APT Detection Center Using Segmented Analysis Modules
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Solution Overview
Problem
Traditional malware detection systems fail to effectively identify advanced persistent threats (APTs) despite recognizing embedded malware, as they do not account for the targeted and persistent nature of APTs, which seek to extract specific data from high-value targets.
Innovation Solution
An APT detection center is established to analyze objects for features indicative of APTs by comparing them against known APT, non-APT, and benign objects stored in an intelligence database, using static and dynamic analysis to classify and predict future threats, and generate analytics for prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional malware detection systems are used, then malware can be identified, but APTs cannot be properly detected because traditional systems do not account for targeted and persistent nature
Solution Approach 1:
The detection system is divided into multiple specialized modules: APT detection module, behavioral analysis module, threat intelligence module, and response module. Each module handles specific aspects of APT detection, allowing the system to precisely identify targeted attacks while maintaining overall adaptability through modular architecture.
Solution Approach 2:
The system employs dynamic behavioral analysis that adapts to different APT tactics and techniques. The detection mechanisms evolve based on observed patterns, updating threat models and detection rules in real-time to maintain effectiveness against persistent, targeted threats while remaining versatile against new attack vectors.
2Measurement precision
If comprehensive analysis is performed to detect APTs, then detection accuracy improves, but system complexity increases
Solution Approach 1:
Complex analysis tasks are segmented into discrete, manageable modules with specialized functions. The APT detection module focuses on targeted attack patterns, the behavioral analysis module handles execution monitoring, and the threat intelligence module manages indicator data. This segmentation achieves comprehensive detection accuracy while keeping individual components manageable and maintainable.
Solution Approach 2:
The system introduces intermediary components such as behavioral abstraction layers and threat intelligence intermediaries that bridge raw data and detection logic. These intermediaries simplify the complexity by standardizing data formats and detection interfaces, allowing comprehensive analysis without proportionally increasing system complexity.
3Speed
If real-time detection is implemented, then response time improves, but computational resources are consumed
Solution Approach 1:
The system applies partial analysis to most objects, performing comprehensive APT-specific detection only when indicators suggest targeted attacks. Behavioral analysis is applied selectively based on risk assessment, and full threat intelligence matching is performed only on suspicious objects. This approach enables real-time detection of APTs while conserving computational resources on benign traffic.
Solution Approach 2:
The system uses periodic sampling and event-driven analysis rather than continuous monitoring of all objects. Behavioral analysis triggers are based on specific events or time intervals, and threat intelligence updates are performed periodically. This periodic approach maintains real-time detection capability for APTs while reducing overall computational resource consumption.
Data Source
AI summary
A computerized method is described that is adapted to compare extracted features of a received object under analysis with one or more features associated with each known malicious object of a plurality of known malicious objects accessible to the one or more servers. Responsive to determining that the extracted features satisfy a prescribed level of correlation with the one or more features of a first known malicious object of the plurality of known malicious objects, identifying the received object as a malicious object. Also, responsive to determining that the extracted features fail to satisfy the prescribed level of correlation, conducting a second analysis that includes a comparison of the extracted features to the one or more features associated with each of the plurality of known malicious objects being of a type of malware other than malware targeting a specific entity.