Network Attack Detection via Flow Signature Extraction
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Solution Overview
Problem
Cloud services face increasing frequency and severity of attacks, necessitating more effective detection methods for malicious network traffic without compromising user privacy or requiring access to individual devices.
Innovation Solution
The development of attack models using reinforcement learning, trained on IP flow data from active servers and honeypots, allows for the identification of malicious network traffic, enabling faster and more accurate detection without needing access to client devices or their logs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network security monitoring methods are used, then access to individual devices and logs is required, but this compromises user privacy and increases system complexity
Solution Approach 1:
The patent extracts only the necessary network flow signature data from the complete device logs, selecting specific features (source/destination IPs, ports, protocols, packet counts) while excluding all other device information. This extraction approach enables security analysis without accessing or storing private user data, resolving the contradiction between detection accuracy and privacy preservation
Solution Approach 2:
The patent segments the network traffic analysis into discrete flow signatures that can be independently analyzed. By breaking down network communications into standardized flow records with specific attributes, the system can detect attacks without needing to examine complete device logs or access individual devices, thus maintaining privacy while improving security monitoring
2Measurement precision
If comprehensive device access is required for security monitoring, then detection accuracy improves, but detection speed decreases due to data collection overhead
Solution Approach 1:
The patent performs preliminary classification of network flows by establishing baseline patterns of normal traffic before actual attack detection. The reinforcement learning model is pre-trained on extensive network flow data to recognize attack patterns, enabling rapid real-time detection without the need to collect and analyze complete device logs during the detection phase, thus improving both accuracy and speed
3Reliability
If detailed device logs are collected for attack detection, then detection accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential network flow signature fields needed for attack detection, excluding all other device-specific data. By selecting and analyzing only source/destination addresses, ports, protocols, and traffic volumes, the system achieves reliable attack detection while maintaining simplicity and reducing resource requirements
Solution Approach 2:
The patent creates a universal network flow signature format that can be applied across multiple devices and network configurations without device-specific customization. The reinforcement learning model processes standardized flow records from any source, making the system broadly applicable and reducing overall system complexity while maintaining high detection reliability
Data Source
AI summary
Enhancements to network security are provided by identifying malicious actions taken against servers in a network environment, without having to access log data from individual servers. Seed data are collected by an administrator of the network environment, from honeypots and servers whose logs are shared with the administrator, to identify patterns of malicious actions to access the network environment. These patterns of use include ratios of TCP flags in communication sessions, entropy in the use of TCP flags over the life of a communication session, and packet size metrics, which are used to develop a model of characteristic communications for an attack. These attack models are shared with servers in the network environment to detect attacks without having to examine the traffic logs of those servers.