AI-Driven Phishing Detection and Prevention System Using Machine Learning and Real-Time Analysis

Abstract

The invention provides an AI Enhanced Phishing Detection System designed to identify and prevent phishing attacks using machine learning and natural language processing techniques. The system features modules for data collection, feature extraction, AI model training, real-time detection, and continuous learning from feedback. The AI models are trained on historical phishing data and are capable of adapting to new phishing techniques through user input, providing a dynamic and efficient defense against cyberattacks.

Specification

Description:This invention relates to the field of cybersecurity, more specifically to systems and methods for detecting phishing attacks using artificial intelligence (AI).
Background:
Phishing attacks have become a prevalent form of cybercrime, where attackers attempt to deceive users into revealing sensitive information such as passwords, credit card details, or other personal data by pretending to be legitimate entities. Current phishing detection systems often rely on traditional techniques like blacklist-based methods or manual filtering, which can be inefficient and prone to failure against sophisticated phishing attempts. AI-enhanced phishing detection systems offer a solution by leveraging machine learning and natural language processing (NLP) techniques to automatically identify and mitigate phishing threats.
Summary of the Invention:
The AI Enhanced Phishing Detection System is designed to efficiently identify and block phishing attempts by utilizing advanced AI algorithms that analyze both the content and the , Claims:1. Claim 1: A system for detecting phishing attacks, comprising:
• A data collection module for gathering email and message content, URLs, and metadata.
• A feature extraction module configured to process the collected data and extract features indicative of phishing activity.
• An AI model training module utilizing machine learning algorithms, including Random Forest, SVM, and deep learning, to classify messages as phishing or legitimate.
• A real-time detection module integrated with email and messaging platforms to analyze and classify incoming messages.
• A feedback and learning module that updates the AI models based on user feedback and new phishing data.
2. Claim 2: The system of claim 1, wherein the feature extraction module analyzes URL links for suspicious patterns or shortened URLs commonly associated with phishing attacks.
3. Claim 3: The system of claim 1, wherein the AI model training module uses a supervised learning technique that is continuously updated with new phishing datasets.
4. Claim 4:

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