Adaptive Network Security System Using Unsupervised Behavioral Modeling
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Solution Overview
Problem
Current network security solutions are vulnerable to attacks due to static configurations and inability to adapt to changing network conditions and new attack variants, such as 'Zero-day' attacks, as they do not automatically update or dynamically adjust rules based on real-time behavior analysis.
Innovation Solution
An Adaptive Network Security System (ANSS) utilizing unsupervised machine learning to model expected behaviors across network data, dynamically generate and adapt security rules, and respond to anomalous behavior, enabling detection and protection against unknown attacks without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static security rules are applied to all user equipment, then configuration and management become simpler, but the system cannot adapt to changing network conditions and new attack variants
Solution Approach 1:
The patent implements dynamic security rules that automatically adjust based on real-time network behavior analysis. The system transitions from static, pre-configured rules to dynamic rules that are continuously generated and updated through machine learning models analyzing network traffic patterns, enabling adaptation to changing conditions without manual intervention
Solution Approach 2:
The security system performs self-service by automatically generating security rules through unsupervised machine learning without requiring manual configuration or updates. The system autonomously analyzes network behavior, identifies anomalies, and creates adaptive security policies, eliminating the need for continuous human intervention while maintaining high adaptability
2Reliability
If manually configured security solutions are used, then implementation becomes easier, but the system cannot detect unknown or Zero-day attacks
Solution Approach 1:
The patent replaces manual security configuration and rule-based detection systems with unsupervised machine learning models. These automated models analyze network behavior patterns and generate security rules without human intervention, enabling detection of unknown attacks by identifying anomalous behaviors that deviate from learned normal patterns
Solution Approach 2:
The system implements continuous feedback loops where network traffic is analyzed, security rules are generated and applied, and the results feed back into the machine learning models for continuous improvement. This automated feedback mechanism enables the system to learn from new attack patterns and continuously enhance detection effectiveness without manual reconfiguration
3Reliability
If security rules are not regularly updated, then system operation becomes more stable, but the security solutions become vulnerable to new attacks
Solution Approach 1:
The patent implements continuous security rule generation and updating through automated machine learning processes. The system continuously analyzes network traffic, updates behavioral models, and generates new security rules in real-time, ensuring continuous protection against evolving threats without interruption or manual intervention
Solution Approach 2:
The system performs preliminary action by proactively generating security rules based on learned normal behavior patterns before attacks occur. The machine learning models continuously prepare updated security policies in advance, enabling the system to respond to new attack variants before they can compromise the network
Data Source
AI summary
Provided is an Adaptive Network Security System (“ANSS”) that receives a first set of network data, detects commonality in a set of parameters within the first set of network data using an unsupervised machine learning technique, and models an expected behavior based on the commonality in the set of parameters. The ANSS may determine a threat risk associated with a second set of network data based on an amount of deviation between the set of parameters from the second plurality of network data and the expected behavior, and may perform a particular action from different actions against the second set of network data in response to the set of parameters from the second set of network data deviating from the expected behavior and the threat risk.