Browser Detection System for Social Engineering Ad Blocking
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Solution Overview
Problem
Current advertisement blockers are ineffective in mitigating web-based social engineering attacks (WSEAs) as these attacks are diverse and configured to evade detection and blocking operations.
Innovation Solution
A real-time detection system that monitors user browsing sessions via instrumented hooks in the browser, using trained artificial intelligence (AI) features to identify social engineering advertisements (SE-ads) and block navigation to potential SE-websites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If current advertisement blockers are used to block ads, then generic advertising is reduced, but social engineering attacks evade detection and blocking
Solution Approach 1:
The system performs preliminary detection of social engineering ads before they can execute malicious navigation. By monitoring DOM elements and navigation events in advance, the system identifies SE-ads and blocks navigation to malicious websites before the user is harmed, preventing the attack from completing.
Solution Approach 2:
The system introduces an intermediary detection layer between the ad network and the user. This intermediary monitors and analyzes ad behavior, DOM modifications, and navigation patterns to identify social engineering attacks, acting as a mediator that blocks malicious ads while allowing legitimate content to pass through.
2Measurement precision
If detection systems monitor all browsing sessions in real-time, then detection accuracy improves, but system complexity and processing overhead increase
Solution Approach 1:
The system applies local quality by focusing detection resources on specific high-risk elements rather than uniformly monitoring everything. It targets DOM elements with suspicious characteristics, navigation events from untrusted sources, and specific action patterns that indicate social engineering, thereby maintaining high detection accuracy while reducing overall system complexity.
Solution Approach 2:
The detection system segments monitoring into distinct functional components: DOM element monitoring, navigation event tracking, and action pattern analysis. Each component handles specific aspects of SE-ad detection independently, making the overall system more manageable and easier to optimize while maintaining comprehensive coverage.
Data Source
AI summary
An exemplary system and method are disclosed that can mitigate online social engineering attacks at scale using a real-time detection system configured to identify social engineering advertisements and to block users' navigation to potential social engineering websites. To detect SE-ads and block the subsequent events, the exemplary system and method can monitor a user's browsing session and evaluate each navigation to determine if it may be related to an SE-ad using trained AI features related to how the navigation was initiated and employing the features in a classification evaluation. Upon detection, the exemplary system and method can output a notification to warn the user.