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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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Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

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Computer Systems Science & Engineering 2023年第11期47卷 1919-1935页

作者： eungyu lee Yongsoo lee Taejin lee Department of Information Security Hoseo UniversityAsan 31499Korea

Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware *** models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection *** problem of robustness to adversarial attacks is a significant barrier to trustworthy *** various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial *** AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and *** evaluation indicators are necessary to evaluate the robustness of AI models against adversarial *** this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness *** uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation *** evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation ***,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial *** paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial *** SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for *** validate the proposed method,experiments were conducted using a malware *** adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data *** addition

关键词： AI robustness adversarial attack adversarial robustness robustness indicator trustworthy AI

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