Malware Detection via HTTP Error Pattern Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Malware often evades detection by generating HTTP errors that mimic those from benign users, making it difficult for existing intrusion detection systems to distinguish between malicious and benign activities, allowing malware to recover and reconnect to backup servers.
Innovation Solution
A malware detector system that filters and clusters HTTP error traffic, extracting feature vectors from error patterns to differentiate between malware-generated and benign errors, using machine learning to classify clusters as malicious and drive automated remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing intrusion detection systems block HTTP errors, then benign user activities are disrupted, but malware-generated errors cannot be distinguished and evade detection
Solution Approach 1:
The patent segments HTTP error traffic into distinct clusters based on error codes, URIs, and temporal patterns. By dividing the monolithic error detection problem into clustered segments, the system can analyze specific error patterns (e.g., 404 errors from malware vs. 403 errors from policy violations) separately, improving detection accuracy while avoiding false positives from benign activities.
Solution Approach 2:
The system dynamically adapts error detection thresholds and clustering parameters based on observed traffic patterns. Machine learning models continuously learn from new error data, adjusting detection sensitivity to distinguish evolving malware techniques from legitimate error-generating activities, thereby maintaining high detection accuracy without disrupting benign users.
2Reliability
If malware connects to multiple backup servers to overcome network failures, then malware resilience improves, but detection complexity increases
Solution Approach 1:
The system performs preliminary clustering and pattern analysis on error traffic before full detection processing. By pre-grouping errors into clusters based on initial pattern recognition, the system simplifies subsequent detection steps and reduces computational complexity while maintaining reliable detection of malware attempting to connect to backup servers.
Solution Approach 2:
The detection system incorporates feedback loops where detection results and clustered error patterns are fed back into the machine learning models. This continuous feedback refines the system's ability to detect malware resilience techniques while adapting to new attack patterns, balancing detection reliability with manageable system complexity through intelligent automation.
3Loss of information
If HTTP error filtering is applied to reduce noise, then benign errors are removed, but malware errors may be filtered out as well
Solution Approach 1:
The patent applies different filtering criteria and analysis depths to different error clusters based on their local characteristics. Instead of uniform filtering, the system tailors filtering intensity and detection sensitivity to each cluster's specific error codes, URIs, and temporal patterns, preserving malware indicators while removing benign noise through localized quality assessment.
Solution Approach 2:
The system dynamically adjusts filtering parameters such as error rate thresholds, time window sizes, and cluster confidence levels based on observed traffic conditions. By changing parameters adaptively rather than using fixed thresholds, the system maintains reliable malware detection while effectively filtering benign error noise under varying network conditions.
Data Source
AI summary
Malware is detected and mitigated by differentiating HTTP error generation patterns between errors generated by malware, and errors generated by benign users/software. In one embodiment, a malware detector system receives traffic that includes HTTP errors and successful HTTP requests. Error traffic and the successful request traffic are segmented for further analysis. The error traffic is supplied to a clustering component, which groups the errors, e.g., based on their URI pages and parameters. During clustering, various statistical features are extracted (as feature vectors) from one or more perspectives, namely, error provenance, error generation, and error recovery. The feature vectors are supplied to a classifier component, which is trained to distinguish malware-generated errors from benign errors. Once trained, the classifier takes an error cluster and its surrounding successful HTTP requests as inputs, and it produces a verdict on whether a particular cluster is malicious. The classifier output then drives an automated mitigation operation.