Threat Detection via Cadence Analysis and ML Classification
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Solution Overview
Problem
Conventional systems lack the capability to early detect and efficiently classify the behavior of targets operating on digital properties, leading to significant financial losses due to delayed detection of malicious activities.
Innovation Solution
A multi-phase machine learning model is implemented to analyze log activities, generate cadence data, and compare it to predefined background data to identify new threat targets, classify their behavior, and apply mitigation algorithms to block or shape traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional detection systems are used, then detection capability is limited to post-damage identification, but early detection capability is insufficient
Solution Approach 1:
The system performs preliminary analysis of cadence patterns and behavior characteristics before malicious damage occurs. By continuously monitoring and establishing baseline behavior patterns of targets, the system can early detect deviations indicating malicious intent, enabling intervention before significant damage happens to digital properties
Solution Approach 2:
The detection system is divided into multiple independent modules: cadence analysis module, behavior pattern recognition module, classification module, and mitigation module. Each module processes specific aspects of target behavior independently, allowing early detection through cadence analysis while maintaining accurate classification through specialized behavior recognition algorithms
2Measurement precision
If comprehensive behavior analysis is performed, then classification accuracy is improved, but system complexity increases
Solution Approach 1:
Different modules of the system apply different levels of analysis complexity appropriate to their function. The cadence analysis module uses simple temporal pattern recognition, while the behavior classification module applies more sophisticated algorithms only when needed. This localized application of complexity maintains accuracy while reducing overall system burden
Solution Approach 2:
The system introduces intermediate representation layers including cadence data structures and behavior feature vectors that bridge raw log data and final classification decisions. These intermediaries organize complex information into structured formats, making subsequent analysis more efficient and accurate without requiring the entire system to handle full complexity simultaneously
3Loss of time
If early detection methods are implemented, then detection timing is improved, but false positive rate may increase
Solution Approach 1:
The system continuously monitors target behavior and compares observed cadence patterns against established baselines and known threat profiles. Feedback loops adjust detection thresholds and refine behavior models based on confirmed threats and false positive corrections, improving reliability over time while maintaining early detection capability
Solution Approach 2:
The system dynamically adjusts detection parameters including cadence thresholds, time windows, and confidence levels based on contextual information and threat intelligence. By changing parameters adaptively rather than using fixed thresholds, the system maintains high detection sensitivity while reducing false positives through context-aware adjustment
Data Source
AI summary
Various methods, apparatuses/systems, and media for detecting a target behavior are disclosed. A processor implements a machine learning cadence model that implements an algorithm to obtain, on a per session basis, cadence data that indicates average time between each call and a standard deviation of times across each call across all active sessions of a desired target. The processor compares the cadence data to predefined background cadence data to identify whether the desired target is a new threat target or a background traffic; generates an internet protocol (IP) address of the new threat target; inputs the IP address of the new threat target into a machine learning behavior model that implements an algorithm to generate a fingerprint of all known places that the new threat target is operating; and applies a mitigation algorithm to all active sessions of the new threat target.