Zero-Day Exploit Detection: A Behavioral Analysis Approach

Traditional signature-based antivirus and intrusion detection systems are fundamentally reactive, proving ineffective against zero-day exploits for which no signature yet exists. This research addresses this critical gap by proposing a proactive detection framework based on behavioral anomaly analysis. Our model does not rely on prior knowledge of attack vectors. Instead, it establishes a baseline of normal system behavior by monitoring a high-dimensional feature set, including system call frequencies, network traffic patterns, and memory allocation changes. We employ an unsupervised machine learning model, specifically an isolation forest combined with a Long Short-Term Memory (LSTM) autoencoder, to identify subtle deviations from this baseline that are indicative of malicious activity. The system was trained on a large dataset of benign software behavior and subsequently tested in a high-fidelity sandbox environment against a suite of known and novel exploit kits. Our framework demonstrated a 98.2% detection rate for zero-day-style attacks while maintaining a low false positive rate of 1.5%, offering a resilient and adaptive defense mechanism for modern cybersecurity challenges.