Mobile Network Anomaly Detection Using Genetic Rule Evolution
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Solution Overview
Problem
Current mobile network systems face challenges in performance and security monitoring due to the difficulty in creating comprehensive rulesets for anomaly detection that need frequent updates as the network evolves, making it economically infeasible to detect non-performant or malicious activities effectively.
Innovation Solution
Implementing a system and method using genetic algorithms for anomaly detection in mobile networks, which generates random detectors, discards those matching known good data, and compares remaining detectors to live traffic to identify anomalies, employing a negative selection algorithm and continuous learning to adapt to network changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive rulesets are built for anomaly detection, then detection accuracy improves, but system complexity and maintenance cost increase
Solution Approach 1:
The system automatically generates detection rules through genetic algorithms without requiring manual ruleset creation. The algorithm self-evolves detection capabilities by selecting and optimizing rules from randomly generated candidates, eliminating the need for expert-driven ruleset maintenance while maintaining high detection accuracy
Solution Approach 2:
The ruleset is not static but dynamically evolves over time through genetic algorithm iterations. Rules are continuously optimized based on performance feedback, allowing the system to adapt to changing network conditions without manual intervention, thus reducing long-term complexity
2Reliability
If comprehensive rulesets are built for anomaly detection, then detection coverage improves, but update frequency and maintenance time increase
Solution Approach 1:
The genetic algorithm operates continuously in the background, constantly evolving and optimizing detection rules. This continuous evolution ensures comprehensive detection coverage is maintained without requiring periodic manual updates, as the system automatically adapts to new anomaly patterns as they emerge
Solution Approach 2:
The system incorporates performance feedback from detected anomalies to guide the genetic algorithm's rule evolution. Successful detection patterns are reinforced and propagated, while ineffective rules are eliminated, enabling the system to maintain comprehensive coverage through automated learning rather than manual updates
3Measurement precision
If manual ruleset updates are performed frequently, then detection effectiveness improves, but economic feasibility deteriorates
Solution Approach 1:
The patent replaces the manual mechanical process of ruleset creation and updating with an automated computational system based on genetic algorithms. This substitution eliminates labor costs associated with expert analysis and manual ruleset maintenance, making frequent updates economically feasible through automation
Solution Approach 2:
The system performs its own optimization and maintenance through automated genetic algorithm execution. By self-managing the evolution of detection rules without requiring external expert intervention, the system dramatically reduces operational costs while maintaining high detection effectiveness
Data Source
AI summary
Exemplary systems and methods are directed to anomaly detection on a mobile communication network. A computing device receives data samples formatted from at least an endpoint and/or an interface on the mobile network. The computing device uses an encoder to build a data model of the mobile network data using data included in one or more data fields of each received data sample. The data model including datasets that meet at least one of a security or performance specification. A set of detectors is generated from the data model by performing a negative selection to select one or more datasets that do not match data that meets the security or performance specification, as well as genetic algorithm operations if anomalous data is available. Data is extracted from the received data samples and compared to the set of detectors to determine whether the mobile network data includes an anomaly.