Anomalous Communication Detection via Staging Network Segmentation
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Solution Overview
Problem
The learning phase in anomaly detection systems is vulnerable to intrusion during the initial construction or when normal communication states change, leading to challenges in ensuring the soundness of the learning phase and potential prolongation of the learning period.
Innovation Solution
A communication system that includes a staging network for testing and an operational network, where an initial model is generated in the staging network and imported to the operational network, allowing simultaneous execution of the detection and learning phases, thereby reducing the vulnerable period and ensuring secure detection of anomalous communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the learning phase is conducted to define normal communication states, then the detection accuracy of anomalous communication is improved, but the system becomes vulnerable to intrusion during the learning phase and the learning period is prolonged
Solution Approach 1:
The system divides the network into a staging network (first network) and an operational network (second network). The staging network is used exclusively for the learning phase to generate initial models in a controlled environment, while the operational network performs detection using these pre-generated models. This segmentation isolates the vulnerable learning phase from the production environment, preventing intrusion risks from affecting normal operations.
Solution Approach 2:
The system performs the learning phase and generates detection models in advance within the staging network before deploying them to the operational network. By completing the learning process preliminarily with trusted test communication, the system establishes a foundation for secure detection without exposing the operational network to the vulnerabilities of the learning phase.
2Measurement precision
If the learning phase is extended to ensure thorough learning of normal states, then the detection capability is improved, but the vulnerable period is prolonged and security risks increase
Solution Approach 1:
The learning phase is confined to the staging network environment, separating it from the operational network. This allows the learning process to proceed thoroughly without extending the vulnerable period in the production system, as the operational network uses pre-generated models for immediate detection.
Solution Approach 2:
The system completes the learning phase and model generation in advance within the staging network. By performing this preliminary action with trusted test communication, the system eliminates the need for an extended learning phase in the operational network, thereby reducing the vulnerable period while maintaining detection capability.
3Reliability
If the system uses a trusted environment for learning phase, then the soundness of learning is ensured, but the system complexity increases due to multiple networks
Solution Approach 1:
The system divides functionality into two networks: the staging network for learning and the operational network for detection. This segmentation ensures that learning occurs in a controlled trusted environment while keeping the operational network simple and focused on detection tasks.
Solution Approach 2:
The system creates an initial model in the staging network that replicates the detection capabilities needed in the operational network. By copying the learned patterns and rules from the trusted environment to the operational model, the system ensures soundness without requiring the operational network to perform learning functions.
Data Source
AI summary
A communication system including an operational network including a host and a learning and detection server, and a staging network including a host of the same type as the host, a test execution server, and a learning and detection server. The test execution server performs a communication test by transmitting test communication in a normal state to the host and receiving communication performed by the host. The learning and detection server learns the communication of the host, generates an initial model for detecting an anomalous communication of the host, and transmits the initial model to the learning and detection server. The learning and detection server learns the communication of the host and generates a model for detecting an anomalous communication of the host, while monitoring the communication of the host using the initial model received from the learning and detection server.