Machine Learning Malware Detection via Runtime Behavior Baseline
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Solution Overview
Problem
Conventional cybersecurity approaches are inadequate for detecting and mitigating latent malware, which can remain undetected during development and become active at critical stages, and they lack the tools for intelligent, real-time responses to prevent damage.
Innovation Solution
A cybersecurity system that learns a computing system's normal behavior during testing and evaluation, using a kernel-based system call interception monitor and machine-learning model to detect deviations and automatically execute mitigation instructions, while minimizing system disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signature-based anti-virus solutions are used, then detection of known malwares is improved, but detection of new and latent malwares deteriorates
Solution Approach 1:
The system performs preliminary action by establishing a baseline of normal system behavior during a testing and evaluation phase before actual operation. This baseline is created through machine learning analysis of system calls, performance metrics, and operational patterns during controlled testing, enabling the system to detect deviations that indicate malware presence without requiring prior knowledge of specific malware signatures.
Solution Approach 2:
The system implements continuous feedback by monitoring system behavior during runtime and comparing it against the established baseline. The machine learning model analyzes ongoing system calls and performance metrics, providing real-time feedback on deviations from normal behavior. This feedback loop enables dynamic detection and response to both known and unknown malware threats.
2Measurement precision
If file system baselining is used to detect malware, then detection of file-based threats is improved, but detection of latent malware that activates at runtime deteriorates
Solution Approach 1:
The system ensures continuity of useful action by transitioning from static file system baselining to continuous runtime behavior monitoring. Instead of performing discrete file integrity checks, the system continuously monitors system calls, process behavior, and performance metrics throughout the system's operational lifecycle. This continuous monitoring ensures that latent malware, which may not manifest in file system changes until activation, is detected through behavioral anomalies.
3Ease of operation
If real-time human-in-the-loop solutions are implemented, then intelligent response capability is improved, but response time and system functionality deteriorate
Solution Approach 1:
The system implements self-service by automating the malware detection and response process through machine learning models and predefined mitigation strategies. When the system detects behavioral deviations indicating malware presence, it automatically executes appropriate countermeasures such as isolating affected processes, blocking malicious system calls, or alerting security personnel. This automation eliminates the need for immediate human intervention while maintaining intelligent response capabilities.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which detects and defends against malware in-flight regardless of the specific nature and methodology of the underlying attack. The analytic server learns the system's normal behavior during testing and evaluation phase and trains a machine-learning model based on the normal behavior. The analytic server monitors the system behavior during runtime comprising the runtime behavior of each sub-system of the system. The analytic server executes the machine-learning model and compares the system runtime behavior with the normal behavior to identify anomalous behavior. The analytic server executes one or more mitigation instructions to mitigate malware. Based on multiple available options for mitigating malware, the analytic server makes an intelligent decision and takes the least impactful action that have the least impact on the system to maintain mission assurance.