Designing and implementing a Packet-Based Intelligent Network phishing Intrusion Detection system. The idea of the design is to use machine learning to classify Network packets to benign and phishing in real-time flow (for both http/https protocol) based on DNS records and domain name features. It operates by using a pre-programmed list of known phishing threat features and their indicators of compromise (IOCs). As a signature based INPDS it will monitor the packets traversing the network, it compares these packets to the database of known IOCs or attack signatures to flag any suspicious behavior.

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PhishLens is an academic project for automated phishing detection. It analyzes URLs and web content using threat intelligence, heuristics, metadata, and machine learning to identify malicious domains and protect users from phishing attacks.

A machine learning model trained to detect phishing websites using features like URL length, domain age, and site metadata. Phishing attacks trick users into revealing sensitive data. Designed for cybersecurity research and educational use, this project demonstrates ML’s power in threat detection.

The Safe Browsing Real-Time Phishing Detection System is a cybersecurity solution that detects malicious websites using machine learning and URL analysis. It evaluates features like HTTPS, domain age, and URL patterns, providing instant alerts to protect users from phishing attacks and ensure safe browsing.

Attackers use domain generation algorithms (DGA) to generate huge amounts of domains. These domains are then used to perform various malicious attacks including (but not limited to) C2 servers in malware attack, phishing domains, tech support scam websites. The domains are generated using various algorithms (phish kits) to evade detection from security software. In this hackathon project, we would like to create a working machine learning based classifier which will detect domains generated using DGAs. This can protect users from being compromised.

AI-driven phishing detection system that protects users from modern web-based threats in real time. It combines fast URL analysis using machine learning with domain intelligence and deep content inspection to detect zero-day phishing attacks. The system generates reports and supports browser-level protection with enterprise-ready architecture.