Neural Network Intrusion Detection for Geolocation-Based Request Screening
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Solution Overview
Problem
Existing network security systems, particularly in cloud computing environments, face challenges in efficiently detecting and preventing network intrusions such as distributed denial-of-service (DDoS) attacks, while ensuring data privacy and security.
Innovation Solution
Implementing a network intrusion detection system using a neural network on a local computing system or cloud computing system that intercepts requests, analyzes geolocation and request rates, and employs a neural network module to identify legitimate or malicious traffic, preventing malicious requests from reaching the service provider system and providing early notifications to administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a cloud computing system is used to process large scale network traffic, then flexibility and scalability are improved, but security and data privacy are worsened
Solution Approach 1:
The patent introduces a neural network-based intrusion detection system as an intermediary layer between the network traffic and the cloud computing system. This intermediary analyzes traffic patterns, geolocation data, and request rates to identify and block malicious requests before they reach the cloud system, thus maintaining security while preserving the scalability benefits of cloud computing.
2Device complexity
If traditional intrusion detection methods are used, then implementation is simpler, but detection accuracy and ability to identify sophisticated attacks is worsened
Solution Approach 1:
The patent replaces traditional rule-based or signature-based intrusion detection mechanisms with a neural network-based system. The neural network learns complex patterns from training data and can identify sophisticated attacks that traditional methods would miss, achieving higher detection accuracy through machine learning instead of manual rule configuration.
3Measurement precision
If all incoming requests are analyzed in detail, then detection accuracy is improved, but processing speed and system performance are worsened
Solution Approach 1:
The patent implements a two-stage analysis approach where not all requests undergo full neural network analysis. Instead, the system first performs preliminary filtering based on obvious criteria, and only subjects suspicious requests to detailed neural network examination. This partial action approach maintains high detection accuracy for malicious traffic while preserving processing speed for legitimate requests.
Data Source
AI summary
A method includes intercepting requests. The requests are analyzed to identify authenticated requests. Remaining requests are identified as suspicious requests. The suspicious requests are grouped into request groups based on respective geolocation information. A first rate of requests is determined for a first request group. In response to determining that the first rate of requests is less than or equal to a request rate threshold, parameters of a first suspicious request of the first request group are analyzed to determining values of the parameters. In response to determining that the values of the parameters are not within respective acceptable parameter value ranges, the first suspicious request is analyzed using a neural network to identify if the first suspicious request is legitimate or malicious. In response to identifying that the first suspicious request is malicious, a first notification indicating that the first suspicious request is identified as malicious is send.