Outlier Connection Detection System with Confidence-Based Phase Transition
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Solution Overview
Problem
Existing network security solutions face challenges in efficiently and accurately identifying outlier connections in real-time or near real-time, which can lead to false positives or missed security risks.
Innovation Solution
A method that involves identifying connections between client applications and services, transitioning from a learning phase to a detection phase based on a confidence level and the number of analyzed connections, and taking action to reduce security risks associated with outlier connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network security solutions are used to identify outlier connections, then security monitoring is performed, but false positives increase and detection accuracy decreases
Solution Approach 1:
The system performs preliminary learning during a learning phase to establish baseline connection patterns before actual outlier detection begins. This preliminary action of collecting and analyzing normal connection data enables the system to distinguish outliers more accurately without false positives, as the detection phase uses pre-established baselines rather than making decisions without sufficient context.
Solution Approach 2:
The system dynamically transitions between learning phase and detection phase based on confidence level thresholds. The outlier detection mechanism is adaptive, adjusting its sensitivity and decision-making criteria based on the accumulated knowledge from previously analyzed connections. This dynamic approach allows the system to optimize detection accuracy while maintaining reliability by adjusting detection parameters based on the confidence level achieved during learning.
2Speed
If real-time outlier detection is implemented, then security risks are identified quickly, but system complexity increases
Solution Approach 1:
The system segments the outlier detection process into distinct phases: learning phase for baseline establishment and detection phase for actual outlier identification. This segmentation allows real-time detection to occur using pre-computed baselines, reducing the computational complexity during the detection phase while maintaining fast response times. The complex learning process is separated from the simpler detection process.
Solution Approach 2:
The system performs complex analysis and baseline establishment in advance during the learning phase, before real-time detection begins. This preliminary action stores processed knowledge that enables rapid detection during the detection phase without requiring complex real-time computations, thus achieving fast detection speed with reduced system complexity during operation.
3Measurement precision
If the learning phase continues for extended periods to improve accuracy, then detection precision increases, but time loss increases
Solution Approach 1:
The system uses feedback from previously analyzed connections to dynamically assess confidence level and determine when the learning phase should end. As more connections are analyzed during the learning phase, the confidence level increases, and the system can automatically transition to the detection phase when sufficient precision is achieved. This feedback mechanism prevents unnecessarily long learning phases while ensuring adequate detection precision.
Solution Approach 2:
The system replaces fixed-duration learning phases with a confidence-level-based transition mechanism. Instead of using a predetermined time-based approach to learning phase duration, the system substitutes a dynamic assessment based on the confidence level derived from analyzed connection data. This substitution allows the learning phase to end precisely when sufficient data is collected, minimizing time loss while ensuring detection precision.
Data Source
AI summary
Techniques for identifying outlier application connections for computer security are described. These techniques include identifying one or more connections between a client application and one or more services, over a communication network, and determining to switch from an outlier connection learning phase to an outlier connection detection phase based on the identified or more connections, including determining, in real-time and based on a confidence level and a number of previously analyzed connections, to switch to the outlier connection detection phase. The techniques further include determining, based on the switch to the outlier connection detection phase, that a first connection of the identified one or more connections is an outlier connection, and acting to reduce a security risk relating to the first connection.