Process Reputation Module for Malicious Activity Detection
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Solution Overview
Problem
Current network security systems face challenges in effectively determining and managing the reputation of processes to detect and prevent malicious activities, such as malware infections and data exfiltration, which can lead to unauthorized access and exploitation of computer networks.
Innovation Solution
A communication system that includes a process reputation module to monitor and update the reputation of processes by merging reputations based on events like library loads, thread attachments, and network interactions, using a whitelist, blacklist, and reputation database to assign a trust level to processes, enabling real-time behavior analysis and detection of malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network security systems are used to monitor process behavior, then basic malware detection is possible, but the systems cannot effectively determine process reputation leading to unauthorized access and data exfiltration
Solution Approach 1:
The security system is segmented into distinct functional modules: a process reputation module that monitors individual process behavior, a reputation database that stores trust scores, and a whitelist/blacklist system. This segmentation allows the system to determine process reputation independently without requiring complete system redesign, thereby improving reliability while managing complexity.
Solution Approach 2:
The system performs preliminary actions by establishing baseline reputations for processes before malicious activities occur. The reputation database pre-stores trust scores and behavioral expectations for legitimate processes, enabling the system to quickly identify deviations indicative of malware without waiting for advanced threat detection, thus improving detection reliability.
2Measurement precision
If comprehensive process monitoring is implemented to detect malicious activities, then security detection capability is improved, but system performance and operational efficiency deteriorate
Solution Approach 1:
The monitoring system applies local quality by focusing computational resources on specific high-risk processes rather than uniformly monitoring all processes. The process reputation module dynamically adjusts monitoring intensity based on individual process reputations and behavioral patterns, applying stricter scrutiny only where needed, thereby improving detection precision while preserving overall system productivity.
Solution Approach 2:
The system implements partial monitoring by selectively applying comprehensive surveillance only to processes with suspicious reputations or behaviors. For trusted processes with established good reputations, the system reduces monitoring overhead, performing only essential checks. This partial action approach maintains high detection precision for malicious activities while minimizing impact on system productivity.
3Measurement precision
If real-time process reputation updates are performed by merging multiple reputation sources, then trust level assessment accuracy is improved, but computational overhead increases
Solution Approach 1:
The system implements periodic action by updating process reputations at scheduled intervals rather than continuously merging all available reputation sources in real-time. The process reputation module performs reputation merges at defined checkpoints or upon triggering events, reducing computational energy consumption while maintaining sufficiently precise trust level assessments for security purposes.
Solution Approach 2:
The system uses lightweight, disposable reputation data structures that can be quickly created, merged, and discarded. Rather than maintaining complex persistent reputation models requiring heavy computational resources for continuous updates, the system employs simplified reputation objects that enable fast merging operations with minimal energy consumption, achieving adequate precision for security decision-making.
Data Source
AI summary
Particular embodiments described herein provide for an electronic device that can be configured to identify a process running on the electronic device, assign a reputation to the process if the process has a known reputation, determine if the process includes executable code, determine a reputation for the executable code, and combine the reputation for the executable code with the reputation assigned to the process to create a new reputation for the process.