advanced AI-powered phishing detection system designed specifically to protect Nigerian internet users from cyber threats, phishing attacks, and online fraud. The platform uses machine learning algorithms to analyze websites and provide real-time security assessments.

Advancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.

AI-powered Cybersecurity Threat Detection System using Machine Learning and Security Logs. Detects malware, phishing attacks, and network intrusions using Python, TensorFlow, and SIEM logs.

Real-time Phishing Attack Detection using Machine Learning 💻

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.

Due to the growing trend of the Internet, the number of computers connected to the Internet is increasing day by day. Almost all companies are moving their main activities from the real world to the cyber world, although that issue increases their sales, it can lead to many vulnerabilities such as cyber-attacks for these companies, especially with the anonymous structure of the Internet. Phishing is one of the popular types of cyber attacks that use the user's ignorance to harm them. In some studies, law-based detection systems are used as a static prevention mechanism and machine learning-based systems as a prevention mechanism. Poya gets help

PhishTackle-API is a powerful machine learning-driven API designed to combat phishing attacks by analysing email content and URLs. Using machine learning algorithms, this API offers superior phishing detection to enhance your email security. Integrate PhishTackle-API into your applications to help detect and prevent phishing threats.

🔍🎣Phishing Detection Using Machine Learning, this project presents a machine learning-based phishing detection system that classifies URLs as either legitimate or phishing. Phishing attacks are a major cybersecurity threat where malicious websites deceive users into revealing sensitive information.

Intrusion Detection System that uses Machine Learning to detect Phishing_URL's for example fake link throught an spear-phishing attack to a login page.

An intelligent phishing detection system that uses Machine Learning to identify malicious URLs in real-time. This tool helps secure users from phishing attacks by analyzing URL patterns, extracting features, and classifying them as benign or phishing with high accuracy.

Phishing Website Detection, a project focused on predicting and preventing phishing attacks using advanced machine learning techniques. With curated datasets, feature extraction, and diverse models, it aim to enhance online security and empower users for a safer digital experience.

An AI-powered phishing detection and alert system with a browser extension and web interface for cybersecurity officials. Uses machine learning, NLP, and URL analysis to detect phishing links in real-time, analyze emails/messages, and trace attack sources.

Due to rapid growth of internet cyber attacks also increasing. One of them is phishing. Phishing is a Technique in which attackers steal sensitive or confidential information such as usernames, passwords,credit card details, personal identification numbers (PIN) etc.... from internet users. The traditional approach to detect phishing attacks is a centralized blacklist approach and heuristics but they usually fail to detect zero-day attacks. The goal of our project is to implement a Phishing detection using machine learning model which classifies whether a given URL is legitimate or a phishing URL by using chrome extension

Detection of Cyber attack(phishing) using Machine Learning Algorithm

Phishing URL Detection using Machine Learning A machine learning-based system for detecting phishing URLs with up to 99.7% accuracy, trained on a labeled dataset of malicious and legitimate URLs. This project leverages supervised learning algorithms to classify URLs and protect users from phishing attacks.

Phishing attacks continue to be a prevalent threat in the digital landscape, targeting individuals and organizations alike. As part of our project, we aim to develop an effective and efficient phishing attack detection system using Machine Learning (ML) techniques.

This is a Machine Learning-based Phishing Detection and Response System that helps in identifying phishing websites and preventing cyber attacks. It uses **Natural Language Processing (NLP) and Machine Learning** to detect whether a website is safe or malicious.

This project aims to improve online security by developing a robust phishing detection system integrated with Google Chrome using hybrid machine learning techniques. The system will accurately identify and thwart phishing attacks in real-time, while minimizing false positives.

PhishAlert is an intelligent email threat detection system that uses machine learning and NLP techniques to identify and classify phishing emails. It helps protect users from malicious attacks by accurately detecting fraudulent email content.

The URL Detection System is a Machine Learning project that classifies URLs as malicious or benign. Malicious URLs are commonly used in phishing, malware distribution, and other cyber-attacks. This project helps in early detection and prevention by analyzing URL patterns and features

System Threat Forecaster is a machine learning project that predicts and classifies potential system threats using historical data. It helps identify risks like malware, phishing, and DoS attacks, enabling proactive defense. Built with Python and scikit-learn, it offers a foundation for real-time threat detection and cybersecurity enhancement.

PhishAlyzer is an advanced phishing detection application designed to identify potentially malicious URLs and safeguard users from phishing attacks. Built with an intuitive GUI using Streamlit, this app leverages machine learning to analyse URL features and predict their legitimacy.

Phishing is a growing cyber crime that involves posing as a trustworthy entity in order to steal sensitive information. Because of the rise in phishing attacks, there is a greater need for effective solutions to detect and prevent these attacks.

My Project

Phishing remains one of the most widespread and damaging cybersecurity threats, targeting users through deceptive URLs and websites. Traditional blacklist and rule-based methods often fail against evolving phishing tactics.

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