Adaptive Deep Learning Framework for Zero-Day Attack Detection using Anomaly and Behavior Analysis

Abstract

Traditional signature-based intrusion detection systems (IDS) and rule-based security mechanisms frequently fail to detect these kinds of advanced and adaptive attacks because they rely on the patterns of attacks in the past. In addition, modern cyberattacks are very dynamic and polymorphic; traditional detection methods are not adequate for real-time cybersecurity protection. In this paper, an Adaptive Deep Learning Framework for Zero-Day Attack Detection Using Anomaly and Behavior Analysis is proposed to overcome the above limitations. The proposed scheme incorporates deep learning, anomaly detection, and behaviour analysis methods to detect novel cyber threats and malicious activities in real-time, intelligently. The framework constantly observes the traffic in the network, the pattern of user activity, system logs, and communication anomalies, and builds adaptive behavioral models that can differentiate between normal and malicious behavior. Advanced deep neural networks, Long Short-Term Memory (LSTM) architectures, and adaptive anomaly detection mechanisms are employed to analyze the temporal attack behavior and detect abnormal network activities with high accuracy. The findings support the adaptability, resilience, and efficiency of the proposed framework for intelligent zero-day attack detection and cybersecurity applications with deep learning.