Platform-Independent Malware Detection via Memory Snapshot Analysis
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Solution Overview
Problem
Existing malware detection systems are inefficient and resource-intensive, requiring frequent updates with operating system changes and struggling to detect both known and unknown threats, especially since they rely on the monitored device's operating system, leading to significant economic and technical burdens.
Innovation Solution
A platform-independent malware detection system that captures memory snapshots to generate datasets, processes them using machine learning and deep learning models, and applies feature extraction techniques to identify infections without relying on the monitored device's operating system, enabling real-time detection and quarantine of malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing malware detection systems rely on the monitored device's operating system, then they can detect malware, but they require frequent updates with each operating system change and consume significant resources
Solution Approach 1:
The detection system is segmented into two independent parts: the monitoring agent running on the monitored device and the central detection server. The agent captures memory snapshots and transmits them to the server for analysis, separating the detection logic from the monitored device's operating system. This eliminates the need for updates tied to OS changes while maintaining detection capability.
Solution Approach 2:
A platform-independent detection server acts as an intermediary between multiple monitored devices and the malware detection logic. The server receives memory snapshots from devices, processes them using machine learning models, and returns detection results. This intermediary architecture allows centralized updates independent of individual device operating systems.
2Reliability
If existing malware detection systems rely on the monitored device's operating system, then they can detect malware, but they consume resources reducing the efficiency and performance of the monitored device
Solution Approach 1:
The heavy computational workload of malware detection is extracted from the monitored device and transferred to a centralized detection server. The monitoring agent on the device only performs lightweight memory snapshot capture and transmission, while complex analysis using machine learning models occurs on the server. This extraction eliminates resource consumption issues on monitored devices while maintaining detection reliability.
3Reliability
If existing malware detection systems use traditional detection methods, then they can detect known malware, but they struggle to detect unknown threats and require constant updates
Solution Approach 1:
The system transitions from signature-based detection parameters to machine learning-based detection parameters. Instead of matching known malware signatures, the system uses trained models to analyze memory snapshot features and detect both known and unknown threats based on behavioral patterns. This parameter change enables detection of previously unseen malware without requiring updates for each new threat.
Solution Approach 2:
The system creates a copy of the device's memory state through snapshots, allowing analysis of the memory contents without executing or interacting with potentially malicious code. This copying approach enables safe analysis of unknown threats and eliminates the need for updates, as the detection logic analyzes static memory images rather than relying on OS-specific signatures.
Data Source
AI summary
A system and method (referred to as the system) detect malware by training a rule-based model, a functional based model, and a deep learning-based model from a memory snapshot of a malware free operating state of a monitored device. The system extracts a feature set from a second memory snapshot captured from an operating state of the monitored device and processes the feature set by the rule-based model, the functional-based model, and the deep learning-based model. The system identifies identifying instances of malware on the monitored device without processing data identifying an operating system of the monitored device, data associated with a prior identification of the malware, data identifying a source of the malware, data identifying a location of the malware on the monitored device, or any operating system specific data contained within the monitored device.