Network Security System Using ML Deception and Rerouting
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Solution Overview
Problem
Current cybersecurity solutions lack a holistic approach and fail to effectively address new and emerging cyber threats, as they do not continuously analyze and interpret new data to provide granular, custom protection for specific IT environments, leading to vulnerabilities in network security.
Innovation Solution
A method that collects, aggregates, and analyzes structured and unstructured data using machine learning to identify trends and patterns, generating custom network security systems that deceive malicious entities and reroute threats to a quarantine zone, leveraging decoys and sensors to protect against internal and external threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current cybersecurity solutions are used, then basic network protection is provided, but they fail to effectively address new and emerging cyber threats due to lack of continuous data analysis and customization
Solution Approach 1:
The system implements dynamic security measures by continuously collecting, analyzing, and adapting to new threat data. Machine learning models are trained on evolving threat patterns, enabling the security system to dynamically adjust its protection strategies against new and emerging cyber threats rather than relying on static defense mechanisms.
Solution Approach 2:
The security system performs self-learning and self-improvement through automated machine learning processes. The system automatically collects security data, analyzes threat patterns, trains models, and generates customized security measures without requiring manual intervention for each new threat type, enabling it to serve itself in adapting to evolving cyber threats.
2Adaptability or versatility
If generic cybersecurity solutions are deployed, then broad coverage is achieved, but granular, custom protection for specific IT environments is lacking
Solution Approach 1:
The system automatically discovers the specific IT environment by collecting data from various sources, analyzes the unique security requirements and threat patterns, and generates customized security measures tailored to that environment. This self-service approach eliminates the need for manual customization while providing granular, environment-specific protection.
Solution Approach 2:
The system changes security parameters and configurations based on the analyzed characteristics of the specific IT environment. Machine learning models adjust security thresholds, detection rules, and response strategies to match the unique parameters of each environment, enabling custom protection without manual configuration complexity.
3Measurement precision
If more data is collected and analyzed to improve threat detection, then detection accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction during data collection, preparing data in advance for analysis. Machine learning models are pre-trained on historical threat data, enabling faster real-time detection without sacrificing accuracy. This preliminary preparation reduces the computational burden during actual threat detection, minimizing processing time delays.
Data Source
AI summary
A software threat analysis, detection and containment system includes a data aggregation model that receives and aggregates data from a plurality of sources in a computer network, a classification engine that classifies the aggregated data, and a plurality of data sets into which the classified data is stored. A model creation engine creates threat models based on the content of each data set and a prediction and analysis engine generates actionable information and predictions based on the content of each threat model.