Proactive Malware Detection via Control Flow Analysis
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Solution Overview
Problem
Conventional malware detection techniques rely on lists of known signatures, which are ineffective against zero-day attacks and polymorphic attacks, and often result in high false positive rates due to coincidental pattern matching, leaving systems vulnerable during the time window before updates are available.
Innovation Solution
A malware detection system that analyzes bit patterns collectively to detect executable code, focusing on loop structures and control flows to identify potential malicious code, while minimizing false positives by assigning weights based on patterns present or absent in the data, and prioritizing analysis of frequently occurring opcodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional signature-based malware detection is used, then known malware can be detected, but zero-day and polymorphic attacks cannot be detected and false positive rates are high
Solution Approach 1:
The patent changes the detection parameters from fixed signature patterns to dynamic characteristics such as loop structures, control flow patterns, and instruction sequence behaviors. By monitoring these behavioral parameters rather than static signatures, the system can detect both known and unknown malware while reducing false positives from coincidental pattern matches
Solution Approach 2:
The system transitions from static signature matching to dynamic analysis of malware behavior characteristics. It analyzes control flow dynamics, instruction execution patterns, and structural properties that change based on the malware's actual behavior rather than relying on predetermined signatures, enabling detection of polymorphic and zero-day threats
2Reliability
If signature-based scanning is performed on all incoming data files, then known malware can be identified, but the system produces high false positive rates due to coincidental pattern matching
Solution Approach 1:
The patent segments the analysis into multiple independent characteristic detections: loop structure detection, control flow analysis, instruction sequence validation, and behavioral pattern recognition. By dividing the detection process into separate segments that each verify specific aspects of malware behavior, the system reduces false positives while maintaining comprehensive detection capability
Solution Approach 2:
The system introduces intermediary analysis layers between raw data and malware classification. These intermediaries include control flow graphs, instruction sequence analyzers, and structural validators that mediate between the input data and final detection decisions, filtering out false positives through multiple verification stages
3Reliability
If frequent updates of malware signature lists are implemented, then detection of new malware can be improved, but the time window of vulnerability between updates remains and system complexity increases
Solution Approach 1:
The patent implements preliminary action by proactively detecting malware based on behavioral characteristics and structural properties before signatures are available. By analyzing control flow patterns, loop structures, and instruction sequences in real-time, the system provides immediate protection against zero-day and polymorphic threats without waiting for signature updates
4Measurement precision
If analysis of every bit pattern in data files is performed, then detection accuracy can be improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts only the most relevant characteristics from data files for analysis: loop structures, control flow patterns, and instruction sequence properties. By taking out and analyzing only these specific features rather than examining every bit pattern, the system maintains high detection accuracy while significantly reducing processing time and computational overhead
Data Source
AI summary
Malware detection systems and methods for determining whether a collection of data not expected to include executable code is suspected of containing malicious executable code. In some embodiments, a malware detection system may disassemble a collection of data to obtain a sequence of possible instructions and determine whether the collection of data is suspected of containing malicious executable code based, at least partially, on an analysis of the sequence of possible instructions. In one embodiment, the analysis of the sequence of possible instructions may comprise determining whether the sequence of possible instructions comprises an execution loop. In a further embodiment, a control flow of the sequence of possible instructions may be analyzed. In a further embodiment, the analysis of the sequence of possible instructions may comprise assigning a weight that is indicative of a level of suspiciousness of the sequence of possible instructions. In a further embodiment, the sequence of possible instructions may begin with a possible instruction that comprises at least one candidate operation code (opcode) that has been determined to occur frequently in executable code.