Behavioral Profiling for Scripting Language Attack Detection
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Solution Overview
Problem
Current methods for detecting malicious software in computer networks are inadequate due to reliance on static signature-based detections, which fail to accurately identify obfuscated threats and often flag legitimate system administrator activities as malicious, leading to false positives.
Innovation Solution
A method involving a hybrid machine learning system that generates behavior profiles based on scripting language commands, using raw machine data to analyze and detect anomalies, thereby reducing false positives and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static signature-based detection methods are used to identify malicious software, then the detection process is simple and fast, but the detection accuracy is low and false positives increase when facing obfuscated threats or legitimate administrator activities
Solution Approach 1:
The patent transforms the detection approach from static signature matching to dynamic behavior analysis by changing the parameters being monitored. Instead of checking for fixed malicious patterns, the system monitors scripting language commands, system calls, and execution behaviors to build dynamic threat detection models that adapt to obfuscated threats while maintaining detection speed through efficient data collection and analysis.
Solution Approach 2:
The patent replaces the mechanical signature-matching system with a machine learning-based behavioral analysis system. The machine learning model processes raw machine data to identify patterns indicative of malicious behavior, substituting the rigid mechanical approach of signature comparison with a more flexible, adaptive intelligent system that reduces false positives while maintaining detection efficiency.
2Reliability
If obfuscation techniques are used by malicious software creators, then the malicious software can evade detection by signature-based methods, but the complexity of analyzing and detecting these threats increases
Solution Approach 1:
The patent extracts and isolates specific indicators of compromise (IOCs) such as scripting language commands, system call patterns, and execution behaviors from the overall software execution. By focusing on these extracted behavioral elements rather than analyzing the entire obfuscated code, the system maintains detection effectiveness while managing complexity through targeted monitoring of suspicious patterns.
Solution Approach 2:
The patent introduces an intermediary layer of behavioral analysis that sits between the raw executable code and the detection system. This intermediary processes and translates obfuscated code into observable behavioral patterns, acting as a mediator that preserves detection capability while simplifying the analysis of complex obfuscated threats through standardized behavioral indicators.
3Ease of operation
If search substrings are used to identify malicious commands, then the detection process is straightforward, but legitimate administrator commands are incorrectly flagged as malicious leading to false positives
Solution Approach 1:
The patent transitions from static search substring matching to dynamic behavioral context analysis. Instead of using fixed keywords that cannot distinguish between malicious and legitimate uses, the system dynamically evaluates the context, sequence, and combination of commands executed by scripting languages, allowing it to maintain operational simplicity while dramatically reducing false positives through contextual understanding.
Data Source
AI summary
Techniques for identifying attack behavior based on scripting language activity are disclosed. A security monitoring system generates a behavior profile for a first client device based on scripting language commands included in a first set of raw machine data received from the first client device, where the first client device is coupled to a network, and the first set of raw machine data is associated with network traffic received by or transmitted from the first client device. The security monitoring system analyzes a second set of raw machine data received from the first client device, where the second set of raw machine data is associated with subsequent network traffic received by or transmitted from the first client device. The security monitoring system detects an anomaly in the second set of raw machine data based on the behavior profile, and initiates a mitigation action in response to detecting the anomaly.