Malware Detection via Transfer Protocol Log Analysis
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Solution Overview
Problem
Conventional malware detection methods are inefficient and limited in their ability to detect new or unknown malware, as they rely on computationally expensive binary analysis and hashing techniques that fail to analyze file contents effectively and only expose previously known malicious files.
Innovation Solution
A system and method for malware detection that analyzes data from non-encrypted internet transfer protocol logs, using a detection module that filters data based on criteria such as file path information and content length, computes outlier scores for feature values, and merges these scores to generate an output score for detecting malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If file binaries are analyzed by machine learning algorithms to identify malicious segments of code, then malware detection accuracy is improved, but computational cost increases significantly because transfer protocol downloads can be very large and frequent
Solution Approach 1:
The patent extracts and analyzes only specific features from transfer protocol logs (such as URL patterns, file paths, content types, and metadata) rather than analyzing entire file binaries. This selective extraction of relevant information maintains malware detection capability while dramatically reducing computational resources required, as the system processes log data instead of complete binary files.
Solution Approach 2:
The detection system segments the malware analysis process into two distinct phases: (1) analyzing transfer protocol logs to identify suspicious download patterns and features, and (2) performing detailed binary analysis only on files that score above a certain threshold. This segmentation allows the system to screen large volumes of downloads efficiently while maintaining deep analysis capability for potential threats.
2Speed
If hashing methodologies are used for malware detection by comparing file hashes to blacklists, then detection speed is improved, but information about file contents is lost and only previously known malware can be detected
Solution Approach 1:
The system performs preliminary analysis of transfer protocol logs before actual file download or binary analysis. By examining metadata, URL patterns, and transfer characteristics in advance, the system can identify suspicious downloads and prioritize them for further analysis, maintaining fast detection while preserving information about file characteristics and download behavior patterns.
Solution Approach 2:
The patent shifts the detection dimension from binary content analysis to transfer protocol log analysis. Instead of hashing file contents, the system analyzes multiple dimensions of log data including HTTP headers, URL structures, referrer information, and temporal patterns. This dimensional shift preserves rich information about file downloads while enabling fast processing of log data.
3Reliability
If conventional file analysis methods are used to detect malware, then previously known malware can be identified, but new or unknown malware cannot be discovered or learned
Solution Approach 1:
The system implements feedback mechanisms where detected malware patterns and new threat intelligence are continuously incorporated into the analysis model. Transfer protocol log analysis results feed into updating detection rules and features, enabling the system to learn from new malware patterns while maintaining reliability for detecting known threats through established blacklist comparisons.
Solution Approach 2:
The patent employs multiple detectable parameters from transfer protocol logs (URL patterns, file paths, content types, response codes, temporal patterns) that can adapt to new malware types. By monitoring changes in these parameters and their relationships, the system can identify novel malware behaviors without relying solely on predefined signatures, enabling discovery of new threats while maintaining detection of known malware.
Data Source
AI summary
Systems and methods for detecting suspicious malware by analyzing data such as transfer protocol data or logs from a host within an enterprise is provided. The systems and methods include a database for storing current data and historical data obtained from the network and a detection module and an optional display. The embodiments herein extract information from non-encrypted transfer protocol metadata, determine a plurality of features, utilize an outlier detection model that is based on historical behaviors, calculate a suspiciousness score, and create alerts for analysis by users when the score exceeds a threshold. In doing so, the systems and methods of the present invention improve the ability to identify suspicious outliers or potential malware on an iterative basis over time.