Endpoint Reputation Scoring for Malware Detection Accuracy
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Solution Overview
Problem
Advanced persistent threats (APTs) pose a challenge in detection and remediation due to their stealthy and continuous hacking processes, which existing technologies struggle to effectively address, especially in remote and orchestrated attacks.
Innovation Solution
The proposed solution involves detecting network traffic at a gateway to trigger queries on originating endpoints, using secure heartbeats to signal potential malware, and employing local and global reputation information for accurate malware detection, along with methods for intrusion detection and remediation, including whitelisting, antivirus engines, and behavioral-based protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection techniques are used, then detection capability is limited, but false positives increase and detection accuracy decreases
Solution Approach 1:
The patent implements dynamic reputation scoring that continuously updates based on observed behavior patterns. The system transitions from static signature-based detection to dynamic behavioral analysis, where reputation scores evolve over time as the system observes process activities, network connections, and system modifications. This dynamic approach allows the system to adapt to new malware variants while maintaining accuracy in detecting known threats.
Solution Approach 2:
The system performs continuous monitoring of endpoint processes and network traffic rather than periodic scanning. The reputation evaluation operates continuously, tracking process behaviors, communication patterns, and system changes in real-time. This continuous observation enables the system to detect malware activities as they occur and maintain up-to-date reputation assessments without interruption.
2Measurement precision
If advanced persistent threat detection methods are implemented, then detection capability improves, but system complexity increases
Solution Approach 1:
The patent divides the threat detection system into distinct modular components: endpoint agents that collect behavioral data, network gateways that analyze traffic patterns, reputation servers that maintain score databases, and response systems that execute remediation actions. Each component operates independently with well-defined interfaces, allowing the complex APT detection functionality to be implemented through coordinated simple modules rather than a monolithic complex system.
Solution Approach 2:
The system introduces reputation scores as an intermediary mechanism that simplifies complex threat assessment. Instead of directly analyzing all behavioral indicators and making complex decisions, the system translates diverse threat signals into standardized reputation scores that serve as intermediaries for decision-making. This intermediary layer abstracts the complexity of multi-factor analysis while maintaining detection capability.
3Measurement precision
If comprehensive monitoring and analysis are performed, then detection accuracy improves, but processing time and resource consumption increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on the most indicative behavioral signals rather than analyzing every possible parameter in equal detail. The reputation evaluation prioritizes key threat indicators such as unusual process behaviors, suspicious network connections, and anomalous system modifications. This selective focus achieves high detection accuracy while avoiding the time and resource costs of comprehensive analysis of all possible parameters.
Data Source
AI summary
A variety of techniques are disclosed for detection of advanced persistent threats and similar malware. In one aspect, the detection of certain network traffic at a gateway is used to trigger a query of an originating endpoint, which can use internal logs to identify a local process that is sourcing the network traffic. In another aspect, an endpoint is configured to periodically generate and transmit a secure heartbeat, so that an interruption of the heartbeat can be used to signal the possible presence of malware. In another aspect, other information such as local and global reputation information is used to provide context for more accurate malware detection.