[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128416-en":3,"doc-seo-128416-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128416,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Intrusion Detection System - A Decentralized Approach Using Multi-agent Systems and Machine Learning","The increasing adoption of cloud computing and digital environments has made network infrastructures more complex and more vulnerable to cyberattacks. Traditional centralized security mechanisms struggle as data and applications are dispersed across multiple environments. This work proposes a distributed intrusion detection system using a multi-agent system and machine learning, aiming to outperform centralized, individual approaches. Results show improved accuracy, precision, recall, and F1-score, with an average action time of 6.33 seconds.","Intrusion Detection System, a Decentralized Approach Using Multi-agent Systems and Machine  \nLearning  \nNathan Cesa Nery de Castro-57011  \nThesis presented to the School of Technology and Management in the scope of the Master in Electrotechnical and Computer Engineering, in the scope of the double diploma programme with the Federal University of Technology-Paraná .  \nSupervisors:  \nProf. Dr. Paulo Jorge Pinto Leitão  \nProf. Dr. Wesley Angelino de Souza  \nBragança  \nii  \nIntrusion Detection System, a Decentralized Approach Using Multi-agent Systems and Machine  \nLearning  \nNathan Cesa Nery de Castro-57011  \nThesis presented to the School of Technology and Management in the scope of the Master in Electrotechnical and Computer Engineering, in the scope of the double diploma programme with the Federal University of Technology-Paraná .  \nSupervisors:  \nProf. Dr. Paulo Jorge Pinto Leitão  \nProf. Dr. Wesley Angelino de Souza  \nBragança  \niv  \nAcknowledgment  \nI would like to thank everyone who contributed to the completion of this work and to my academic journey.  \nGratitude to the the educational institutions UTFPR (Federal Technological University of Paraná) and IPB (Polytechnic Institute of Bragança) for the opportunity to participate in the dual degree program.  \nI would also like to thank my supervisors and course colleagues for their support and exchanged experiences.  \nFinally, my deepest gratitude to all friends, relatives and family. Especially my parents, for all their support and encouragement to complete this journey.  \nAbstract  \nThe increasing adoption of cloud computing and digital environments has made network infrastructures more complex and, at the same time, more vulnerable to cyberattacks. Traditional centralization of security mechanisms is becoming increasingly challenging, since data and applications are dispersed across multiple environments. Faced with this scenario, new decentralized approaches are emerging to try to solve this problem. This work presents a distributed approach for an intrusion detection system, through a multi-agent system and machine learning, and how this can be an advantageous tool compared to a centralized and individual system.  \nThrough a multi-agent system, it is possible to solve complex problems collaboratively, using the interaction among multiple autonomous entities that work in a coordinated manner. Each agent within the system has specific objectives, the ability to perceive the environment and the ability to make decisions independently or in conjunction with other agents. This approach is especially useful in dynamic and distributed scenarios, such as logistics, process optimization and large-scale simulations, etc. In addition, multi-agent systems favor the adaptability, scalability and robustness of solutions, allowing an efficient response to challenges that require decentralization and cooperation. Combining this with machine learning techniques, it is possible to add new features to an intrusion detection system, making it intelligent and decentralized, in order to increase the scope of detections, reduce data processing time and reaction.  \nAmong the results obtained, comparing a collaborative system with an individual system, accuracy improved by 1.13%, precision by 1.31%, recall (sensitivity) by 0.04% and F1-score by 0 .69% . Having an average action time of 6 .33 seconds.  \nKeywords: Cybersecurity, intrusion detection system, machine learning, multi-agent systems.  \nResumo  \nA crescente adoção da computação em nuvem e dos ambientes digitais têm tornado as infraestruturas de rede mais complexas, e ao mesmo tempo mais vulneráveis a ataquescibernéticos. A centralização tradicional dos mecanismos de segurança está se tornandocada vez mais desafiadora, uma vez que os dados e as aplicações estão dispersos em diversos ambientes. Diante desse cenário novas abordagens descentralizadas vêm surgindo para tentarem sanar este problema. Este trabalho apresenta uma abordagem distri","cbCaihZvzckTKHAJ","https://ap.wps.com/l/cbCaihZvzckTKHAJ","pdf",4950880,4,1,96,"English","en",105,"# Introduction\n## Motivation for decentralized intrusion detection\n## Multi-agent system rationale\n## Machine learning integration\n# Methodology\n## Agent objectives and coordination\n## Decentralized decision-making and adaptability\n# Results\n## Accuracy, precision, recall and F1-score comparison\n## Average action time\n# Conclusion\n## Benefits vs centralized and individual systems","[{\"question\":\"Why is a decentralized intrusion detection approach needed?\",\"answer\":\"Cloud and digital environments increase network complexity and attack exposure, while centralized security becomes difficult to scale because data and applications are distributed across multiple environments.\"},{\"question\":\"How do multi-agent systems contribute to the intrusion detection system?\",\"answer\":\"Multiple autonomous agents collaborate by perceiving the environment, pursuing specific objectives, and making independent or coordinated decisions, improving adaptability, scalability, and robustness in dynamic scenarios.\"},{\"question\":\"What performance improvements were observed compared with an individual system?\",\"answer\":\"The collaborative system improved accuracy by 1.13%, precision by 1.31%, recall by 0.04%, and F1-score by 0.69%, with an average action time of 6.33 seconds.\"}]","Intrusion Detection System - 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