AI Malware Detection via Dimensionality Reduction
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Solution Overview
Problem
Current security systems are ineffective in detecting zero-day attacks, which exploit unknown security vulnerabilities in computer software, as they rely on known malicious activity data and lack effective detection methods for unknown threats.
Innovation Solution
The implementation of a deep learning-based system that processes network traffic flow data to identify potentially malicious hosts by converting high-dimensional representations into low-dimensional representations using techniques like t-distributed stochastic neighbor embedding and principal component analysis, and then classifying them using k-nearest neighbor clustering, allowing for automatic remedial actions such as firewall configuration to block malicious communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security systems use known malicious activity data from threat intelligence feeds, then detection of known threats is improved, but detection of zero-day attacks deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing network traffic flow data before zero-day attacks occur. Deep learning models are pre-trained on historical flow data to establish baseline patterns of normal and malicious traffic. This preliminary preparation enables the system to detect zero-day attacks by comparing new traffic patterns against learned representations, rather than waiting for threat intelligence feeds to update with known signatures.
Solution Approach 2:
The patent introduces flow representation as an intermediary between raw network traffic and detection decisions. Instead of directly comparing traffic against known threat signatures, the system converts traffic into high-dimensional flow representations using deep learning, then transforms these into low-dimensional representations for comparison. This intermediary representation layer enables detection of previously unseen attacks by capturing underlying patterns rather than relying on explicit threat knowledge.
2Adaptability or versatility
If deep learning models process high-dimensional flow data, then detection capability for zero-day attacks is improved, but computational complexity increases
Solution Approach 1:
The system applies dimensionality change by transforming high-dimensional flow representations into low-dimensional representations through deep learning models. This dimensional transformation reduces computational complexity while preserving essential patterns for zero-day attack detection. The model learns to map complex multi-dimensional traffic features into a compressed representation space that maintains discriminative power for detecting novel threats.
Solution Approach 2:
The patent changes parameters by adjusting the dimensionalities of flow representations at different processing stages. High-dimensional representations capture detailed traffic characteristics for training, while low-dimensional representations reduce complexity for real-time detection. This parameter adjustment allows the system to balance between capturing sufficient detail for accurate detection and maintaining computational efficiency for practical deployment.
3Reliability
If the system blocks communication with potentially malicious hosts, then network security is improved, but false positives may increase
Solution Approach 1:
The system implements feedback by continuously monitoring network traffic and updating flow representations based on detected patterns. When potentially malicious hosts are identified, the system feeds this information back into the deep learning model to refine future detections. This feedback mechanism allows the system to learn from actual attack patterns and reduce false positives over time, improving security reliability while minimizing harmful impacts on legitimate traffic.
Data Source
AI summary
Implementations include receiving flow data representative of communication traffic of the network, determining that at least one blacklisted Internet protocol (IP) address is present in the flow data, and in response: providing a set of high-dimensional flow representations of network traffic by processing historical flow data through a deep learning (DL) model, providing a set of low-dimensional flow representations of the network traffic based on the set of high-dimensional flow representations, and labeling at least a portion of the set of low-dimensional flow representations to provide a sub-set of labeled low-dimensional flow representations and a sub-set of unlabeled low-dimensional flow representations, and identifying a host associated with an unlabeled low-dimensional flow representation as a potentially malicious host, and in response, automatically executing a remedial action with respect to the potentially malicious host.