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Explainable Artificial Intelligence-Based model Drift Detection Applicable to Unsupervised Environments

Computers, Materials & Continua 2023年第8期76卷 1701-1719页

作者： Yongsoo Lee Yeeun Lee Eungyu Lee Taejin Lee Department of Information Security Hoseo UniversityAsan31499Korea

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect ***,the rapidly changing data environment makes model life-cycle management after deployment ***-time detection of drift signals from various threats is fundamental for effectively managing deployed ***,detecting drift in unsupervised environments can be *** study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised *** proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current *** validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by *** on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world *** results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised *** proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment *** conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for *** proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection *** is versatile and suitable for various realtime data analysis contexts beyond DGA detection *** study significan

关键词： Cybersecurity machine learning(ML) model life-cycle management drift detection unsupervised environments shapley additive explanations(SHAP) explainability

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