ATTENTION-AUGMENTED DEEP SEQUENTIAL LEARNING FRAMEWORK FOR ADAPTIVE INTRUSION DETECTION

Abstract

The paper presents a new deep learning framework for adaptive intrusion detection that combines attention mechanisms with sequential modeling. The proposed system treats network events as time-based sequences, leveraging recurrent neural networks to capture gradually the complex behavioral patterns during the attack. The most important innovation is an attention layer that shows dynamically the most important and anomalous events in the sequence which substantially boosts the detection of sophisticated and multi-stage attacks. The accuracy and interpretability of the model are both enhanced by this emphasis. In addition, the framework is made up of continuous adaptation, utilizing online learning methods to update its database of threats gradually with new threat data without very costly full retraining. The model's performance on real-world network traffic benchmarks has been evaluated and it has been found to outperform existing methods, achieving higher detection rates for both known and novel attacks while keeping the false positive rate low. The findings suggest a solid and smart answer to the ever-changing problems of cybersecurity.