Cybersecurity Intelligence Hub for Retroactive Malware Reclassification
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Solution Overview
Problem
Current malware detection systems face performance degradation with increasing network traffic, particularly in detecting advanced or unknown malware, due to limited accessibility to cybersecurity intelligence, leading to inefficiencies in rapid detection, accuracy, and visibility of cyber-attacks.
Innovation Solution
A comprehensive cybersecurity platform with a cybersecurity intelligence hub that parses, formats, stores, manages, updates, analyzes, and distributes cybersecurity intelligence across a global data store, providing meta-information for rapid and accurate malware detection by consolidating verdicts from multiple sources and reducing repetitive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If malware detection devices are placed at the periphery of the enterprise network to analyze network traffic, then known malware can be detected successfully, but performance decreases and detection accuracy drops when network traffic increases due to limited accessibility to cybersecurity intelligence
Solution Approach 1:
The patent introduces a centralized cybersecurity intelligence hub as an intermediary that aggregates threat intelligence from multiple sources and distributes it to malware detection devices. This hub acts as a mediator that enables detection devices to access comprehensive cybersecurity intelligence without being burdened by the complexity of collecting and analyzing intelligence from multiple sources independently, thereby maintaining detection accuracy while improving overall system productivity
Solution Approach 2:
The cybersecurity intelligence hub provides multi-functional capabilities by consolidating intelligence aggregation, analysis, verification, and distribution functions into a single centralized system. This universal platform serves multiple malware detection devices simultaneously, enabling them to all access the same verified intelligence and work together more efficiently without duplicating intelligence-gathering efforts
2Measurement precision
If malware detection devices conduct further analysis of suspicious information locally or through separate detection systems, then detection capability is maintained, but analysis time increases and repetitive analytics reduce efficiency
Solution Approach 1:
The cybersecurity intelligence hub performs preliminary analysis and verification of threat intelligence before distributing it to malware detection devices. By pre-processing and validating intelligence data centrally, the system eliminates the need for each detection device to independently verify every piece of intelligence, thereby maintaining detection accuracy while significantly reducing the time required for analysis
3Reliability
If centralized cybersecurity intelligence hub consolidates and verifies intelligence from multiple sources, then detection accuracy and visibility improve, but system complexity increases
Solution Approach 1:
The patent segments the cybersecurity system into distinct functional modules: intelligence sources, a centralized hub for aggregation and verification, and malware detection devices. This segmentation allows each component to perform its specific function independently, reducing the complexity burden on individual devices while maintaining high detection accuracy through the coordinated work of the centralized hub
Data Source
AI summary
A system for detecting artifacts associated with a cyber-attack features a cybersecurity intelligence hub remotely located from and communicatively coupled to one or more network devices via a network. The hub includes a data store and retroactive reclassification logic. The data store includes stored meta-information associated with each prior evaluated artifact of a plurality of prior evaluated artifacts. Each meta-information associated with a prior evaluated artifact of the plurality of prior evaluated artifacts includes a verdict classifying the prior evaluated artifact as a malicious classification or a benign classification. The retroactive reclassification logic is configured to analyze the stored meta-information associated with the prior evaluated artifact and either (a) identify whether the verdict associated with the prior evaluated artifact is in conflict with trusted cybersecurity intelligence or (b) identify inconsistent verdicts for the same prior evaluated artifact.