Network Traffic Anomaly Detection for Fraud Mitigation
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Solution Overview
Problem
Electronic and Internet-connected devices are inadequately protected against fraudulent and malicious activities, with conventional anti-virus or anti-malware software failing to provide complete protection, leading to risks such as identity theft, unauthorized access, and other harmful activities.
Innovation Solution
A system comprising a Data Collector and Mediator Unit, a Predictor Unit with Machine Learning capabilities, and a fraud and malicious activity mitigation unit, which monitors network traffic, detects anomalies, and triggers mitigation operations to classify and address fraudulent or malicious activities, using features extraction and classification of traffic patterns and user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anti-virus or anti-malware software is used, then basic protection is provided, but protection against fraudulent and malicious activities is inadequate
Solution Approach 1:
The system is divided into distinct functional modules: Data Collector and Mediator Unit for monitoring network traffic, Predictor Unit with Features Extractor and Machine Learning unit for analysis, and fraud and malicious activity mitigation unit for response actions. This segmentation allows each component to specialize in specific tasks, improving overall detection reliability while maintaining manageable complexity through modular design.
Solution Approach 2:
The Data Collector and Mediator Unit acts as an intermediary between network traffic and the analysis system. It monitors and collects traffic data before passing it to the Predictor Unit, enabling comprehensive protection without requiring direct integration of complex detection logic into every device, thus balancing protection effectiveness with system complexity.
2Measurement precision
If Machine Learning analysis is applied to detect anomalies, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The Features Extractor unit performs preliminary processing by extracting relevant features from raw network traffic data before passing them to the Machine Learning unit. This pre-processing step reduces the dimensionality and complexity of the input data, enabling faster and more efficient anomaly detection while maintaining high accuracy through focused feature analysis.
Solution Approach 2:
The system extracts only the most relevant features from network traffic data using the Features Extractor unit, rather than analyzing complete raw data. This extraction of essential characteristics maintains detection accuracy while significantly reducing computational burden and processing time required by the Machine Learning model.
3Reliability
If comprehensive mitigation operations are triggered, then protection coverage is enhanced, but system performance and user experience may deteriorate
Solution Approach 1:
The fraud and malicious activity mitigation unit applies mitigation operations selectively based on the specific anomalies detected by the Machine Learning unit. Rather than implementing blanket restrictions across all traffic, the system targets only the identified fraudulent or malicious activities with appropriate mitigation actions, maintaining comprehensive protection coverage while minimizing impact on legitimate system performance and user experience.
Data Source
AI summary
Systems, devices, and methods of protecting electronic or Internet-connected devices against fraudulent and malicious activities. A Data Collector and Mediator Unit monitors network traffic, and generates datasets of network traffic; each dataset includes network traffic monitored within a time-slot having a particular fixed time-length. A Predictor Unit includes a Features Extractor, to extract features from the datasets; and a Machine Learning (ML) unit, to run the extracted features through a ML model and to classify a particular traffic-portion as being either (I) an anomalous traffic-portion that is associated with fraudulent or malicious activity, or (II) a non-anomalous traffic-portion that is not-associated with fraudulent or malicious activity. The ML unit operates on both (i) anomalies in traffic patterns, and (ii) anomalies of user behavior and/or device behavior. A fraud and malicious activity mitigation unit activates mitigation operations for traffic-portions that were classified anomalous traffic-portions associated with fraudulent or malicious activity.