[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118637-en":3,"doc-seo-118637-105":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},118637,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning-based Intrusion Detection System for Social Network Infrastructure - Research","The growing number of cyber-attacks drives the need for reliable mechanisms to prevent unauthorized data access. This study develops an intrusion detection approach that identifies malicious connections using key traffic parameters and trains models on normal and abnormal events via machine learning and data mining. Five models—Random Forest, Bagging, Boosting, Support Vector Machine, and K-Nearest Neighbor—are evaluated using experimental data with varying features, iterations, and hyperparameters, achieving detection rates up to 98.7%. Random Forest delivers the strongest performance and supports an IDS to flag threats early and stop cyber-attacks before serious network compromise.","Machine Learning-based Intrusion Detection System for Social Network Infrastructure  \nGovind Kumar Jha  \nDepartment of Computer Science & Engineering, Government Engineering College Munger  \nMunger, India  \n[gvnd.jha@gmail.com](gvnd.jha@gmail.com)  \nPreetish Ranjan  \nAmity School of Engineering & Technology, Amity University Patna, Patna, India  \n[pranjan@ptn.amity.edu](pranjan@ptn.amity.edu)  \nRitesh Ravi  \nAmity Business School, Amity University Patna, Patna, India  \n[rravi@ptn.amity.edu](rravi@ptn.amity.edu)  \nHardeo Kumar Thakur  \nDepartment of Computer Science & Engineering, Bennett University, Greater Noida, India  \n[hardeo.thakur@bennett.edu.in](hardeo.thakur@bennett.edu.in)  \nAbstract :  \nThe growing number of cyber-attacks demands a critical measure to prevent unauthorized data access. Thus, intrusion detection has become critical to deal with such attacks. This work attempts to identify malicious connections using a few key parameters. The system has been trained using data relating to normal and abnormal events through machine learning and data mining techniques. To detect intrusions, this study assessed five distinct machine learning models: Random Forest, Bagging, Boosting, Support Vector Machine, and K-Nearest Neighbor (KNN). Based on the number of features, iterations, and hyperparameters, the models were evaluated using experimental data collected in real time. With a detection rate of up to 98.7%, the Random Forest approach surpassed existing machine learning models for intrusion detection. The paper proposes a novel intrusion detection system (IDS) based on these findings that successfully identifies possible threats before they seriously compromise network security and stop cyberattacks.  \nIndex Terms: Machine Learning, Intrusion Detection System, Random Forest, Support Vector Machine, K Nearest Neighbor (KNN), Bag and Boosting, Ensemble learning.  \n1. Introduction  \nThe term \"intrusion\" describes an unauthorized user's access to a system or network, frequently with malevolent intent. Even with sophisticated intrusion detection systems, firewalls frequently fail to identify the financial ramifications of assaults. Data breaches cost companies an average of $4.35 million in 2022, according to AAG IT Services (June 2023) . Not to  \nmention the harm to one's reputation and other losses, losing this much money in a cyberattack is a big worry. According to Petrosyan (2022), the global cost of cybercrime is increasing. Therefore, there is a great demand for cybersecurity products. Enterprises are swiftly creating Intrusion Detection Systems (IDS) in reaction to cyberattacks directed at both public and private organizations (Sultana, 2019) . As a result of intrusions, there may be a rise in ransomware assaults, in  \nwhich a company's data is encrypted and rendered unreadable. These intrusions have major consequences if they are not discovered and examined promptly; for this reason, they must be handled carefully and strategically. Numerous methods, including network segmentation, firewalls, access control, behavioral analytics, data loss prevention, distributed denial of service (DDoS) prevention, antivirus, and anti-malware software, application security, and firewalls, are frequently used to prevent unauthorized access to the system. They have the ability to block data outflow, filter information, create alerts, and stop dangerous activity. In firewalls and spam filters, simple rule-based algorithms are frequently used to accept and reject protocols, ports, and IP addresses. However, firewalls and filters have limitations of distinguishing between ‘good traffic’ and ‘bad traffic’.  \nPreparedness to deal with the consequences is of utmost importance. Therefore, this article aims to develop a model based on Machine Learning and Deep Learning techniques. The objective is to effectively distinguish between positive and negative connections, outperforming existing models while minimizing false positives. Figure 1 is t","cbCaiq1T3Kz0Q5lM","https://ap.wps.com/l/cbCaiq1T3Kz0Q5lM","pdf",862301,1,11,"English","en",105,"# Introduction\n## Threats and limitations of existing defenses\n## Proposed objective and workflow\n# Related work and dataset\n## KDD Cup 99 and feature categories\n# Methodology and model evaluation\n## Models compared: RF, Bagging, Boosting, SVM, KNN\n## Training factors and experimental assessment\n# Results and proposed IDS","[{\"question\":\"What problem does the proposed system address?\",\"answer\":\"It targets the detection of unauthorized and malicious connections to prevent cyber-attacks and protect network security.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"Random Forest, Bagging, Boosting, Support Vector Machine, and K-Nearest Neighbor (KNN) are assessed for intrusion detection performance.\"},{\"question\":\"How does the Random Forest approach perform compared with other models?\",\"answer\":\"It surpasses other evaluated models and reports a detection rate of up to 98.7%, supporting early threat identification.\"}]","Machine Learning-based Intrusion Detection System for Social Network Infrastructure - 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