AI Cyber Threat Hypothesis Analysis for Novel Attack Detection
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Solution Overview
Problem
Legacy cyber security tools are inadequate in detecting evolving cyber threats due to their reliance on predefined rules and signatures, failing to recognize novel attacks and insider threats, and are unable to handle the vast amount of security information efficiently.
Innovation Solution
An AI cyber security analyst system that uses machine learning to identify abnormal behavior, form hypotheses on potential threats, gather and analyze data, and generate formalized reports to assist human analysts, automating the detection and reporting of cyber threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional legacy defense tools with predefined rules and signatures are used, then detection of known threats is possible, but detection of novel and evolving cyber threats fails
Solution Approach 1:
The system transitions from static predefined rules to dynamic machine learning models that continuously learn and adapt to new threats. The AI models are trained on historical security data and update their detection capabilities over time, enabling the system to detect both known and novel threats effectively.
Solution Approach 2:
The system changes the fundamental parameter of threat detection from signature-matching to behavioral analysis using machine learning. By transforming the detection approach from rule-based to AI-based, the system gains the ability to identify patterns indicative of novel threats while maintaining detection of known threats through learned behavioral patterns.
2Productivity
If manual analysis of security information is performed by human analysts, then detailed investigation is possible, but the vast amount of security information cannot be processed efficiently
Solution Approach 1:
The system introduces an AI intermediary that bridges the gap between raw security data and human analyst interpretation. The AI models process and analyze security information at scale, generating structured reports and hypotheses that human analysts can then review and investigate further, combining machine processing speed with human analytical depth.
Solution Approach 2:
The analysis process is segmented into distinct stages: automated AI analysis for initial processing and pattern recognition, followed by human analyst review for deep investigation and decision-making. This segmentation allows each component to focus on tasks where it excels, improving overall productivity while maintaining analysis precision.
3Productivity
If automated AI analysis is implemented, then processing speed and productivity improve, but system complexity increases
Solution Approach 1:
The AI system is designed as a multi-functional platform that performs diverse security analysis tasks including anomaly detection, threat classification, hypothesis generation, and report formatting. By consolidating multiple security functions into a single unified AI system, the complexity is managed more effectively than having separate tools for each function.
Solution Approach 2:
The system incorporates self-training capabilities where the AI models continuously learn from new security data and feedback. The automatic data gathering and model training processes reduce the need for manual configuration and maintenance, offsetting the initial complexity increase with long-term operational simplicity.
Data Source
AI summary
An analyzer module forms a hypothesis on what are a possible set of cyber threats that could include the identified abnormal behavior and/or suspicious activity with AI models trained with machine learning on possible cyber threats. The Analyzer analyzes a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models. A formatting and ranking module outputs supported possible cyber threat hypotheses into a formalized report that is presented in 1) printable report, 2) presented digitally on a user interface, or 3) both.