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Journal articles on the topic 'DDoS attack detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Aladaileh, Mohammad Adnan, Mohammed Anbar, Ahmed J. Hintaw, et al. "Effectiveness of an Entropy-Based Approach for Detecting Low- and High-Rate DDoS Attacks against the SDN Controller: Experimental Analysis." Applied Sciences 13, no. 2 (2023): 775. http://dx.doi.org/10.3390/app13020775.

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Software-defined networking (SDN) is a unique network architecture isolating the network control plane from the data plane, offering programmable elastic features that allow network operators to monitor their networks and efficiently manage them. However, the new technology is security deficient. A DDoS attack is one of the common attacks that threaten SDN controllers, leading to the degradation or even collapse of the entire SDN network. Entropy-based approaches and their variants are considered the most efficient approaches to detecting DDoS attacks on SDN controllers. Therefore, this work a

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Han, Dezhi, Kun Bi, Han Liu, and Jianxin Jia. "A DDoS attack detection system based on spark framework." Computer Science and Information Systems 14, no. 3 (2017): 769–88. http://dx.doi.org/10.2298/csis161217028h.

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There are many problems in traditional Distributed Denial of Service (DDoS) attack detection such as low accuracy, low detection speed and so on, which is not suitable for the real time detecting and processing of DDoS attacks in big data environment. This paper proposed a novel DDoS attack detection system based on Spark framework including 3 main algorithms. Based on information entropy, the first one can effectively warn all kinds of DDoS attacks in advance according to the information entropy change of data stream source IP address and destination IP address; With the help of designed dyna

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Dasari, Kishore Babu, and Nagaraju Devarakonda. "Detection of Different DDoS Attacks Using Machine Learning Classification Algorithms." Ingénierie des systèmes d information 26, no. 5 (2021): 461–68. http://dx.doi.org/10.18280/isi.260505.

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Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while d

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Beshah, Yonas Kibret, Surafel Lemma Abebe, and Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System." Electronics 13, no. 6 (2024): 1004. http://dx.doi.org/10.3390/electronics13061004.

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Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic enviro

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Li, Feng, and Hai Ying Wang. "Design on DDoS Attack Detection and Prevention Systems." Applied Mechanics and Materials 530-531 (February 2014): 798–801. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.798.

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For DDoS attacks, it must be sniffing this step, the attacker to be able to successfully launch the final realization of the invasion and attack, we must find a suitable host computer and can be used as hosts puppet machine. In this thesis, a DDoS attack detection technologies, and further proposed based DDoS attack defense system design, the results show that our design can effectively prevent DDoS network attacks.

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Xu, Hao, and Hequn Xian. "SCD: A Detection System for DDoS Attacks based on SAE-CNN Networks." Frontiers in Computing and Intelligent Systems 5, no. 3 (2023): 94–99. http://dx.doi.org/10.54097/fcis.v5i3.13865.

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The pervasive application of network technology has given rise to a numerous of network attacks, including Distributed Denial of Service (DDoS) attacks. DDoS attacks can lead to the collapse of network resources, making the target server unable to support legitimate users, which is a critical issue in cyberspace security. In complex real-world network environments, differentiating DDoS attack traffic from normal traffic is a challenging task, making it significant to effectively distinguish between attack types in order to resist DDoS attacks. However, traditional DDoS attack detection methods

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D., Glăvan. "DDoS detection and prevention based on artificial intelligence techniques." Scientific Bulletin of Naval Academy XXII, no. 1 (2019): 134–43. http://dx.doi.org/10.21279/1454-864x-19-i1-018.

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Distributed Denial of Service (DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of Things (IoT), artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attack

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Zhang, Jian, Qidi Liang, Rui Jiang, and Xi Li. "A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors." Applied Sciences 9, no. 21 (2019): 4633. http://dx.doi.org/10.3390/app9214633.

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In recent years, distributed denial of service (DDoS) attacks have increasingly shown the trend of multiattack vector composites, which has significantly improved the concealment and success rate of DDoS attacks. Therefore, improving the ubiquitous detection capability of DDoS attacks and accurately and quickly identifying DDoS attack traffic play an important role in later attack mitigation. This paper proposes a method to efficiently detect and identify multivector DDoS attacks. The detection algorithm is applicable to known and unknown DDoS attacks.

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Xie, Bailin, Yu Wang, Guogui Wen, and Xiaojun Xu. "Application-Layer DDoS Attack Detection Using Explicit Duration Recurrent Network-Based Application-Layer Protocol Communication Models." International Journal of Intelligent Systems 2023 (June 17, 2023): 1–13. http://dx.doi.org/10.1155/2023/2632678.

