Computational Model for Malware Signature Detection
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Solution Overview
Problem
Existing malware detection methods face challenges in efficiently identifying concealed malware and determining the generator used to create files, due to the large number of recognizable patterns and the difficulty in maintaining a current database, as well as the obfuscation techniques employed by malware authors.
Innovation Solution
The use of computational models, such as autoencoders, that can automatically train in an unsupervised manner to determine signatures of files, allowing for increased flexibility and accuracy in analysis and classification, rather than relying on fixed databases of patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed databases of patterns are used for malware detection, then detection accuracy can be maintained for known malware, but the system becomes time-consuming and inefficient when dealing with new or obfuscated malware variants
Solution Approach 1:
The system employs self-supervised learning where the model automatically trains itself using unlabeled data streams without requiring manual annotation or supervision. The computational model processes data streams and generates its own training signals, enabling autonomous adaptation to new malware patterns without human intervention, thus resolving the contradiction between maintaining detection accuracy and reducing analysis time
Solution Approach 2:
The patent implements dynamic computational models that continuously adapt and evolve as they process data streams. The model parameters are updated in real-time based on incoming data, allowing the system to dynamically adjust to new malware variants and obfuscation techniques, thereby maintaining high detection accuracy while reducing the time required to identify threats
2Adaptability or versatility
If comprehensive pattern databases are maintained to cover all malware types, then detection coverage is improved, but the complexity and difficulty of maintaining current databases increases
Solution Approach 1:
The computational model automatically learns and adapts to diverse malware patterns through self-supervised learning, eliminating the need for manual database curation. The system processes unlabeled data streams and autonomously identifies patterns, generating comprehensive coverage across malware types without requiring human experts to maintain complex pattern databases
Solution Approach 2:
The patent employs a universal computational model that can handle multiple malware types and obfuscation techniques through a single unified architecture. This multi-functional approach replaces the need for separate specialized databases for different malware categories, simplifying maintenance while maintaining broad adaptability across threat types
3Duration of action of stationary object
If obfuscation techniques are used by malware authors to evade detection, then malware survival time increases, but the difficulty of determining harmful actions without running malware increases
Solution Approach 1:
The patent replaces traditional mechanical pattern-matching approaches with computational models based on self-supervised learning. These models analyze data streams and learn underlying patterns without relying on predefined signatures, enabling them to detect obfuscated malware by understanding behavioral patterns rather than matching static patterns, thus reducing the difficulty of determining harmful actions
Solution Approach 2:
The computational model acts as an intermediary between raw data streams and malware classification. It processes and transforms obfuscated data into meaningful representations through self-supervised learning, bridging the gap between obscured malware characteristics and detectable patterns, thereby reducing the difficulty of determining harmful actions without executing the malware
Data Source
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AI summary
Example techniques described herein determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processor can locate training analysis regions of training data streams based on predetermined structure data, and determining training model inputs based on the training analysis regions. The processor can determine a computational model based on the training model inputs. The computational model can receive an input vector and provide a corresponding feature vector. The processor can then locate a trial analysis region of a trial data stream based on the predetermined structure data and determine a trial model input. The processor can operate the computational model based on the trial model input to provide a trial feature vector, e.g., a signature. The processor can operate a second computational model to provide a classification based on the signature.