Neural Network Intrusion Detection for HTTP Data
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Solution Overview
Problem
Existing network security systems rely on signature-based approaches that fail to detect new or variant attacks, and suffer from high false negative intrusion detection rates, making them ineffective in preventing data leakage and malicious activity.
Innovation Solution
The implementation of a machine learning-based intrusion detection system using trained neural network models that processes HTTP data to extract feature values and generate attack vector arrays, enabling the detection of new, previously seen, and variant attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based approaches are used for intrusion detection, then the system can detect known attacks with exact matches, but the system cannot detect new attacks or variant attacks that modify command strings
Solution Approach 1:
The patent replaces the mechanical signature-matching system with a machine learning-based neural network system. Instead of using rigid pattern matching algorithms that require exact or near-exact matches to known attack signatures, the system employs trained neural networks that can learn and recognize attack patterns, including novel and variant attacks, by analyzing feature vectors extracted from network traffic data.
Solution Approach 2:
The patent transforms the intrusion detection approach by changing from fixed signature parameters to dynamic feature vectors. The system extracts multiple features from network traffic (such as packet characteristics, flow patterns, and behavioral metrics) and feeds these into the neural network, allowing the system to adapt its detection criteria based on learned patterns rather than predetermined signatures.
2Measurement precision
If network packet sniffing approaches are used, then the system can monitor network traffic, but the system suffers from high false negative intrusion detection rates
Solution Approach 1:
The patent segments the intrusion detection process into distinct functional components: a feature extraction module that processes raw network traffic into meaningful feature vectors, and a neural network module that performs the actual intrusion classification. This segmentation allows each component to be optimized independently, with the feature extraction focusing on capturing relevant traffic characteristics and the neural network focusing on pattern recognition and classification.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between raw network traffic and intrusion detection decisions. Instead of directly analyzing raw packets, the system extracts meaningful features (such as flow statistics, packet distribution patterns, and behavioral metrics) that serve as a bridge, enabling the neural network to make more accurate intrusion determinations with reduced false negatives.
3Ease of manufacture
If exact match checking is used for command strings, then the system can identify previously seen attacks, but the system fails to detect attacks with modified command strings
Solution Approach 1:
The patent replaces the simple exact-match checking mechanism with a sophisticated machine learning system. Instead of using straightforward string comparison operations, the system employs neural networks that can recognize attack patterns based on learned features, enabling detection of attack variants that modify command strings while maintaining the simplicity of implementation through automated model training.
Data Source
AI summary
An intrusion detection system that includes a processor configured to receive HyperText Transfer Protocol (HTTP) data and to convert the HTTP data into a data tree object that links field values from the HTTP data. The processor is further configured to identify a field value from the data object tree and to determine a set of feature values for the field value corresponding with input features for the neural network model. The processor is further configured to apply the determined set of feature values to the neural network model to generate an attack vector array. The attack vector array includes flag bits that each correspond with an attack type. The processor is further configured to trigger an event in response to determining that at least one flag bit is set.