Hidden Markov Attack Graphs for Detection-Evasive Sequences
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Solution Overview
Problem
Existing methods for generating attack graphs in information security lack the ability to accurately predict and emulate attack sequences that evade detection and prevention by security tools, thereby failing to effectively identify and address vulnerabilities in computer networks.
Innovation Solution
A method utilizing a hidden Markov model to generate attack graphs based on historical data, calculating sequences of techniques that are least likely to be detected or prevented, and emulating these sequences on target networks to expose vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional attack graph generation methods are used, then the attack graph can be constructed, but it fails to accurately predict attack sequences that evade detection and prevention
Solution Approach 1:
The patent transforms the attack graph generation from a static structural representation to a dynamic probabilistic model by introducing transition probabilities between attack techniques. The Hidden Markov Model parameters (transition probability matrix, emission probability matrix, initial probability distribution) are learned from historical attack data, enabling the system to predict attack sequences with varying likelihoods rather than treating all paths equally. This parameter-based approach directly addresses the contradiction by improving prediction accuracy through probabilistic modeling.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously learning from historical attack data to update the Hidden Markov Model parameters. The transition probabilities and emission probabilities are refined based on observed attack patterns, allowing the model to adapt to evolving threat landscapes. This feedback loop enhances both the prediction accuracy and reliability of vulnerability identification by incorporating real-world attack evidence into the model.
2Adaptability or versatility
If comprehensive attack sequences are predicted to identify all vulnerabilities, then security coverage is improved, but the complexity of analysis and computation increases
Solution Approach 1:
The patent applies parameter changes by utilizing the probabilistic nature of the Hidden Markov Model to prioritize attack sequences based on their likelihood. Instead of exhaustively analyzing all possible attack paths, the system focuses computational resources on high-probability sequences determined by the learned transition and emission probabilities. This approach maintains comprehensive security coverage by considering multiple attack paths while reducing computational complexity through probability-based filtering.
Solution Approach 2:
The system implements partial action by selecting and analyzing only the most probable attack sequences rather than exhaustively evaluating all possible paths. The Hidden Markov Model enables the system to identify a subset of critical attack sequences that are most likely to occur and evade detection, providing sufficient security coverage without the prohibitive computational cost of complete enumeration. This selective approach balances coverage and complexity effectively.
3Measurement precision
If historical attack data is utilized to improve prediction accuracy, then the model becomes more accurate, but the requirements for data processing and model training increase
Solution Approach 1:
The patent applies preliminary action by pre-training the Hidden Markov Model on historical attack data to establish the transition probability matrix, emission probability matrix, and initial probability distribution before deployment. This offline training phase processes the historical data once to learn attack patterns, and the resulting model can then rapidly predict attack sequences without requiring real-time data processing. This approach accumulates the data processing time upfront, enabling fast and accurate predictions during actual security operations.
Data Source
AI summary
A method includes: generating a transition probability matrix defining a set of transition probabilities for a set of techniques, each transition probability representing a probability of transitioning from a technique i to a technique j; defining a set of emission probability vectors corresponding to the set of techniques, each emission probability vector representing a probability of detecting a technique i and a probability of preventing a technique i; defining an initial technique vector representing an initial probability distribution of techniques; generating a hidden Markov model correlating a target sequence of observations with a hidden state sequence of techniques based on the transition probability matrix, the set of emission probability vectors, and the initial technique vector; and calculating a sequence of techniques, based on the hidden Markov model, exhibiting greatest probability to yield, for each technique in the sequence of techniques, absence of detection or prevention of the technique.