Malware Classifier Using Pattern Classification
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Solution Overview
Problem
Current malware detection techniques are ineffective in identifying unknown malware, as they rely on predefined pattern databases or manual rule-based systems, which are difficult to maintain and cannot achieve both high detection rates and low false-positive rates.
Innovation Solution
A malware classifier using a pattern classification algorithm that statistically analyzes software features, such as dynamic link library and function name strings, to categorize software as benign or malicious, with a trained model and feature definition file, employing algorithms like Support Vector Machine (SVM) for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined pattern databases are used to detect known malware, then detection accuracy for known malware is improved, but the system cannot detect new unknown malware
Solution Approach 1:
The system performs preliminary statistical analysis during a training phase to build classification models before actual malware detection is needed. This preliminary action creates reusable models that can handle both known and unknown malware without requiring manual rule updates for each new threat
Solution Approach 2:
The system changes from using fixed predefined patterns to using statistical parameters derived from training data. By analyzing feature distributions and building probabilistic models during training, the system adapts to new malware types while maintaining detection accuracy for known threats
2Adaptability or versatility
If manual rules and heuristics are used to detect unknown malware, then some detection capability is achieved, but the system is difficult to maintain and time-consuming
Solution Approach 1:
The system performs self-service by automatically learning detection rules from training data during the training phase. Instead of requiring manual rule creation and maintenance, the statistical analysis and model building are automated, eliminating the time-consuming manual effort while maintaining detection capability
Solution Approach 2:
The system replaces manual mechanical rule-writing with automated statistical analysis and machine learning. The classification algorithms automatically generate detection logic from training examples, substituting the manual mechanical process with an automated computational approach
3Device complexity
If limited rules are used for malware detection, then the system remains simple, but both detection rate and false-positive rate cannot be optimized simultaneously
Solution Approach 1:
The system segments the malware detection problem into distinct feature categories (e.g., file headers, code characteristics, behavioral patterns). This segmentation allows the statistical analysis to focus on specific aspects independently, maintaining manageable complexity while achieving high detection performance through combined feature analysis
Solution Approach 2:
The system transitions from binary rule-based decisions to multi-dimensional statistical analysis. By analyzing multiple features simultaneously and using probabilistic classification, the system adds dimensional complexity in a controlled way that improves reliability without overwhelming system complexity
Data Source
AI summary
A malware classifier uses features of suspect software to classify the software as malicious or not. The classifier uses a pattern classification algorithm to statistically analyze computer software. The classifier takes a feature representation of the software and maps it to the classification label with the use of a trained model. The feature representation of the input computer software includes the relevant features and the values of each feature. These features include the categories of: applicable software characteristics of a particular type of malware; dynamic link library (DLL) and function name strings typically occurring in the body of the malware; and other alphanumeric strings commonly found in malware. By providing these features and their values to the classifier, the classifier is better able to identify a particular type of malware.