Adaptive Malware Detection Using Synthetic Code Pattern Training
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Solution Overview
Problem
Existing cybersecurity solutions require complex correlation rules and skilled personnel for threat detection, and there is a need for efficient and automated malware detection systems that can adapt to evolving threats.
Innovation Solution
A multi-category machine-learning system using a generator-discriminator network (CatGAN) and an auto-encoder (CVAE) is trained with known malware and benevolent code patterns to generate synthetic patterns, determining statistical distributions and quality values for accurate malware detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex correlation rules are used for threat detection, then detection accuracy is improved, but system complexity and personnel requirements increase
Solution Approach 1:
The patent replaces manual correlation rules with an automated machine learning system that uses neural networks to detect malware. The system automatically learns patterns from training data and makes detection decisions without requiring complex manual rule configuration, thereby reducing system complexity while maintaining detection accuracy.
Solution Approach 2:
The machine learning system performs self-training by automatically processing training data, learning patterns, and improving its detection capabilities without human intervention. The system self-adjusts to new threats by continuously learning from new data, eliminating the need for manual rule updates and reducing operational complexity.
2Reliability
If skilled personnel are required for rule tuning, then detection reliability is improved, but operational complexity increases
Solution Approach 1:
The system automatically trains and tunes itself by processing large volumes of training data to learn optimal detection patterns. The machine learning model self-adjusts its parameters and decision boundaries without human intervention, maintaining high detection reliability while eliminating the need for skilled personnel to manually tune rules.
Solution Approach 2:
The system creates synthetic training data that replicates real malware patterns, allowing the model to learn from numerous examples without human analysis. This copying approach enables the system to achieve expert-level detection capabilities through data repetition rather than human expertise.
3Measurement precision
If more training data is used for machine learning, then detection accuracy is improved, but data processing requirements increase
Solution Approach 1:
The patent generates synthetic training data that replicates real malware patterns, allowing the model to train on extensive datasets without requiring actual malware samples. This synthetic data generation enables unlimited training capacity while avoiding the complexity of processing and securing large volumes of real threat data.
Solution Approach 2:
The system replaces manual data processing with automated machine learning algorithms that efficiently process and extract patterns from training data. The neural network automatically learns from data without requiring manual feature engineering or data preprocessing, reducing the computational burden despite using large datasets.
Data Source
AI summary
The exemplary embodiments disclose a method, a computer system, and a computer program product for detecting malware. The exemplary embodiments may include aggregating known malware patterns by storing malware patterns and related malware categories of the malware patterns. The exemplary embodiments may additionally include training a first machine-learning system, comprising a generator portion and a discriminator portion, by using the known malware patterns and the related malware categories as training data. The exemplary embodiments may also include generating additional synthetic code patterns by feeding random code samples to the trained first machine-learning system. The exemplary embodiments may further include training a second machine-learning system by using benevolent code patterns and the generated additional synthetic code patterns as training data. The exemplary embodiments may include determining a statistical distribution of predicted malicious code patterns and related categories, and determining a quality value of the training of the second machine-learning system.