Phishing Detection With Real-Time Session Analysis and Safe Preview
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Solution Overview
Problem
Traditional cybersecurity measures are inadequate in detecting sophisticated and evolving phishing and social engineering attacks, particularly through web, SMS, and social media, as they lack behavior analysis environments and rely on manual or semi-automatic processing, which is inefficient against growing volumes of attacks.
Innovation Solution
An automated system using machine learning classifiers and virtual browser analysis modules to analyze network session activity in real-time, classify phishing attacks, block them, and provide safe previews, integrated with existing security software via APIs, employing NLP for message analysis and URL inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional security measures (manual or semi-automatic processing) are used to detect phishing attacks, then the system is simpler to implement, but the productivity and ability to handle growing volumes of attacks is insufficient
Solution Approach 1:
The system employs machine learning classifiers that automatically analyze and classify phishing attacks without human intervention. The classifiers self-train on labeled datasets and autonomously process incoming traffic, performing security analysis that would otherwise require manual security expert involvement.
Solution Approach 2:
Manual security analysis is replaced with automated machine learning-based classification systems. The patent substitutes human-driven security processing with algorithm-driven automated classification, enabling scalable handling of attack volumes without proportional increases in human resources.
2Measurement precision
If machine learning classifiers are deployed for automated phishing detection, then the productivity and detection accuracy improve, but the device complexity and computational requirements increase
Solution Approach 1:
The security analysis system is divided into multiple specialized machine learning classifiers, each trained to detect specific phishing techniques or attack patterns. This segmentation allows the complex detection task to be distributed across multiple simpler, specialized models rather than requiring one monolithic complex system.
Solution Approach 2:
The machine learning classifiers are pre-trained on labeled datasets containing examples of phishing and legitimate traffic before deployment. This preliminary training action enables the system to perform accurate classification from the start, reducing the complexity of real-time analysis by having pre-computed knowledge ready for application.
3Speed
If real-time inspection of web traffic and messages is implemented, then the speed of attack detection improves, but the use of energy and computational resources increases
Solution Approach 1:
The system applies machine learning classifiers selectively to traffic that exhibits suspicious characteristics or matches known attack patterns, rather than analyzing every single packet or message in full detail. This partial action approach maintains real-time detection capability while reducing overall computational resource consumption.
Data Source
AI summary
Provided are systems, software, and methods for phishing analysis and detection. The provided systems, software, and methods may comprise interfaces configured to capture and scan network session activity in real-time to detect a phishing attack using a set of trained machine learning classifiers, detect a phishing attach, classify the attack into one or more phishing classes, block the phishing attack and provide a safe preview of the blocked phishing attack. The machine learning classifiers may be deployed remotely. The systems, software, and methods may further comprise a web application programing interface configured to integrate the one or more analysis interfaces into at least one external software or application.