Akshaya Suresh

In an increasingly digital and interconnected world, the need for robust network intrusion detection systems is crucial to ensure cybersecurity. This article presents a novel approach to network intrusion detection that integrates both traditional machine learning methods and advanced reinforcement learning techniques to enhance detection capabilities and accuracy. The proposed system uses Proximal Policy Optimization, a reinforcement learning algorithm, to dynamically adjust ensemble weights, thereby optimizing the contributions of base learners, such as Random Forest and CatBoost. Additionally, a Multi-layer Perceptron-based meta-learner is employed to refine the predictions, leading to an overall improvement in detection performance. The model was evaluated on five diverse datasets, including NSL-KDD, CICIDS, TON IoT, DDoS, and UNSW-NB15, achieving an average accuracy of 97.16%, and an average precision, recall, and F1-score of 97% across all datasets. The proposed work is compared with the existing state-of-the-art detection methods demonstrating its better performance in detecting both known and novel attack types. Furthermore, the integration of reinforcement learning allowed for dynamic and context-sensitive decision-making, enabling the system to handle complex attack patterns that traditional models struggle with. The training and validation results across all datasets showed rapid convergence and minimal overfitting, further supporting the model’s robustness.

An AI-supported anomaly-based intrusion detection system (IDS) is a security technology that uses advanced machine learning algorithms to analyze network traffic in real-time and identify patterns that deviate from normal behavior. The system continuously monitors the network for any unusual or abnormal activities, and when it detects such patterns, it raises an alert. The AI-supported anomaly-based IDS uses various techniques such as supervised learning, unsupervised learning, and semi-supervised learning to perform anomaly detection. Once an alert is raised, the system can take appropriate actions to protect the network, such as blocking the suspicious traffic, isolating the affected system, or triggering an incident response plan. The AI-supported anomaly-based IDS can also provide detailed information on the nature and source of the attack, which can be used to improve the security of the network. In summary, AI-supported anomaly-based IDS is an advanced security technology that uses machine learning algorithms to detect unknown attacks by identifying patterns that deviate from normal behavior. It continuously monitors the network and raises an alert when it detects any abnormal activities and can take appropriate actions to protect the network.