[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118504-en":3,"doc-seo-118504-105":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},118504,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Security Analysis of Machine Learning Models and TOR Against Adversarial Attacks - Research Paper","Cyber attacks are evolving in sophistication, making reliable detection methods essential for network security teams. This paper evaluates how combining onion routing with machine learning can improve identification of BOT attacks under adversarial conditions. Performance is analyzed across widely used classifiers and models, including Random Forest, SVM, Logistic Regression, K-means clustering, HMM, and several neural network variants, using comparative evaluation metrics. Results determine which algorithms offer stronger detection and explain the sources of their performance differences, outlining practical strengths and limitations for real network deployments.","Security Analysis of Machine Learning Models and TOR Against Adversarial Attacks.  \nT.Gayathri1*, A.Saraswathi2  \n1*,2Department Computer Science, Govt. Arts College,Trichy, Tamilnadu, India.  \n(Received: 02 September 2023 Revised: 14 October Accepted: 07 November)  \nKEYWORDS  \nBOT attacks, TOR, machine learning algorithms  \nABSTRACT:  \nWith the increasing sophistication of cyber attacks, it has become essential for network security professionals to develop robust and effective methods for detecting BOT attacks. Machine learning algorithms have emerged as a promising solution in this regard, offering the ability to learn from data and detect patterns that may be indicative of malicious activity. Onion routing is a technique which protects internet user from the malicious attacks. Due to its slow performance in multi-layer of encryption and data can be routed to several servers. In our research we can combine onion routing with machine learning algorithms. In this paper, we analysed the performance of several popular machine learning algorithms, including Random Forest, Support Vector Machine (SVM), Neural Network Stochastic Gradient Descent, Neural Network Limited-memory Broyden-Fletcher-Goldfarb-Shanno, Neural Network adam, K-means Clustering, Hidden Markov Models (HMM), and Logistic Regression, for detecting BOT attacks in a network. We begin by providing an overview of BOT attacks and the different machine learning algorithms used for detecting them. Next, we explore the performance metrics used to evaluate these algorithms and compare their performance based on these metrics. Finally, we identify the algorithm(s) that perform better in detecting BOT attacks and discuss the reasons for their superior performance. By the end of this paper, readers will have a comprehensive understanding of the strengths and weaknesses of different machine learning algorithms for detecting BOT attacks in a network.  \n1. Introduction:  \nBot attacks, also known as botnet attacks, refer to the use of bot malware and botnets to support harmful online activities. These attacks are carried out to gather information about the target before launching other devastating attacks. However, certain algorithms like SVM may have slower training times due to the use of a non-linear kernel and a significant number of samples. [1] . Furthermore, future tests may involve using smaller input sets to test performance changes, although this may negatively impact model accuracy in some cases [5] . Botnets are made up of devices such as cameras, routers, DVRs, wearables, and other embedded devices. The most common Botnet malware attacks that affect include Mirai and BASHLITE. Botnets can be used for click fraud, distributed denial of service attacks, spam and virus distribution, identity theft, and key-logging [2][1] . In SDN-enabled networks, bot attacks are common and hinder system availability by consuming system resources. Botnets identify new devices with security holes and infect and control them. Once accessed, compromised devices can be used to launch devastating attacks on the  \nnetwork. Network-probing botnet attacks continuously collect information about devices in the network or the network itself. These attacks can use Internet relay chat (IRC) or [HTTP](HTTP) for communication between the attacker and botmaster's communication and control server. Bots also gain access to a network through network-probing attacks such as IP address scanning, port scanning, and sending a simple service discovery protocol (SSDP) search query. Detecting bot attacks is a significant challenge in cybersecurity, and machine learning algorithms have been established to address this issue. Machine learning and deep learning models are used to detect malware in BOT attacks [2] . Currently, detection techniques can identify a DDoS attack and the Botnet network after the attack has occurred. To detect malware, continuous series data collection is required for representative approa","cbCaijzaexXMsGQG","https://ap.wps.com/l/cbCaijzaexXMsGQG","pdf",315141,1,12,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Literature Survey\n# 3. Machine Learning Algorithms for BOT Attack Detection\n# 4. Performance Metrics and Comparative Analysis\n# 5. Results: Best-Performing Algorithms and Reasons\n# 6. Conclusion","[{\"question\":\"What is the main goal of the paper regarding BOT attack detection?\",\"answer\":\"The paper aims to detect BOT attacks in a network by analyzing the performance of multiple machine learning algorithms, and discusses which ones perform better based on evaluation metrics.\"},{\"question\":\"Which technique is used to protect users while supporting the proposed detection approach?\",\"answer\":\"Onion routing is used to protect internet users by routing data through multiple encryption layers and servers, and the paper explores combining it with machine learning for detection.\"},{\"question\":\"Which machine learning algorithms are compared in the performance analysis?\",\"answer\":\"The paper compares Random Forest, Support Vector Machine (SVM), several neural-network variants (including stochastic gradient descent and other specified optimizers), K-means clustering, Hidden Markov Models (HMM), and Logistic Regression.\"}]","Security Analysis of Machine Learning Models and TOR Against Adversarial Attacks - Research Paper | PDF",1785683912,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"security-analysis-of-machine-learning-models-and-tor-against-adversarial-attacks-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/security-analysis-of-machine-learning-models-and-tor-against-adversarial-attacks-research-paper/118504/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the paper regarding BOT attack detection?","Question",{"text":75,"@type":76},"The paper aims to detect BOT attacks in a network by analyzing the performance of multiple machine learning algorithms, and discusses which ones perform better based on evaluation metrics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which technique is used to protect users while supporting the proposed detection approach?",{"text":80,"@type":76},"Onion routing is used to protect internet users by routing data through multiple encryption layers and servers, and the paper explores combining it with machine learning for detection.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are compared in the performance analysis?",{"text":84,"@type":76},"The paper compares Random Forest, Support Vector Machine (SVM), several neural-network variants (including stochastic gradient descent and other specified optimizers), K-means clustering, Hidden Markov Models (HMM), and Logistic Regression.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]