Private-learned IDS for Isolated Networks
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Solution Overview
Problem
Isolated networks, such as those in government and critical infrastructure, face challenges in maintaining effective intrusion detection systems (IDS) due to the need for constant updates and external synchronization, which they cannot easily implement without specialized teams and external connections.
Innovation Solution
A machine learning-based IDS that operates independently within a private network, allowing administrators to adjust and improve detection capabilities without external updates, by learning from single examples of threats and adjusting detector parameters to minimize false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional IDS require constant updates and external synchronization to maintain detection efficacy, then detection accuracy is improved, but isolated networks cannot implement these updates without external connections and specialized teams
Solution Approach 1:
The IDS enables isolated networks to perform self-updates by autonomously generating security updates from internally detected threats. The system allows any user to submit threat samples, automatically trains detection models on these samples, and deploys updates without requiring external threat intelligence feeds or specialized security teams, thus resolving the contradiction between maintaining detection accuracy and operating in isolated networks.
2Adaptability or versatility
If isolated networks employ in-house security teams to create security updates internally, then update capability is improved, but system complexity and resource requirements increase significantly
Solution Approach 1:
The system automates the entire update creation process, allowing any user to contribute threat samples without requiring specialized security expertise. The automated model training and update deployment mechanisms eliminate the need for complex in-house security teams, reducing system complexity while maintaining update capability.
Solution Approach 2:
The system implements feedback loops where detection results and threat samples are continuously fed back into the model training process. This automated feedback mechanism replaces manual security analysis processes, reducing the complexity associated with human expert involvement while maintaining effective update generation.
3Reliability
If traditional IDS rely on external threat intelligence feeds, then threat detection coverage is improved, but networks that cannot connect externally cannot leverage these intelligence sources
Solution Approach 1:
The system generates its own threat intelligence internally by learning from threat samples submitted by any user within the network. This self-generated intelligence replaces external threat feeds, enabling isolated networks to maintain comprehensive threat detection coverage without requiring external connectivity to threat intelligence sources.
Solution Approach 2:
The system changes the source of threat intelligence from external feeds to internal user-submitted samples. By transforming the intelligence generation process from externally-dependent to internally-generated, the system maintains reliable threat detection coverage while adapting to networks with restricted external connectivity.
Data Source
AI summary
In one embodiment, a device in a network maintains a plurality of machine learning-based detectors for an intrusion detection system. Each detector is associated with a different portion of a feature space of traffic characteristics assessed by the intrusion detection system. The device provides data regarding the plurality of detectors to a user interface. The device receives an adjustment instruction from the user interface based on the data provided to the user interface regarding the plurality of detectors. The device adjusts the portions of the feature space associated with the plurality of detectors based on the adjustment instruction received from the user interface.