[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120228-en":3,"doc-seo-120228-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120228,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","UNCERTAINTY-AWARE MACHINE LEARNING TECHNIQUES FOR SUSTAINABLE MALWARE DETECTION","Malware remains a major global cybersecurity threat, with vast numbers of new variants emerging rapidly, creating an urgent need for robust automated detection. Conventional approaches struggle to keep pace with evolving behavior, especially under concept drift, where malware characteristics change over time and degrade model performance. This thesis presents an uncertainty-aware malware detection framework using Gaussian processes to measure prediction uncertainty and improve reliability. By integrating Gaussian processes into machine learning, the method provides probabilistic confidence and supports selective abstention on highly uncertain decisions, reducing both false positives and false negatives. Experiments on two distinct mobile malware datasets show strong gains in accuracy and adaptability on Android.","UNCERTAINTY-AWARE MACHINE LEARNING TECHNIQUES FOR SUSTAINABLE MALWARE DETECTION  \nBy  \nDHEERAJ VURUKUTI  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nWASHINGTON STATE UNIVERSITY School of Electrical Engineering and Computer Science  \nDECEMBER 2024  \n© Copyright by DHEERAJ VURUKUTI, 2024 All Rights Reserved  \n© Copyright by DHEERAJ VURUKUTI, 2024 All Rights Reserved  \nTo the Faculty of Washington State University:  \nThe members of the Committee appointed to examine the thesis of DHEERAJ VURUKUTI find it satisfactory and recommend that it be accepted  \nJanardhan Rao Doppa, Ph.D., Co-Chair  \nHaipeng Cai, Ph.D., Co-Chair  \nYan Yan, Ph.D.  \nACKNOWLEDGMENT  \nI would like to take this opportunity to express my heartfelt gratitude to everyone who helped me during this thesis work. First and foremost, I extend my deepest thanks to my advisor, Dr. Janardhan Rao Doppa, whose unwavering guidance and encouragement have been invaluable over the past two years. His mentorship has been a source of inspiration, and I am grateful for his role as a model guide. I would like to thank Dr. Haipeng Cai for his invaluable insights and support throughout this thesis; his advice greatly enhanced the direction of my research. I would also like to thank Dr. Aryan Deshwal for his significant contributions and invaluable assistance during the course of this research, which played a vital role in shaping this work. Additionally, I am deeply grateful to Dr. Yan Yan for his time and thoughtful consideration in serving on my committee.  \nUNCERTAINTY-AWARE MACHINE LEARNING TECHNIQUES FOR  \nSUSTAINABLE MALWARE DETECTION  \nAbstract  \nby Dheeraj Vurukuti, M.S.  \nWashington State University  \nDecember 2024  \nCo-Chairs: Janardhan Rao Doppa and Haipeng Cai  \nMalware remains a significant global cybersecurity threat, with millions of new variants appearing rapidly, highlighting the urgent need for effective automated detection methods. Traditional malware detection systems face challenges in adapting to the rapidly evolving landscape, particularly due to concept drift, where malware characteristics change over time, leading to performance degradation. This thesis introduces an innovative framework for uncertainty-aware malware detection using Gaussian processes (GPs) to quantify prediction uncertainty and enhance the reliability of malware detection.  \nOur approach integrates GPs into machine learning models to address the limitations of existing malware detection techniques. By providing a probabilistic measure of confidence in predictions, it enables selective abstention from highly uncertain classification decisions, reducing false positives and negatives. We applied this framework to two qualitatively different mobile malware datasets.  \nThrough comprehensive evaluations, we demonstrate that our GP-based models significantly outperform traditional methods in accuracy and adaptability to evolving malware in Android. Extensive ablation studies validated our hypothesis on the effectiveness of uncer-  \ntainty quantification in improving the overall detection performance. Our results provide valuable insights into malware behavior dynamics and the importance of adaptive detection strategies.  \nOverall, this research contributes significantly to the development of robust and adaptable mobile malware detection solutions by integrating uncertainty quantification into machine learning-based detection systems. This study underscores the potential for GPs to enhance long-term performance in real-world applications, addressing the critical need for reliable detection mechanisms in an increasingly complex cyberthreat landscape.  \nTABLE OF CONTENTS  \nPage  \nACKNOWLEDGMENT .................................. iii  \nABSTRACT ........................................ iv  \nLIST OF TABLES ..................................... ix  \nLIST OF FIGURES .................................... xi  \nCHAPTER  \n1 INTRODUCTION ................","cbCaitQrbOv34L9J","https://ap.wps.com/l/cbCaitQrbOv34L9J","pdf",2171552,1,81,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Problem Setup\n## Managing Concept Drift: Classification with Rejection\n# Chapter 3 Related Work\n# Chapter 4 Uncertainty-Aware Malware Detection\n## Proposed Approach\n## Gaussian Processes and Uncertainty Quantification\n# Chapter 5 Experimental Setup\n## Feature Extraction\n## GP Classifier\n## Evaluation of Performance Metrics\n# Chapter 6 Results and Discussion","[{\"question\":\"Why do traditional malware detection systems degrade over time?\",\"answer\":\"They face concept drift, where malware characteristics change over time, leading to performance degradation and reduced detection reliability.\"},{\"question\":\"How does the thesis quantify prediction uncertainty?\",\"answer\":\"It uses Gaussian processes to provide a probabilistic measure of prediction uncertainty, enabling confidence-aware decision-making.\"},{\"question\":\"What benefit does uncertainty-aware abstention provide?\",\"answer\":\"Selective abstention on highly uncertain classifications reduces both false positives and false negatives, improving overall detection quality.\"}]","UNCERTAINTY-AWARE MACHINE LEARNING TECHNIQUES FOR SUSTAINABLE MALWARE DETECTION | 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