[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124216-en":3,"doc-seo-124216-105":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},124216,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning and Federated Learning-Guided Defense for Cyber-Security - A Dissertation","This dissertation develops machine learning and federated learning–guided defenses for cyber-security, focusing on malware detection under adversarial and resource-constrained conditions. It frames malware as a threat and investigates detection approaches including energy-harvesting support for IoT-based learning. The work includes stealthy malware detection using complex symbolic sequences, and on-device federated learning for malware detection with mechanisms resilient to feature alteration. A robust FedProx-style aggregation strategy and performance-aware federated learning are evaluated with datasets and experimental results, including visualization and key findings.","MACHINE LEARNING AND FEDERATED LEARNING-GUIDED DEFENSE FOR CYBER-SECURITY  \nby  \nSanket Shukla  \nA Dissertation  \nSubmitted to the  \nGraduate Faculty  \nof  \nGeorge Mason University  \nIn Partial fulfillment of  \nThe Requirements for the Degree of  \nDoctor of Philosophy Electrical and Computer Engineering  \nCommittee:  \nDate:  04/22/2024   \nDr. Sai Manoj P D, Dissertation Director Dr. Khaled Khasawneh, Committee Member Dr. Kai Zeng, Committee Member  \nDr. Setareh Rafatirad, Committee Member Dr. Brian Mark, Department Chair  \nSpring Semester 2024  \nGeorge Mason University Fairfax, VA  \nMachine Learning and Federated Learning-Guided Defense for Cyber-Security  \nA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University  \nBy  \nSanket Shukla  \nMaster of Science  \nGeorge Mason University, 2022  \nBachelor of Science  \nUniversity of Mumbai, 2015  \nDirector: Dr. Sai Manoj P D, Professor  \nDepartment of Electrical and Computer Engineering  \nSpring Semester 2024  \nGeorge Mason University  \nFairfax, VA  \nCopyright © 2024 by Sanket Shukla All Rights Reserved  \nDedication  \nI sincerely dedicate this thesis to my beloved Lord Jagannath Maharaj and Shree Siddhivinayak. I dedicate the thesis to my loving parents, Manisha Shukla and Sanjay Shukla; my uncles Prashant and Ashesh; my aunties Sharda and Geeta and my brothers Chinmay and Aayush. Without their motivation and nurture it would have been impossible to keep treading in testing times. This cannot be complete without crediting my grandparents Ramakant Shukla, Nalini Shukla, Yeshwant Rane and Suhasini Rane. I also would like to thank my friends, Abhijitt, Gaurav, and many others for their constant support and encouragement. I cannot forget to express my deep gratitude to my mentors, Sandhini and Mahamantra for their loving care and best wishes. I want to deeply appreciate my friends from India and my cousins, and all my relatives for their care, support, motivation, best wishes, and blessings. I extend my gratitude to everyone who has been instrumental in the success so far and much more to come.  \nAcknowledgments  \nI would like to thank the following special people who made this long journey possible seamlessly. I sincerely thank Dr. Sai Manoj P D, my Master’s and PhD advisor for his kind support always in trying to make me a better version of myself. I have learned from him many qualities which are conducive to being an enthusiastic and sincere student. Dr. Sai’s strong intellect and advice has helped me to overcome many challenges throughout my research. I would like to thank Dr. Monson Hayes, Dr. Brian Mark, Dr. Kris Gaj, Dr. Kai Zeng, Dr. Khaled Khasawneh, Dr. Liling Huang, Dr. Craig Lorie, Dr. Houman Homayoun, Dr. Setareh Rafatirad, Ms. Jammie Chang, Ms. Patricia Sahs; Dr. Vivek Venugopalan from USC ISI, Dr. Sharmila Petkar, and Ms. Trupti Agarkar from D.Y. Patil Ramrao Adik Institute of Technology, for being instrumental in their capacity as my advisors, professors, collaborators, trainers, teachers and much more.  \nTable of Contents  \nPage  \nList of Tables ........................................ ix  \nList of Figures ........................................ x  \nAbstract ........................................... xii  \n1 Introduction ...................................... 1  \n1.1 Overview- “Malware” as a Threat ....................... 1  \n1.2 Machine Learning for Malware Detection ................... 2  \n1.3 Energy Harvesting for Machine Learning on IoTs ............... 4  \n1.4 Federated Learning for Malware Detection ................... 5  \n1.5 Communication Efficient Federated Learning ................. 7  \n1.6 Need for Heterogeneous Models in Federated Learning ............ 8  \n1.7 Network Attributes Assisted Malware Detection ................ 9  \n1.8 Thesis Contributions ............................... 10  \n1.9 Organization of Thesis .............................. 11  \n2 Background and State-of-the-Art ....","cbCaijReIknIYSW3","https://ap.wps.com/l/cbCaijReIknIYSW3","pdf",18903048,1,124,"English","en",105,"# Table of Contents\n## List of Tables\n## List of Figures\n## Abstract\n## 1 Introduction\n## 2 Background and State-of-the-Art\n## 3 Deep Learning-based Stealthy Malware Detection using Complex Symbolic Sequence\n## 4 On-Device Malware Detection using Federated Learning\n## 5 Robust and Data-aware Federated Learning-inspired Malware Detection in Internetof-Things IoT","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"The dissertation addresses malware detection as a cyber-security threat, including challenges posed by stealthy behavior and adversarial feature manipulation.\"},{\"question\":\"How does federated learning contribute to the proposed defenses?\",\"answer\":\"It enables on-device malware detection while coordinating learning across devices, reducing dependence on centralized raw data and improving robustness in the threat model.\"},{\"question\":\"What evaluation elements are included in the work?\",\"answer\":\"The research includes dataset formulation and visualization, experimental setups, performance assessment, and results interpretation with key findings.\"}]","Machine Learning and Federated Learning-Guided Defense for Cyber-Security - 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