Cluster-Based Network Attack Mitigation via Decision Tree Segmentation
Solution Overview
Problem
Conventional methods for mitigating network attacks often result in collateral damage by incorrectly identifying valid data sources as attackers, leading to unnecessary blocking of legitimate traffic.
Innovation Solution
A cluster-based approach that uses a processor to detect unusual patterns in network data traffic, extracts signature parameters, assigns importance weights, builds a decision tree, and creates optimal clusters to selectively take mitigating actions, thereby distinguishing between legitimate and malicious data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods block data traffic from data sources exceeding the baseline, then attack mitigation is improved, but legitimate traffic is erroneously blocked causing collateral damage
Solution Approach 1:
The patent segments data sources into distinct clusters based on behavioral patterns rather than treating all exceeding-baseline sources uniformly. By dividing the data source population into clusters with similar characteristics, the system can apply differentiated mitigation strategies, blocking only malicious clusters while preserving legitimate traffic clusters that happen to exceed baselines for valid reasons.
Solution Approach 2:
The patent applies different quality characteristics to different clusters of data sources. Instead of uniform blocking, each cluster receives treatment appropriate to its identified behavior pattern. Malicious clusters receive blocking while legitimate clusters maintain normal flow, allowing local optimization of traffic handling based on cluster-specific properties rather than global uniform policy.
2Object-affected harmful factors
If the attack detector blocks data sources exceeding the baseline, then attack indicators are suppressed, but valid data sources are incorrectly identified as attackers
Solution Approach 1:
The system continuously monitors data source behavior and uses feedback from cluster performance to refine identification accuracy. By observing whether blocked clusters correspond to actual attacks and whether permitted clusters maintain legitimate functionality, the system can adjust cluster formation and mitigation policies to improve both attack detection precision and reduction of false positives.
Solution Approach 2:
The patent implements dynamic cluster formation and adjustment based on changing traffic patterns. Clusters are not static but evolve as new behavioral patterns emerge, allowing the system to adapt to new attack techniques while maintaining accurate identification of legitimate sources. This dynamic approach improves measurement precision by continuously refining the distinction between malicious and benign traffic.
3Difficulty of detecting and measuring
If traditional approaches block all data sources exceeding the baseline, then attack detection sensitivity is improved, but network service disruption increases
Solution Approach 1:
By segmenting data sources into clusters, the patent enables selective mitigation that maintains high detection sensitivity for attack clusters while preserving service continuity for legitimate clusters. This segmentation allows the system to achieve thorough attack detection without the blanket blocking that causes widespread service disruption.
Solution Approach 2:
The patent changes the parameter of mitigation application from uniform blocking to cluster-specific selective blocking. By adjusting which clusters receive blocking based on their identified characteristics, the system maintains high sensitivity to attacks while minimizing disruption to network services, effectively changing the mitigation parameter from all-or-nothing to differentiated response.
Data Source
AI summary
Provided are methods and systems for cluster-based mitigation of a network attack. A method for cluster-based mitigation of a network attack may commence with detecting an unusual pattern in network data traffic associated with data sources. The method may further include extracting signature parameters associated with the network data traffic. The signature parameters may be indicative of the network attack. The method may continue with assigning importance weights to the signature parameters based on historical signature data to generate weighted signature parameters. The method may further include building a decision tree for the data sources based on the weighted signature parameters. The method may continue with creating an optimal number of clusters for the data sources based on an analysis of the decision tree. The method may further include selectively taking at least one mitigating action with regard to the data sources within the clusters.