[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117635-en":3,"doc-seo-117635-105":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},117635,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","DDoS Attack Detection with Machine Learning","Distributed Denial of Service (DDoS) attacks pose a critical threat to internet security by overwhelming servers or network infrastructure with malicious traffic, making legitimate users unable to access system services. This paper proposes a machine learning–based DDoS detection method using supervised classification. Three models—K-Nearest Neighbor, Multilayer Perceptron, and Random Forest—are evaluated under both binary and multi-class setups with feature engineering on the NSL-KDD dataset.","Journal of Informatics and Web Engineering  \nVol. 3 No. 3 (October 2024) eISSN: 2821-370X  \nDDoS Attack Detection with Machine Learning  \nWei-Wu Tay1, Siew-Chin Chong1*, Lee-Ying Chong1.  \n1Faculty of Information Science & Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia  \n*corresponding author: ([chong.siew.chin@mmu.edu.my](chong.siew.chin@mmu.edu.my); ORCiD: 0000-0003-0421-4367)  \nAbstract-Nowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to assess the accuracy of the model in training and testing processes. RF classification provides robustness and interpretability, MLP offers deep learning capabilities for complex patterns, and K-NN delivers simplicity and adaptability for instance-based learning. Together, these methods can contribute to a comprehensive DDoS attack detection system using machine learning. There are two types of classification setups: binary and multi-class classification. Binary classification involves identifying traffic as either a DDoS attack or normal using the NSL-KDD dataset. Multi-class classification, on the other hand, distinguishes between various types of DDoS attacks (such as DoS, Probe, U2R, and Sybil) and normal traffic using the NSL-KDD dataset. Feature engineering is also involved in this experiment to convert the categorical features into numerical values for detecting DDoS attack. Our model's performance was effective compared to other machine learning methods. RF achieved the highest accuracy rates: 99.35% in binary classification and 97.71% in multi-class classification. K-NN followed with 99.15% in binary and 97.35% in multi-class classification, while MLP achieved 90.63% in binary and 84.33% in multi-class classification.  \nKeywords – DDoS Attack, Random Forest, Machine Learning, Multilayer Perceptron, K-Nearest Neighbor  \nReceived: 03 July 2024; Accepted: 21 August 2024; Published: 16 October 2024  \nThis is an open access article under the CC BY-NC-ND 4.0 license.  \n1. INTRODUCTION  \nThe rapid expansion of online services and the growing complexity of network infrastructures have elevated DDoS attacks to a major threat to the availability and security of web services. A DDoS attack involves overwhelming a target system with an influx of malicious traffic, causing it to become unresponsive to legitimate users. To resolve this issue, DDoS Attack detection is a way to fix this problem. In the meantime, this project aims to first classify its protocol and use a simple binary model (0,1) to identify any attack. To achieve this, it is essential to study machine learning techniques in the realm of DDoS Attack detection.  \nThere are various of DDoS attack such as DoS attack, Probe attack, U2R, Sibir, etc [1], which the major to threat the security network. Therefore, detection of DDoS attacks is important as to detect the normal activities and malicious activities in the dataset. For improvement and enhancement of DDoS attack detection, many algorithms and methods  \nare being designed and researched. DDoS attack is a cybercrime, and it disrupts the target server on the normal traffic, network, or service by flooding (creating a massive amount of traffic) . Similar to an unanticipated traffic jam on highway that prevent normal traffic reach to destination. DDoS attack can employ multiple compromise computer system to achie","cbCainS5SQM9yF67","https://ap.wps.com/l/cbCainS5SQM9yF67","pdf",1437195,1,18,"English","en",105,"# Introduction\n## DDoS attack background and threat\n## Attack types and classification concepts\n## Detection approach and dataset setup\n# Machine Learning Method\n## Supervised classification models\n## Binary and multi-class classification\n## Feature engineering on NSL-KDD","[{\"question\":\"What problem does this paper address?\",\"answer\":\"The paper addresses DDoS attacks that flood servers or network infrastructure, preventing legitimate users from accessing system services.\"},{\"question\":\"Which machine learning models are used for detection?\",\"answer\":\"The proposed approach evaluates K-Nearest Neighbor, Multilayer Perceptron, and Random Forest in supervised classification settings.\"},{\"question\":\"How are the classification tasks organized?\",\"answer\":\"The method supports both binary classification (DDoS vs. normal) and multi-class classification that distinguishes multiple DDoS categories and normal traffic using the NSL-KDD dataset.\"}]","DDoS Attack Detection with Machine Learning | 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