Automated Behavioral Signatures for New Web Malware Detection
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Solution Overview
Problem
Existing anti-malware security solutions fail to detect new malware or new malware variants on the web with a low false positive rate, exposing enterprises to significant security risks.
Innovation Solution
A system for automated generation of behavioral signatures for malicious web campaigns that synthesizes signatures from static and dynamic behaviors of web pages, using a browser environment to crawl URLs and generate human-interpretable malware campaign signatures, which can detect malicious web sites even with encrypted or obfuscated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing anti-malware security solutions are used, then security coverage is provided, but detection capability for new malware variants is insufficient
Solution Approach 1:
The system dynamically generates behavioral signatures by analyzing actual malware operations in real-time environments. Instead of relying on static signature databases that require manual updates, the system continuously monitors malware behavior patterns, extracts actionable intelligence, and automatically generates updated signatures, enabling adaptive detection of new variants without manual intervention
Solution Approach 2:
The system performs self-learning through automated analysis of malware operations. It independently collects operational data, analyzes behavior patterns, generates signatures, and updates detection rules without requiring human security analysts. This self-service capability allows the system to automatically adapt to new malware variants and maintain high detection accuracy
2Productivity
If automated signature generation is implemented, then detection speed is improved, but system complexity increases
Solution Approach 1:
The system divides the complex malware detection task into separate modular components: a malware operation collection module that captures behavioral data, an analysis module that processes and identifies patterns, and a signature generation module that creates detection rules. This segmentation allows each component to be optimized independently and simplifies maintenance while maintaining high automation speed
Solution Approach 2:
The system introduces an intermediary analysis layer that processes raw malware operational data and transforms it into structured behavioral patterns. This intermediary processing step bridges the gap between raw data collection and final signature generation, automating the transformation process while managing complexity through standardized data formats and processing pipelines
3Measurement precision
If behavioral analysis is performed, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis by collecting and pre-processing malware operational data during normal execution. It extracts and stores behavioral patterns in advance, organizing them in a structured format that can be quickly processed when generating signatures. This preliminary data preparation reduces the time required for final analysis and signature creation while maintaining high accuracy
Data Source
AI summary
Techniques for automated generation of behavioral signatures for malicious web campaigns are disclosed. In some embodiments, a system/process/computer program product for automated generation of behavioral signatures for malicious web campaigns includes crawling a plurality of web sites associated with a malware campaign; determining discriminating repeating attributes (e.g., behavior related attributes, which can be determined using dynamic analysis, and static related attributes, which can be determined using static analysis) as malware campaign related footprint patterns, wherein the discriminating repeating attributes are not associated with benign web sites; and automatically generating a human-interpretable malware campaign signature based on the malware campaign related footprint patterns.