Network Traffic Analysis for File-Less Malware Detection
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Solution Overview
Problem
Traditional malware detection methods fail to effectively identify file-less malware, which resides in memory and uses obfuscation algorithms to evade detection, making it difficult to detect through traditional disk scans and network scanners.
Innovation Solution
The approach involves analyzing network traffic using decryption and scoring algorithms to identify obfuscation techniques employed by malware, classifying traffic samples as potentially malicious, and applying thresholds to confirm malicious activity, allowing for the detection and remediation of malware without relying on memory analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional disk scan methods are used, then detection simplicity is maintained, but detection capability against file-less malware is lost
Solution Approach 1:
The patent replaces traditional mechanical disk scanning methods with network traffic analysis. Instead of scanning files on disk, the system intercepts and analyzes network packets to detect malware communication patterns, thereby detecting file-less malware that resides only in memory and does not write to disk.
Solution Approach 2:
The patent introduces network traffic as an intermediary to detect malware. By analyzing the communication between malware and command-and-control servers, the system can identify malicious activity without directly examining the malware itself, which may be obfuscated or file-less.
2Object-affected harmful factors
If obfuscation algorithms are used by malware, then malware evasion capability is improved, but detection difficulty increases
Solution Approach 1:
The patent inverts the detection approach by not trying to directly analyze and decode obfuscated malware code. Instead, it analyzes the network traffic patterns generated by malware, which reveal characteristic communication behaviors regardless of code obfuscation. This allows detection of obfuscated malware through its behavioral fingerprints in network traffic.
Solution Approach 2:
The patent changes the detection perspective from examining malware code directly (which may be obfuscated) to examining network traffic characteristics. Different malware types produce distinct network traffic patterns, allowing identification through these 'color changes' in traffic analysis rather than code analysis.
3Reliability
If memory analysis is used, then file-less malware detection is improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts the detection function from direct memory analysis and relocates it to network traffic analysis. By capturing and analyzing network packets at the network level, the system can detect file-less malware without needing to extensively analyze memory contents, thereby reducing computational resource consumption on endpoint devices.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to detect malware based on network traffic analysis. An example apparatus includes a classification controller to: in response to a first classification score of a first network traffic sample satisfying a first threshold, determine whether a second classification score of a second network traffic sample satisfies a second threshold; and in response to the second classification score of the second network traffic sample satisfying the second threshold, classify network traffic associated with the first network traffic sample and the second network traffic sample as potentially malicious network traffic; and a remediation controller to, in response to the network traffic being classified as the potentially malicious network traffic, execute a remediation action to remediate malicious activity associated with the potentially malicious network traffic.