maxxmad

Context-Aware AI Firewall for Detection of Distributed Malware Chunks: Context-Aware AI Firewall: Detect fragmented / distributed malware Track packets across time (not just individually) Reconstruct suspicious payloads Use AI to classify malicious behavior Traditional firewalls: Work on static rules Inspect packets individually Do NOT track long-term context The proposed solution is an AI-powered, context-aware firewall that enhances traditional packet inspection by incorporating flow-based tracking and temporal analysis. Instead of analyzing packets in isolation, the system maintains a contextual buffer of packets belonging to the same network flow and reconstructs potential payloads by combining distributed data chunks. An AI model (such as LSTM or Transformer-based architecture) is trained to identify patterns of fragmented malware within these reconstructed sequences. When suspicious behavior or malicious patterns are detected, the firewall dynamically blocks the entire flow and prevents further communication from the source. This approach enables the detection of advanced and stealthy attacks, including zero-day threats, by leveraging sequence learning and contextual awareness, thereby significantly improving network security beyond conventional methods. Packet → Flow Tracking → Context Buffer → Reconstruct Data → AI Model → Decision → Block/Allow Packet Capture The system continuously monitors incoming network traffic. Each packet entering the network is captured. Flow Identification Each packet is assigned to a flow using: Source IP Destination IP Ports Protocol For every flow, maintain a temporary memory (buffer) Detect Suspicious Pattern Trigger The system checks: If enough packets are collected OR unusual pattern appears Payload Reconstruction Extract data from packets Combine them: AI Model Analysis The reconstructed data is sent to AI model If Benign: Allow packets Continue monitoring If Malicious: Block entire flow Drop all future packets Flag source as suspicious