Malware Detection via Behavioral Baseline Analysis
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Solution Overview
Problem
Existing antivirus programs are insufficient in protecting computers from the growing number of malwares due to their reactive signature-based detection methods, which require time to identify and remove malware, consume significant computing resources, and often result in false positives, slowing down systems and requiring frequent updates.
Innovation Solution
A malware detection and rectification system that monitors computing systems in real-time, using a combination of techniques such as disassembler, virtual machine, heuristic scanning, keyword analysis, and data mining to analyze process activities and prevent malware execution without relying on traditional signature analysis, thereby reducing resource consumption and the need for regular updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection method is used, then malware detection capability is improved, but response time increases and system resources are consumed
Solution Approach 1:
The patent implements preliminary action by performing static analysis and generating operational attributes for all processes before they execute malicious actions. The system pre-computes baseline attributes and creates a database of normal process behaviors, enabling it to detect malware based on deviations from these pre-established patterns rather than waiting for signature matches after infection occurs.
Solution Approach 2:
The patent replaces the mechanical signature-matching system with a behavioral analysis system that uses machine learning and statistical methods. Instead of mechanically comparing file signatures against known malware databases, the system substitutes this with analyzing operational attributes, process behaviors, and deviations from baseline patterns, enabling faster detection without relying on pre-existing signatures.
2Reliability
If signature-based detection method is used, then malware detection capability is improved, but computing resources are consumed
Solution Approach 1:
The patent extracts only the essential operational attributes needed for malware detection from the entire process execution environment. Instead of analyzing all system activities, it selectively extracts key attributes such as process creation patterns, file access behaviors, registry modifications, and network connections, significantly reducing the computational burden while maintaining detection effectiveness.
Solution Approach 2:
The patent changes parameters by transitioning from analyzing complete malware signatures to analyzing condensed operational attributes and behavioral metrics. It transforms the detection parameters from static file characteristics to dynamic process behaviors, and further optimizes by using statistical deviations from baselines rather than exhaustive pattern matching, reducing computational resource requirements.
3Reliability
If traditional antivirus scan engine is used, then malware detection is provided, but system speed is reduced
Solution Approach 1:
The patent introduces an intermediary layer that operates transparently between the user and the system resources. The behavioral analysis engine monitors process operations and compares them against baseline patterns without interfering with normal system execution. This intermediary approach enables continuous malware detection while maintaining system performance, as the analysis occurs in parallel without blocking user operations.
Solution Approach 2:
The system performs preliminary establishment of baseline operational attributes during normal system operation, so that when malware is detected, the comparison against pre-computed baselines can occur rapidly without slowing down system execution. The baseline profiles are created in advance, enabling real-time detection decisions to be made quickly.
4Reliability
If signature-based detection is used, then known malware is detected, but false positives occur
Solution Approach 1:
The patent implements dynamics by using adaptive baselines that evolve with system usage patterns. Instead of static signatures that may incorrectly match legitimate software, the system continuously learns normal operational patterns and adjusts its behavioral expectations dynamically. This enables it to distinguish between legitimate software performing unusual actions and actual malware, reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and system responses are used to refine baseline patterns over time. When legitimate software is flagged, the system learns from this feedback and adjusts its behavioral thresholds. This continuous feedback loop improves measurement precision by reducing false positives while maintaining high malware detection accuracy.
Data Source
AI summary
Approaches for detecting and rectifying the malware in the computing systems are described. In an example, a request by a process or is intercepted by the malware detection module. Relevant information and characteristics pertaining to the request are extracted and on the based on the extraction, operational attributes are generated. These extracted operational attributes are analyzed and compared with the baseline attributes and if there are any anomalies present, the susceptible code or process originating from the intercepted request is ascertained as malicious.