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Existing application-layer distributed denial of service (AL-DDoS) attack detection methods are mainly targeted at specific attacks and cannot effectively detect other types of AL-DDoS attacks. This study presents an application-layer protocol communication model for AL-DDoS attack detection, based on the explicit duration recurrent network (EDRN). The proposed method includes model training and AL-DDoS attack detection. In the AL-DDoS attack detection phase, the output of each observation sequence is updated in real time. The observation sequences are based on application-layer protocol keywo

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Goparaju, Bhargavi, and Dr Bandla Srinivasa Rao. "A DDoS Attack Detection using PCA Dimensionality Reduction and Support Vector Machine." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 1s (2023): 01–08. http://dx.doi.org/10.17762/ijcnis.v14i1s.5586.

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Distributed denial-of-service attack (DDoS) is one of the most frequently occurring network attacks. Because of rapid growth in the communication and computer technology, the DDoS attacks became severe. So, it is essential to research the detection of a DDoS attack. There are different modes of DDoS attacks because of which a single method cannot provide good security. To overcome this, a DDoS attack detection technique is presented in this paper using machine learning algorithm. The proposed method has two phases, dimensionality reduction and model training for attack detection. The first pha

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Sudhanva, Manjunath, Abhay Pratap Singh Athreya, Chandra Gowda Naveen, T. Yerriswamy, and H. N. Veena. "Machine Learning Techniques to Detect DDoS Attacks in IoT's, SDN's: A Comprehensive Overview." International Journal of Human Computations and Intelligence 2, no. 4 (2023): 203–11. https://doi.org/10.5281/zenodo.8027034.

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Attacks known as distributed denial of service (DDoS) compromise user privacy while disrupting internet services and posing a serious danger to network security. DDoS attack detection using machine learning (ML) techniques has showed promise, but the evolving nature of these attacks presents challenges in accurately distinguishing between attack patterns and normal traffic. This paper presents a comprehensive overview of effective ML techniques for DDoS attack detection, focusing on IoTs, SDNs, and cloud. The literature survey analyzes research findings, categorized according to a suggested ta

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Zeinalpour, Alireza, and Hassan A. Ahmed. "Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method." Electronics 11, no. 17 (2022): 2736. http://dx.doi.org/10.3390/electronics11172736.

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The curse of dimensionality, due to lots of network-traffic attributes, has a negative impact on machine learning algorithms in detecting distributed denial of service (DDoS) attacks. This study investigated whether adding the filter and wrapper methods, preceded by combined clustering algorithms using the Vote classifier method, was effective in lowering the false-positive rates of DDoS-attack detection methods. We examined this process to address the curse of dimensionality of machine learning algorithms in detecting DDoS attacks. The results of this study, using ANOVA statistical analyses,

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Katuk, Norliza, Mohamad Sabri Sinal, Mohammed Gamal Ahmed Al-Samman, and Ijaz Ahmad. "An observational mechanism for detection of distributed denial-of-service attacks." International Journal of Advances in Applied Sciences 12, no. 2 (2023): 121. http://dx.doi.org/10.11591/ijaas.v12.i2.pp121-132.

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<span>This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results sugg

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Norliza, Katuk, Gamal Ahmed Al-Samman Mohammed, and Ahmad Ijaz. "An observational mechanism for detection of distributed denial-of-service attacks." International Journal of Advances in Applied Sciences (IJAAS) 12, no. 2 (2023): 132. https://doi.org/10.11591/ijaas.v12.i2.pp121-132.

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This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results suggested that t

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Kasture, Pradnya. "DDoS Attack Detection using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6421–24. http://dx.doi.org/10.22214/ijraset.2023.53133.

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Abstract: DDoS attacks are an attempt to prevent the service from being unavailable by overloading the server with malicious traffic. In the past few years, distributed denial of service attacks is becoming the most difficult and burdensome problem. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. As there are different attack patterns or new types of attacks, it is difficult to detect such attacks effectively. New techniques for generating and mitigating distributed denial of service attacks have been developed in the prese

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Aineyoona, Patrick. "A MACHINE LEARNING ALGORITHM WITH SELF-UPDATE PARAMETER CALIBRATION TO IMPROVE INTRUSION DETECTION OF DDOS IN COMMUNICATION NETWORKS." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (2021): 72–79. http://dx.doi.org/10.33564/ijeast.2021.v06i06.008.

