ML Security Testing Network for New Vulnerability Detection
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Solution Overview
Problem
Existing network security systems are passive and unable to detect new security threats that have not yet been recognized, as they rely on predefined patches and are ineffective against emerging vulnerabilities.
Innovation Solution
A machine learning-based system that actively attempts security attacks, uses natural language processing to gather information about potential threats, and learns from the results to identify vulnerabilities, providing a trained model to users for proactive security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive vulnerability scanning using predefined patches is used, then known vulnerabilities can be detected, but new security threats cannot be detected
Solution Approach 1:
The system performs preliminary security attacks on a copy of the target network before actual attacks occur. By proactively attempting attacks and observing successful penetrations, the system identifies vulnerabilities before they are exploited by malicious actors, enabling preemptive patching and defense preparation.
Solution Approach 2:
The system uses machine learning to automatically learn from attack results and generate its own security models without human intervention. The ML algorithm processes attack outcomes, identifies vulnerability patterns, and updates the security model autonomously, enabling the system to adapt to new threats independently.
2Adaptability or versatility
If active security attack simulation is implemented, then new vulnerabilities can be identified, but system complexity increases
Solution Approach 1:
The system introduces a controlled testing environment with a copy of the target network as an intermediary. Security attacks are simulated on this replica rather than directly on the production network, allowing vulnerability identification while isolating potential damage and simplifying the actual production system.
Solution Approach 2:
The system replaces manual security testing with automated machine learning algorithms. Instead of human security analysts manually testing each vulnerability, the ML model automatically processes attack results, identifies patterns, and generates security models, significantly reducing operational complexity.
3Reliability
If machine learning is used to train security models, then proactive defense is enabled, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary training using historical attack data and simulated attacks before actual security incidents occur. By pre-training the machine learning model on past vulnerabilities and attack patterns, the system prepares defense mechanisms in advance, reducing real-time training requirements during actual security events.
Solution Approach 2:
The system discards ineffective attack approaches and unsuccessful training iterations, recovering only the successful learning patterns. The machine learning algorithm filters out redundant information from attack results, retaining only the essential vulnerability patterns needed for the security model, thereby reducing computational overhead.
Data Source
AI summary
A system and method for training a security model in a testing system using machine learning is disclosed. For example, the method includes accessing information about security attacks from at least one security attack information source, recognizing types of security attacks being attempted using natural language processing to process the information about security attacks, attempting at least one of the recognized types of security attacks on a network, identifying at least one security vulnerability based on results of the attempting, training the security model based on the identified at least one security vulnerability using machine learning, providing the trained security model to a user of the network, and blocking a security attack using the trained security model.