly detection
Field	Computer Applications
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-25
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58758

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Leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to detect Cyber Attacks: A Comparative Study

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