Allot‘s patented system uses a Data Collector, Predictor Unit with Machine Learning, and fraud mitigation operations to protect electronic devices from malicious activities. The system includes a Machine Learning Re-Training Unit and an Auto-Encoder Unit with a Convolution Neural Network for data analysis and visualization. GlobalData’s report on Allot gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Allot, Network traffic analysis was a key innovation area identified from patents. Allot's grant share as of April 2024 was 60%. Grant share is based on the ratio of number of grants to total number of patents.

Fraud detection system for network traffic with machine learning

Source: United States Patent and Trademark Office (USPTO). Credit: Allot Ltd

A recently granted patent (Publication Number: US11943245B2) discloses a sophisticated system designed to detect and mitigate fraudulent or malicious activities in network traffic. The system comprises a Data Collector and Mediator Unit to monitor network traffic, a Predictor Unit with a Features Extractor and Machine Learning (ML) unit to classify anomalous traffic portions, a fraud and malicious activity mitigation unit, a Machine Learning Re-Training Unit, and an Auto-Encoder Unit with a Convolution Neural Network (CNN) to generate recurrent plot images. The system utilizes extracted features to classify traffic portions as anomalous or non-anomalous, based on anomalies in traffic patterns and user or device behavior. Additionally, the system includes a clustering unit to group datasets into discrete clusters based on traffic or behavioral anomalies.

Furthermore, the patent describes a method and a non-transitory storage medium implementing the system's functionalities, including monitoring network traffic, extracting features, running ML models for classification, triggering mitigation operations for anomalous traffic portions, and periodically re-training the ML model. The Auto-Encoder Unit generates recurrent plot images in a three-channel format, representing Internet access requests, frequent categories of sites, and suspicious categories. The system's hybrid Predictor Unit includes Traffic Patterns and User Behavior anomaly detector units to detect anomalies in traffic patterns and user navigation patterns. Overall, the patented system offers a comprehensive approach to identifying and addressing fraudulent or malicious activities in network traffic through advanced ML techniques and recurrent plot image generation.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.