Hardware-Accelerated Memory Analysis for Malicious URL Detection
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Solution Overview
Problem
Existing security solutions for detecting malicious activities such as ransomware, malicious URLs, and DGA malware are insufficient as they rely on static analysis and external monitoring, failing to detect new unknown malware and encrypted or obfuscated threats, and are evaded by malware.
Innovation Solution
A hardware-accelerated security service on an integrated circuit uses a machine learning detection system to extract features from memory data, employing random-forest, binary, and two-stage classification models to detect malicious activity in real-time, even when malware is encrypted or obfuscated, and before it establishes connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static analysis and external monitoring are used to detect malicious activities, then existing security solutions can identify known threats, but they fail to detect new unknown malware and encrypted or obfuscated threats
Solution Approach 1:
The system performs preliminary analysis of memory contents before malicious activities are fully executed or connected externally. By extracting and analyzing features from memory data in advance, the system can identify malicious patterns (such as DGA domain names, ransomware encryption functions, or malicious URL structures) before they establish external connections or complete their harmful actions, enabling early detection of unknown and encrypted threats
Solution Approach 2:
The patent replaces traditional mechanical/static analysis methods with machine learning-based detection models that can process complex, encrypted, and obfuscated data. The ML models (including random forest, binary classification, and two-stage classification models) analyze memory features dynamically, substituting conventional signature-based detection with intelligent pattern recognition that adapts to new malware variants and encryption methods
2Measurement precision
If traditional security solutions are used, then known malware can be identified, but detection rates are insufficient and malware can evade detection
Solution Approach 1:
The system continuously monitors memory contents and provides feedback to the machine learning detection models in real-time. By analyzing memory data streams and comparing detected features against trained models, the system can dynamically adjust detection thresholds and improve accuracy over time, enabling progressive enhancement of detection rates and accuracy against evolving malware threats
Solution Approach 2:
The patent changes the detection parameters from static signatures to dynamic memory feature extraction. By monitoring multiple parameters simultaneously (such as memory addresses, data patterns, execution contexts, and temporal relationships), the system can identify malicious activities with higher precision and adapt to evasion techniques that modify single signatures while maintaining malicious behavior patterns
3Speed
If real-time memory analysis is performed to detect malicious activity early, then detection speed improves, but processing complexity and resource requirements increase
Solution Approach 1:
The system segments the memory analysis process into distinct stages: memory data extraction, feature extraction, classification modeling, and detection output. By dividing the complex real-time analysis into manageable segments handled by specialized components (memory managers, feature extractors, classification models), the system achieves high detection speed while managing processing complexity through modular architecture
Solution Approach 2:
The system performs partial analysis of memory data by focusing on specific high-value regions and patterns rather than analyzing every byte. The machine learning models are trained to identify the most discriminative features (such as specific memory patterns, address ranges, or data structures) that indicate malicious activity, enabling fast detection by concentrating computational resources on the most informative parameters rather than exhaustive analysis
Data Source
AI summary
Apparatuses, systems, and techniques for classifying a candidate uniform resource locator (URL) as malicious using a machine learning (ML) detection system. An integrated circuit is coupled to physical memory of a host device via a host interface. The integrated circuit hosts a hardware-accelerated security service to protect one or more computer programs executed by the host device. The security service extracts a set of features from data stored in the physical memory, the data being words in a candidate URL and numeric features of a URL structure of the candidate URL. The security service classifies, using the ML detection system, the candidate URL as malicious or benign using the set of features. The security service outputs an indication of a malicious URL responsive to the candidate URL being classified as malicious.