International Journal of Scientific Research and Engineering Development

The high proliferation of networked systems has contributed to high levels of cyberattacks and intrusion detection systems (IDS) has become a key element of the contemporary cybersecurity framework. The classic signature-based IDS methods are not sufficient to counter the developing and the zero-day attacks, and the use of intelligent machine learning techniques is adopted. This paper will provide an in-depth comparative analysis of supervised learning and reinforcement learning methods of intrusion detection in cybersecurity systems. Popular supervised classifiers (Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), and Deep Neural Networks (DNN)) are tested in comparison with reinforcement learning models, including Q-Learning and Deep Q-Networks (DQN), on a benchmark intrusion detection dataset. The experimental findings support the idea that the application of supervised learning models has the high performance in terms of detection in the stationary environment with Random Forest and DNN giving the performance at 96.8 and 97.5 percent, respectively, and F1-score of over 0.96. They however perform poorly when subjected to new patterns of attack. Conversely, reinforcement learning models are more adaptable with the DQN-based IDS showing 94.2 detection accuracy, false positive rate of 3.1, and stability in detecting attacks over time in dynamic attack environments. Even though reinforcement learning models take more time to train, they are more resilient to emerging threats. The comparative analysis reveals the trade-offs between accuracy and adaptability and provides an idea that hybrid IDS frameworks combining supervised and reinforcement learning methods can provide better cybersecurity resilience.

Moni Gautam, Gaurav Goel, Preeti Verma,"A Comparative Evaluation of Supervised and Reinforcement Learning Techniques for Intrusion Detection in Cybersecurity Systems" International Journal of Scientific Research and Engineering Development, V9(3): Page(34-49) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.