Pre-cognitive SIEM with Predictive Attack Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional intrusion detection systems (IDSs) are reactive and struggle to predict novel intrusions, detect anomalies over extended periods, and create rules from known attack patterns, making it challenging for network administrators to effectively respond to and mitigate attacks with minimal damage.
Innovation Solution
A pre-cognitive Security Information and Event Management (SIEM) system that combines clustering and classification models with predictive attack graphs to anticipate future network state transitions, using fuzzy clustering and artificial neural networks (FC-ANNs) to generate anomaly scores and severity scores, and implement ephemeral rules to block malicious traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intrusion detection systems are used to monitor network activities, then malicious activities can be detected, but the systems are reactive and cannot predict novel intrusions or anomalies over extended periods
Solution Approach 1:
The system performs preliminary actions by building predictive attack graphs that map potential attack paths before actual attacks occur. The FC-ANN model trains on historical security data to predict future anomalies and attack patterns, enabling the system to prepare defensive measures in advance rather than reacting after detection
Solution Approach 2:
The system implements continuous feedback loops where detected anomalies and attack patterns are fed back into the FC-ANN model for retraining and refinement. This feedback mechanism improves prediction accuracy over time and allows the system to adapt to new threat patterns, resolving the contradiction between reliable detection and adaptive prediction
2Measurement precision
If intrusion detection systems monitor all network activities, then security violations can be identified, but false positives occur and response time is delayed
Solution Approach 1:
The system takes preliminary anti-action by predicting potential attacks before they fully materialize. The predictive attack graphs identify vulnerable paths and the FC-ANN model forecasts anomalies, allowing security teams to implement countermeasures before actual attacks occur, thus reducing both false positives and response time
Solution Approach 2:
By pre-computing attack graphs and training prediction models on historical data, the system prepares analytical frameworks in advance. This preliminary action enables rapid real-time analysis of incoming security events without requiring extensive computation during incident response, reducing loss of time
3Adaptability or versatility
If complex predictive models are implemented to predict attack paths, then future vulnerabilities can be identified, but system complexity increases
Solution Approach 1:
The system segments the complex prediction task into distinct modular components: attack graph generation module, FC-ANN prediction module, anomaly detection module, and response recommendation module. Each module handles a specific aspect of attack prediction, making the overall complex system manageable and maintainable while preserving predictive capabilities
Data Source
AI summary
According to an example, pre-cognitive SIEM may include using trained classifiers to detect an anomaly in input events, and generating a predictive attack graph based on the detected anomaly in the input events. The predictive attack graph may provide an indication of different paths that can be taken from an asset that is related to the detected anomaly to compromise other selected assets in a network of the asset, and the other selected assets may be selected based on a ranking criterion and a complexity criterion. A rank list and a complexity list may be generated. The rank list, the complexity list, a depth of the predictive attack graph, and a weighted value may be used to generate a score that provides an indication of a number of assets that can be compromised and a difficulty of exploiting vulnerabilities related to services of the assets that can be compromised.