Anomaly Detection System for Network Traffic Analysis
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Solution Overview
Problem
Telecommunication service providers face challenges in detecting and mitigating malicious and fraudulent behavior on their networks, which can lead to unauthorized use, network overload, and user inconvenience, requiring continuous monitoring to quickly identify and address anomalies.
Innovation Solution
Anomaly detection systems that monitor network and user behavior, aggregate data to identify anomalous activity, and generate notifications to service provider personnel and users, allowing for rapid mitigation of potential issues without human intervention, thereby reducing network traffic and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of network behavior is implemented to detect malicious activities, then detection capability is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments network monitoring into multiple specialized components: anomaly detection logic that analyzes individual user behaviors, aggregation logic that combines data from multiple sources, and threshold logic that determines when anomalies constitute threats. This modular segmentation improves detection capability while managing system complexity through divided responsibilities.
Solution Approach 2:
The patent introduces an intermediary anomaly detection system that sits between raw network traffic and service provider response mechanisms. This intermediary aggregates and analyzes behavior data, transforming raw network observations into actionable anomaly detections, thereby simplifying the overall system architecture while enhancing detection reliability.
2Loss of time
If automated anomaly detection and notification systems are deployed, then response time to malicious activities is reduced, but false positives and user notifications increase
Solution Approach 1:
The system performs preliminary actions by continuously establishing baseline behaviors and pre-defining threshold levels for various anomaly types. When real-time behavior data is collected, it is immediately compared against pre-established thresholds, enabling rapid automated response while reducing false positives through pre-calibrated detection criteria.
Solution Approach 2:
The system implements feedback mechanisms where anomaly detections trigger notifications to service providers, who can then adjust thresholds and detection parameters based on actual threat patterns. This feedback loop refines the system over time, reducing false positives while maintaining rapid response capability for genuine threats.
Data Source
AI summary
A method may include monitoring calls and/or traffic on a network and identifying behavior associated with each of a plurality of user devices with respect to activity on the network. The method may also include aggregating information about the behavior associated with the user devices, determining whether the aggregated information corresponds to an anomaly with respect to usage of the network and determining, when the aggregated information corresponds to the anomaly, whether the anomaly meets a threshold based on a type of anomaly and a number of user devices affected by the anomaly. The method may further include identifying, when the aggregated information corresponds to the anomaly, user devices in an area corresponding to the anomaly, generating a notification in response to determining that the aggregated information corresponds to the anomaly and transmitting the notification to the identified user devices in the area corresponding to the anomaly.