Cyber Threat Identification Analytics for Malware IOC Grouping
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Solution Overview
Problem
Incident responders face difficulties in identifying and managing cyber threats due to the vast number of malware variants, making it impractical to manually inspect each for potential threats, which delays response times and increases the risk of damage during cyber-security incidents.
Innovation Solution
The Cyber Threat Identification and Analytics (CTIA) system automatically identifies and compiles a list of relevant indicators of compromise (IOCs) by analyzing behavioral malware data, grouping statistically relevant IOCs without human intervention, allowing for quicker threat identification and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of each malware variant is performed, then threat identification accuracy is improved, but response time increases and productivity decreases
Solution Approach 1:
The system creates a virtual copy of the malware analysis process by using behavioral malware data and indicators of compromise (IOCs) to represent actual malware threats. Instead of manually inspecting each malware variant, the system analyzes copied behavioral patterns and IOCs from previously detected malware, enabling rapid identification of new threats without direct manual inspection of each variant.
Solution Approach 2:
The system introduces an intermediary layer of behavioral malware data and IOCs between the manual inspection process and the actual malware variants. This intermediary contains aggregated threat information that can be quickly analyzed to identify new malware, serving as a mediator that enables fast response while maintaining identification accuracy through comprehensive threat data.
2Measurement precision
If manual inspection of each malware variant is performed, then threat identification accuracy is improved, but the workload and complexity increase
Solution Approach 1:
The system extracts essential threat information from the complex malware variants by isolating behavioral patterns and IOCs. This extraction process removes unnecessary complexity while retaining the critical identification features, allowing analysts to focus only on the extracted IOCs rather than the full complexity of each malware variant.
Solution Approach 2:
The system segments the malware analysis task into separate components: behavioral malware data collection, IOC extraction, and threat identification. This segmentation divides the complex workload into manageable segments that can be processed independently and automatically, reducing overall complexity while maintaining identification accuracy.
3Measurement precision
If comprehensive malware data is collected and analyzed, then threat identification accuracy is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary action by pre-collecting and pre-processing behavioral malware data and extracting IOCs before actual threat identification is needed. This preliminary preparation creates a ready-to-use database of threat indicators that can be quickly queried and analyzed, eliminating the need for time-consuming real-time analysis of comprehensive malware data during incident response.
Data Source
AI summary
The cyber threat identification and analytics (“CTIA”) apparatuses, methods and systems, for example, identify a list of relevant malware indicators of compromise (IOCs) during a cyber security incident. The CTIA system automatically groups relevant malware IOCs from all known samples of a particular threat, given either a threat or a specific IOC without knowing the threat. In this way, an incident responder can use the group of relevant malware IOCs to have the highest probability of locating infections of variations of malware of the particular threat.