Network Session Fingerprinting for AI-Assisted Cyberattack Detection
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Solution Overview
Problem
Existing cybersecurity systems struggle to detect AI-assisted cyberattacks due to their dynamic and evolving patterns, making rule-and-anomaly-based intrusion detection approaches inadequate.
Innovation Solution
The method involves converting one-dimensional network packet data into two-dimensional images using space-filling curves, applying AI algorithms to analyze these images for threat levels, and implementing countermeasures based on the detected threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-and-anomaly-based intrusion detection approaches are used, then detection of conventional attacks is possible, but detection of AI-assisted cyberattacks becomes inadequate due to dynamic patterns
Solution Approach 1:
The patent applies dynamics by transitioning from static rule-based detection to dynamic AI-driven detection. The system uses machine learning models that continuously learn from new attack patterns and adapt their detection strategies in real-time, enabling them to respond to evolving AI-assisted cyberattacks while maintaining high reliability in detecting conventional threats.
Solution Approach 2:
The patent changes the fundamental parameters of detection by moving from binary rule-matching to multi-dimensional feature analysis. The system extracts multiple features from network traffic, transforms them into visual representations, and analyzes them using AI algorithms that operate on transformed parameter spaces, enabling detection of sophisticated attacks that evade traditional parameter-based rules.
2Reliability
If AI algorithms are used to detect cyberattacks, then detection of dynamic patterns improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the detection process into distinct stages: packet data extraction, feature transformation into visual representations, AI-based analysis, and countermeasure application. This segmentation allows each stage to be optimized independently, with the visual transformation stage preparing data in advance for faster AI processing, thereby reducing overall processing time while maintaining high detection accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-processing network packet data into visual representations before AI analysis. This transformation of packet data into images or visual patterns enables the AI algorithms to process information more efficiently, reducing computation time while enhancing detection capability for dynamic attack patterns.
3Speed
If real-time packet data collection is performed, then detection speed improves, but data volume and processing complexity increase
Solution Approach 1:
The patent extracts only the most relevant features from network packet data for analysis. By identifying and extracting key characteristics such as traffic patterns, protocol behaviors, and anomaly indicators, the system reduces the data volume that requires processing while maintaining real-time detection speed. This selective extraction approach simplifies processing complexity compared to analyzing complete packet contents.
Data Source
AI summary
A method for detecting cyberattacks in network communications includes collecting packet data in real time during the network communication, extracting relevant data from the collected data, positioning each packet in the relevant data to develop a fingerprint by creating an image containing blocks, colouring a block at the block position based on a value of each byte transmitted in the packet data, finding, by an artificial intelligence (AI) algorithm, by using the fingerprint in the image, as a threat level of a potential cyberattack in the collected data, and applying a countermeasure to the network communication based on the threat level of the potential cyberattack.