ML-Based Intrusion Detection for Computing Environments
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Solution Overview
Problem
Current intrusion detection systems lack effective mechanisms to promptly identify and mitigate malicious activities in computing environments, leading to potential data theft and system compromise.
Innovation Solution
The implementation of a machine learning-based intrusion detection system that collects network data, uses unsupervised learning to identify intrusion types, and generates real-time remedial communications to mitigate threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intrusion detection mechanisms are used, then system security is maintained, but detection speed and accuracy are insufficient leading to delayed response to malicious activities
Solution Approach 1:
The patent replaces traditional rule-based and signature-based intrusion detection mechanisms with machine learning models that automatically learn patterns from network data. The system uses trained ML models to classify intrusions, substituting manual security analysis with automated intelligent detection that operates faster and with higher accuracy.
Solution Approach 2:
The system implements self-learning through machine learning models that automatically adapt to new intrusion patterns without requiring constant manual updates of security rules. The ML models continuously analyze network data and improve their detection capabilities autonomously, reducing the need for human intervention in maintaining detection accuracy.
2Measurement precision
If comprehensive network data analysis is performed to improve intrusion detection, then detection accuracy increases, but computational complexity and processing time increase
Solution Approach 1:
The patent pre-trains machine learning models using extensive network data before deployment. This preliminary training phase allows the models to learn complex patterns and relationships in advance, so that during actual intrusion detection, the system can quickly classify threats without performing exhaustive analysis in real-time, thus reducing operational complexity.
Solution Approach 2:
The system uses trained machine learning models that capture and replicate complex detection patterns. Once a model learns effective intrusion detection rules from training data, it creates a simplified copy of these complex relationships that can be rapidly applied to new network data without reprocessing the entire training dataset.
Data Source
AI summary
A method comprises collecting network data associated with data transmission in a computing environment. The method also comprises identifying, using one or more machine learning models, at least one intrusion type affecting the computing environment. The identification of the at least one intrusion type is based at least in part on the collected network data. In the method, one or more remedial communications addressing the at least one intrusion type are generated, and the one or more remedial communications are transmitted to a user.