[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122386-en":3,"doc-seo-122386-105":30,"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":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},122386,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Hybrid Machine Learning Based Black Hole Attack Detection And Prevention Of In MANET","A Mobile Ad hoc Network (MANET) enables mobile devices to communicate without fixed infrastructure, yet it remains exposed to disruptive threats such as black hole attacks. This thesis presents Hybrid Machine Learning based secure AODV (HML-SAODV), which strengthens MANET routing by modifying the AODV protocol and integrating machine learning detection using features related to destination sequence number. Using NS-2 collected over 150,000 records, regression and classification models guide the secure routing phase, then performance is evaluated against standard AODV and existing solutions. Results show improved detection (above 99%) with higher packet delivery ratio and throughput, and reduced delay and routing overhead.","JIMMA UNIVERSITY  \nJIMMA INSTITUTE OF TECHNOLOGY ELECTRICAL AND COMPUTER ENGINEERING  \nMASTER OF SCIENCE IN COMPUTER ENGINEERING  \nHYBRID MACHINE LEARNING BASED BLACK HOLE ATTACK DETECTION AND  \nPREVENTION OF IN MANET  \nA Thesis Submitted to the School of Graduate Studies of Jimma Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering  \nBy  \nAtnatiyos Tefera  \nJun 2023  \nJimma, Ethiopia  \nii  \nThesis Approval Sheet  \nWe have examined the thesis entitled hybrid machine learning based black hole attack detecting and preventing in mobile ad hoc networks defended in Jun 2023 by student Atnatiyos Tefera. We are satisfied with the revisions made and thus approve the thesis in its current form.  \nAdvisor:  \nEngr. Kris Calpotura, MSME, MIT  \nCo-advisor:  \nMr. AdaneTadesse, MSc  \nExternal Examiner:  \nDr. Henock Mulugeta  \nInternal Examiner:  \nChairperson:  \nSigned: ~~ ~~ Date ~~ ~~  \nSigned:~~ ~~ Date ~~ ~~  \nSigned:~~ ~~~~ ~~ Date ~~ ~~  \nSigned: ~~ ~~ Date ~~ ~~  \nSigned: ~~ ~~ Date ~~ ~~  \niii  \nDeclaration  \nI, Atnatiyos Tefera, declare that this thesis titled,(Hybrid machine learning based black hole attack detection and prevention in MANET and the work presented in it are my own. Iconfirm that:  \n• This work was done for a master of science in computer engineering at Jimma University.  \n• Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution.  \n• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.  \n• I have acknowledged all main sources of help.  \nSigned:   \nDate:  \niv  \nAbstract  \nA Mobile Ad hoc Network (MANET) is a type of wireless network where mobile devices communicate with each other without relying on any pre-existing infrastructure or centralized administration. They are useful in emergency situations but can be vulnerable to attacks. One such attack is the black hole attack, where false route information is sent to intercept communication between nodes, causing major disruptions and rendering the network useless.  \nHybrid machine learning-based secure AODV (HML-SAODV) is a machine learning-based approach proposed to enhance security in MANETs By modifying the existing AODV routing protocol. This research is organized into phases. The first phase involves gathering information using the NS-2 simulator, which includes four features related to the destination sequence number. Over 150,000 data were collected and analyzed using both regression and classification machine learning techniques to determine their relationship. The second phase focused on incorporating the suggested approach into the AODV protocol and evaluating its performance in comparison to standard AODV under a black hole attack and other proposed solutions by various researchers.  \nHML-SAODV significantly improves network performance metrics in detecting and preventing black hole attacks. The packet delivery ratio and throughput are increased by 2.88 and, 2.71 while end-to-end delay and routing overhead are decreased by 2, and 7-fold respectively. The proposed solution does not significantly degrade the network performance when there is no attacker present. A slight increase in the end-to-end delay by 17.4% and routing overhead by 20% . HML-SAODV black hole attack detection rate is above 99% .  \nHowever, larger networks with more attacker nodes may have a higher false positive rate. Overall, HML-SAODV is a promising solution to enhance wireless ad hoc network security and performance.  \nKeywords: Mobile Ad hoc Network, Ad hoc On-Demand Distance Vector, black hole attacks, Reactive Routing Protocol, Machine Learning, Regression, Classification  \nv  \nAcknowledgements  \nThis project work is done not only by my effort. It has got input from different individuals. I want to express my deepest respect and mo","cbCaidlZ8zX0slRu","https://ap.wps.com/l/cbCaidlZ8zX0slRu","pdf",1610039,1,104,"English","en",105,"# Introduction\n## Background\n## Motivation\n## Statement of The Problem\n## Research Questions\n## Objective of The Study\n## Research Contributions\n## Scope and Limitation of The Study\n## Outline of The Research\n# Review of Related Literature\n## MANET Overview\n## Routing Protocols in MANET\n## Reactive Routing Protocol in MANET (AODV)\n## MANET Security Challenges\n## Malicious Attacks in MANET (Black Hole Attack)\n## Performance Analysis under Malicious Attacks\n## Machine Learning Algorithms","[{\"question\":\"What problem does the thesis address in MANETs?\",\"answer\":\"It addresses security weaknesses in MANET routing, specifically the black hole attack that injects false route information to intercept communications and disrupt the network.\"},{\"question\":\"How does HML-SAODV enhance security?\",\"answer\":\"HML-SAODV improves security by modifying the AODV routing protocol and integrating hybrid machine learning for black hole detection using selected routing-related features.\"},{\"question\":\"What performance improvements are reported by the proposed approach?\",\"answer\":\"The thesis reports increased packet delivery ratio and throughput and decreased end-to-end delay and routing overhead under black hole attack conditions, while performance remains largely unaffected when no attacker is present.\"}]","Hybrid Machine Learning Based Black Hole Attack Detection And Prevention Of In MANET | 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problem does the thesis address in MANETs?","Question",{"text":76,"@type":77},"It addresses security weaknesses in MANET routing, specifically the black hole attack that injects false route information to intercept communications and disrupt the network.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does HML-SAODV enhance security?",{"text":81,"@type":77},"HML-SAODV improves security by modifying the AODV routing protocol and integrating hybrid machine learning for black hole detection using selected routing-related features.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance improvements are reported by the proposed approach?",{"text":85,"@type":77},"The thesis reports increased packet delivery ratio and throughput and decreased end-to-end delay and routing overhead under black hole attack conditions, while performance remains largely unaffected when no attacker is 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