Advanced Anomaly Detection and Adaptive Learning Systems for Cyber-Security

In the face of increasingly sophisticated cyber threats, traditional security measures are often insufficient for detecting and mitigating emerging attacks. This paper presents an advanced cybersecurity framework that integrates anomaly detection with adaptive learning mechanisms and robust encryption to address these challenges. The system is designed to leverage diverse datasets, including historical cyber threat data, to build detection models using supervised, unsupervised, and reinforcement learning algorithms, significantly improving accuracy and reliability across a variety of threat scenarios. At the core of the detection framework is a hybrid Long Short-Term Memory (LSTM) model, enhanced by the Grey Wolf Optimization (GWO) algorithm for feature selection. This approach enables the system to detect anomalies in network traffic, user behavior, and system activities with high precision, identifying both known and novel threats. The inclusion of adaptive learning techniques allows the system to continuously learn from past incidents, improving its detection capabilities and response strategies over time. Additionally, the system implements AES-256 encryption and strong access control mechanisms to ensure the confidentiality and integrity of data throughout its lifecycle, even in the event of a breach. the proposed GWO-LSTM model exhibits exceptional performance with an accuracy of 99.09%, precision of 99.14%, recall of 99.26%, and an F1-Score of 99.67%, highlighting its superior effectiveness in comparison to the other algorithms. This work contributes to the field of cybersecurity by offering a comprehensive solution that combines anomaly detection, adaptive learning, and robust encryption, providing a proactive defense against evolving cyber threats while securing sensitive information. The proposed framework is evaluated using benchmark datasets, demonstrating superior performance in terms of accuracy, precision, recall, and F1-score when compared to existing methods.