Network Threat Mitigation via Feature Vector Analysis
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Solution Overview
Problem
Existing network security systems struggle to detect and prevent spearphishing attacks, which are targeted and harder to filter due to their personalized nature, often evading honeypots and standard filtering mechanisms.
Innovation Solution
A method involving the generation of feature vectors based on network destinations, training models using these vectors and indicators, and applying these models to monitor and block access to potentially malicious network destinations, including spearphishing attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If honeypots and standard filtering mechanisms are used, then indiscriminate phishing emails can be detected and blocked, but targeted spearphishing attacks cannot be effectively detected or prevented
Solution Approach 1:
The patent segments the detection approach into two distinct components: a general honeypot system for catching indiscriminate phishing and a specialized spearphishing detection system using feature vectors and machine learning models. Each component is optimized for its specific attack type, resolving the contradiction between detecting common attacks and adapting to targeted attacks.
Solution Approach 2:
The patent implements dynamic adaptation by continuously training machine learning models with new feature vectors extracted from network destinations. The system evolves over time to recognize new spearphishing patterns, making it adaptable to targeted attacks while maintaining reliability through the stable honeypot foundation.
2Reliability
If feature vectors and machine learning models are used to detect spearphishing, then detection accuracy for targeted attacks improves, but system complexity increases
Solution Approach 1:
The patent creates a multi-functional system where the feature vector extraction and machine learning models serve multiple purposes: detecting spearphishing attacks, analyzing network destinations, and providing intelligence for both blocking and notification actions. This universal approach reduces overall system complexity by consolidating functions.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation layer between raw network destination data and the machine learning models. This intermediary simplifies the complexity by transforming complex network data into standardized numerical vectors that are easier to process and compare.
3Reliability
If network browsing is monitored and models are continuously trained, then false positives are reduced and user protection is enhanced, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models with extensive feature vector data before deployment. This preliminary training reduces the need for continuous heavy processing during operation, as the models are already optimized for recognition tasks, thereby reducing real-time processing time while maintaining low false positive rates.
Solution Approach 2:
The patent implements partial monitoring by focusing computational resources on analyzing specific features of network destinations that are most indicative of spearphishing attacks. Rather than monitoring all aspects of network browsing equally, the system selectively processes relevant features, reducing overall processing time while maintaining detection accuracy.
Data Source
AI summary
A system and method for preventing access to potentially malicious network destinations. The method includes determining a plurality of network destinations and indicators of the plurality of network destinations including an indicator of a first network destination. A plurality of feature vectors are generated based on the plurality of network destinations including a first feature vector based on the first network destination. Access by a user via a computing device to a second network destination is detected. A second feature vector is generated, and an indicator is determined based on the second network destination. The second feature vector is compared to the plurality of feature vectors. The access by the user to the second network destination is blocked based on the indicator of the first network destination, the indicator of the second network destination, and the comparison of the second feature vector to the plurality of feature vectors.