Machine Learning Threat Detection for Scalable Online Attack Prediction
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Solution Overview
Problem
The industry faces a shortage of security data scientists with dual expertise in machine learning and online threat detection, making it difficult to effectively monitor and analyze the exponential growth of online threats.
Innovation Solution
A plug-and-play platform utilizing machine learning techniques to automatically recognize suspicious patterns in internet traffic and registry data, comprising a threat plug and play platform, threat identification and detection engine, threat prediction engine, and threat correlation engine, enabling security researchers to analyze large volumes of data without scripting or coding skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of threat data is performed by security experts, then detection accuracy is improved, but productivity deteriorates due to the overwhelming volume of data and shortage of experts
Solution Approach 1:
The patent introduces machine learning models as an intermediary between raw threat data and human security experts. The system automatically processes and analyzes threat feeds, logs, and security data using trained ML models, producing structured outputs that experts can efficiently review. This intermediary layer handles the overwhelming data volume while maintaining detection accuracy through automated pattern recognition.
Solution Approach 2:
The patent replaces manual mechanical analysis by security experts with automated machine learning-based analysis systems. The ML models perform threat detection, correlation, and prioritization tasks that were previously done manually, dramatically increasing productivity while maintaining or improving detection accuracy through consistent automated processing of large datasets.
2Reliability
If more security data scientists are hired to handle big data, then threat detection capability is improved, but device complexity and cost worsen due to the difficulty of finding and retaining dual-expertise talent
Solution Approach 1:
The patent segments the complex dual-expertise role into separate functional components: data scientists who develop and train machine learning models, and security analysts who interpret results and respond to threats. This segmentation allows organizations to hire specialists in each area rather than requiring rare dual-expertise individuals, reducing organizational complexity while maintaining or improving threat monitoring capability.
Solution Approach 2:
The patent implements self-service capabilities through automated machine learning systems that perform data collection, processing, analysis, and alert generation without requiring constant human intervention. The system serves itself by automatically training models on new data, adapting to emerging threats, and providing structured outputs, reducing the need for large teams of dual-expertise security data scientists.
3Ease of operation
If traditional security analysis methods are used, then ease of operation is maintained, but productivity deteriorates due to the inability to scale with exponential threat growth
Solution Approach 1:
The patent creates a universal machine learning platform that handles multiple threat types, data formats, and analysis tasks through a single integrated system. The system can process various threat feeds, logs, and security data using the same ML infrastructure, providing both ease of operation through standardized interfaces and productivity through scalable automated processing that grows with threat volumes.
Data Source
AI summary
A method for monitoring online security threats comprising of a machine-learning service that receives data related to a plurality of features related to internet traffic metrics, the service then processes said data by performing operations selected from among: an operation of ranking at least one feature, an operation of classifying at least one feature, an operation of predicting at least one feature, and an operation of clustering at least one feature, and as a result the machine learning service outputs metrics that aid in the detection, identification, and prediction of an attack.