Hash Analytics for Cyberattack Prediction in Industrial Control Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Detecting and predicting malicious content and cyberattacks in industrial control systems (ICS) is challenging due to inadequate correlation of tools used during attacks and lack of infrastructure for inside cyberattacks, which often involve physical access and malicious content on portable storage devices.
Innovation Solution
The implementation of hash analytics in a system architecture that includes a secure media exchange node and a cyberattack prediction server, which generates file hashes and queries historical and predictive databases to analyze potential threats, providing detailed analysis and notifications for predicted cyberattacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional attack detection methods are used in ICS, then existing infrastructure can be leveraged, but correlation of attack tools and detection of inside cyberattacks is insufficient
Solution Approach 1:
The system performs preliminary actions by pre-building a knowledge base of attack tools, techniques, and procedures (TTPs) before attacks occur. Historical data from known cyberattacks is collected and stored, enabling the system to recognize and correlate attack patterns when they manifest, thereby improving detection reliability without requiring complex real-time analysis during the attack itself.
Solution Approach 2:
An intermediary knowledge base serving as a mediator is introduced between the ICS environment and the attack detection system. This knowledge base contains structured information about attack tools, techniques, and procedures, allowing the system to correlate observed behaviors with known attack patterns without directly analyzing complex attack tool interactions in real-time.
2Measurement precision
If comprehensive attack tree mapping is implemented, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The attack tree mapping is segmented into discrete, manageable components representing different attack phases and techniques. Each segment corresponds to a specific TTP that can be independently analyzed and correlated. This segmentation allows the system to achieve high prediction accuracy by combining results from individual segments without requiring the entire attack tree to be processed simultaneously, thereby reducing computational complexity.
Solution Approach 2:
The system transitions from analyzing attacks in a single dimension (individual file hash) to multiple dimensions by mapping attacks across different phases and techniques. The knowledge base stores attack information organized by multiple dimensions (reconnaissance, exploitation, access maintenance, exfiltration), allowing the system to correlate patterns across dimensions and improve prediction accuracy without proportionally increasing complexity.
3Reliability
If hash analytics are used for file analysis, then detection of malicious content improves, but infrastructure requirements increase
Solution Approach 1:
Instead of requiring direct analysis of all files in the ICS environment, the system creates a simplified copy of the analysis process through hash analytics. By computing hashes of files and comparing them against the knowledge base of known attack tools and techniques, the system achieves reliable malicious content detection without needing complex infrastructure for deep file inspection or execution analysis.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method, apparatus and computer program product for detecting malicious content and predicting cyberattacks are described herein. In the context of a method, the method receives a hash query comprising a file hash based on one or more files. The method queries a cyberattack case studies information database based on the hash query to generate one or more attack correlation information items associated with at least one of the one or more files. The method also generates and outputs a file security analysis based on the attack correlation information items for authorization of the one or more files.