Our AI model (99.88% accuracy) detects malicious network traffic in real time and analyzes PCAP files by processing key packet features. Its intuitive interface shows predictions and confidence for pr
CyberShield AI – Real-Time Packet Sniffer & Threat Detection
Overview
CyberShield AI is an advanced real-time network packet sniffer designed to detect cybersecurity threats using a trained AI model. This system analyzes live network traffic, extracts meaningful features, and classifies packets as either benign or malicious. With a detection accuracy of *99.88%, it provides a robust defense against cyber threats such as DDoS attacks, port scanning, malware, and SQL injection attempts.
Key Features
Real-Time Network Monitoring
The system actively captures packets from a Wi-Fi interface, allowing users to monitor their network traffic continuously. Every captured packet is analyzed on the fly, ensuring *instant threat detection* without delays.
AI-Based Cyber Threat Detection
Using a trained AI model, CyberShield AI identifies different types of cyber threats. The model classifies packets based on extracted features such as *protocol type, packet size, source/destination ports, and TTL values. The system can differentiate between normal network activity and potential attacks with high accuracy.
Graphical User Interface (GUI)
A user-friendly interface allows users to view detected threats, confidence scores, and packet details. A dynamic confidence meter visualizes the likelihood of an attack, making it easy to interpret real-time predictions.
PCAP File Analysis
Users can analyze previously recorded network traffic by importing PCAP files. This feature enables forensic analysis of past incidents, helping security professionals detect and understand threats that may have gone unnoticed.
Minimal Resource Usage
CyberShield AI is optimized for speed and efficiency, ensuring *low latency* while sniffing network packets. It processes traffic without slowing down the network or system performance.
How It Works
1. Captures Network Packets – The system detects incoming and outgoing network traffic from a Wi-Fi interface.
2. Extracts Features – It gathers critical information such as packet size, protocol type, source/destination details, and header values.
3. AI-Based Classification – The extracted data is passed to a trained AI model, which classifies the packet as either benign or a specific type of attack.
4. Visualization & Alerts – The results are displayed in an interactive GUI, highlighting malicious packets and their confidence scores.
Performance & Accuracy
- 99.88% accuracy in detecting cyber threats.
- Real-time packet processing ensures instant alerts for suspicious activity.
- Graphical confidence meter provides visual insights into threat detection accuracy.
Future Enhancements
- Cloud integration for remote threat detection.
- Automated alerting system for high-risk threats.
- Advanced deep learning models for even better classification accuracy.
Conclusion
CyberShield AI is a powerful cybersecurity tool that enables real-time network monitoring and threat detection using AI. Its high accuracy, ease of use, and minimal resource consumption make it ideal for cybersecurity professionals, network administrators, and ethical hackers.
By leveraging AI-driven insights, CyberShield AI helps *strengthen network security and prevent cyber attacks* before they escalate. 🚀
Progress During DevSummit2025 Hackathon – CyberShield AI Day 1: Ideation, Model Training & Initial Implementation - Began with brainstorming ideas and finalized the concept of CyberShield AI, an AI-powered **real-time network packet sniffer* for *cyber threat detection. - Defined key features: live packet sniffing, AI-based classification, GUI for visualization, and PCAP file analysis. - Gathered and preprocessed a network security dataset, focusing on threats like DDoS, port scans, malware, and SQL injection attacks. - Trained a high-accuracy AI model (99.88%) using LightGBM, ensuring fast and precise packet classification. - Saved the trained model (lightgbm_model.pkl) and verified its performance on test data. - Started implementing *packet sniffing functionality* using Scapy for real-time traffic capture. - Developed a basic feature extraction module to analyze network packets. - Successfully classified the first batch of captured packets using the AI model. Day 2: Real-Time Monitoring, GUI Development & Final Testing - Built a user-friendly GUI using Tkinter to display live packet data and AI predictions. - Integrated a Treeview table for real-time packet classification results and Matplotlib graphs for confidence visualization. - Added PCAP file analysis, allowing users to scan saved network traffic for threats. - Optimized sniffing for Wi-Fi interfaces, ensuring smooth packet capture across different devices. - Conducted extensive testing, refining the feature extraction and model integration. - Successfully demonstrated real-time threat detection, with packets classified instantly upon capture. - Finalized and submitted the project with documentation, ensuring clarity for future improvements. Outcome CyberShield AI was fully functional by the end of the hackathon, achieving real-time cyber threat detection with high accuracy and a responsive user interface. The project showcased how AI can enhance network security by providing instant insights into potential threats. 🚀🔐
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DevSummit 2025