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Currently DDoS attack has become one of the most common network attacks worldwide. This is largely due to the fact that we live in the age of the Internet of Things, with the rapid development of computer and communication technology evolving into big, complex and distributed systems that are exposed to several kinds of attacks in addition to new threats. In order to detect intruders in an efficient and timely manner, a real time detection mechanism, proficient in dealing with a variety of forms of attacks is highly important. However, due to the uniformity and evolution of DDoS attack modes a

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Amrish, R., K. Bavapriyan, V. Gopinaath, A. Jawahar, and C. Vinoth Kumar. "DDoS Detection using Machine Learning Techniques." March 2022 4, no. 1 (2022): 24–32. http://dx.doi.org/10.36548/jismac.2022.1.003.

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A Distributed Denial of Service (DDoS) attack is a type of cyber-attack that attempts to interrupt regular traffic on a targeted server by overloading the target. The system under DDoS attack remains occupied with the requests from the bots rather than providing service to legitimate users. These kinds of attacks are complicated to detect and increase day by day. In this paper, machine learning algorithm is employed to classify normal and DDoS attack traffic. DDoS attacks are detected using four machine learning classification techniques. The machine learning algorithms are tested and trained

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Chen, Hongsong, Caixia Meng, and Jingjiu Chen. "DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment." International Journal of Information Security and Privacy 15, no. 3 (2021): 1–18. http://dx.doi.org/10.4018/ijisp.2021070101.

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Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In

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Reddy, N. Narasimha, and G. M. Vema Reddy. "DDoS Attack Detection in SDN using ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 2035–38. http://dx.doi.org/10.22214/ijraset.2023.56350.

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Abstract: The increasing prevalence of DDoS attacks poses a serious threat to modern network infrastructures. SDN has been proposed as a promising solution for enhancing network security. However, detecting and mitigating DDoS attack in software definednetwork remains a challenging task. In this research paper, suggest an innovative approach in order to identify DDoS assaults in software-defined networks using (ML) techniques. Ourmethod entails gathering and analyzing network data. Traffic data using SDN controllers. We use variety of ML techniques analyze the traffic information to discover u

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Rudro, Rifat Al Mamun, MD FARUK ABDULLAH AL SOHAN, Syma Kamal Chaity, and Rubina Islam Reya. "Enhancing DDoS Attack Detection Using Machine Learning: A Framework with Feature Selection and Comparative Analysis of Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 14, no. 03 (2023): 1185–92. http://dx.doi.org/10.61841/turcomat.v14i03.14086.

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Distributed Denial of Service (DDoS) attacks are an ever-present threat to network security and can make online services hard for users to access. Conventional detection methods often struggle to effectively counter new and sophisticated DDoS attacks. This research article aims to assess the effectiveness of several machine learning methods in detecting distributed denial-of-service (DDoS) attacks. The evaluation is conducted using the DDOS attack SDN dataset, which is sourced from Google's research dataset. Various algorithms, including Random Forest, Decision Tree, Naive Bayes, and Support V

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Kumavat, Kavita S., and Joanne Gomes. "Common Mechanism for Detecting Multiple DDoS Attacks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 81–90. http://dx.doi.org/10.17762/ijritcc.v11i4.6390.

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An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of

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Ma, Zheng, Rui Zhang, and Lang Gao. "Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOA." Algorithms 18, no. 7 (2025): 449. https://doi.org/10.3390/a18070449.

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The development of 5G environments has several advantages, including accelerated data transfer speeds, reduced latency, and improved energy efficiency. Nevertheless, it also increases the risk of severe cybersecurity issues, including a complex and enlarged attack surface, privacy concerns, and security threats to 5G core network functions. A 5G core network DDoS attack detection model is been proposed which utilizes a binary improved non-Bald Eagle optimization algorithm (Sin-Cos-bIAVOA) originally designed for IoT DDoS detection to select effective features for DDoS attacks. This approach em

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Xiong, Ze Yu. "Traffic Classification Features and its Application in DDoS Detection." Applied Mechanics and Materials 380-384 (August 2013): 2673–76. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2673.

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DDoS attacks have relatively low proportion of normal flow in the boundary network at the attack traffic,In this paper,we establish DDoS attack detection method based on defense stage and defensive position, and design and implement collaborative detection of DDoS attacks. Simulation results show that our approach has good timeliness, accuracy and scalability than the single-point detection and route-based distributed detection scheme.

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Haseeb-ur-rehman, Rana M. Abdul, Azana Hafizah Mohd Aman, Mohammad Kamrul Hasan, et al. "High-Speed Network DDoS Attack Detection: A Survey." Sensors 23, no. 15 (2023): 6850. http://dx.doi.org/10.3390/s23156850.

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Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting

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Le, Duc, Minh Dao, and Quyen Nguyen. "Comparison of machine learning algorithms for DDoS attack detection in SDN." Information and Control Systems, no. 3 (June 15, 2020): 59–70. http://dx.doi.org/10.31799/1684-8853-2020-3-59-70.

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Introduction: Distributed denial-of-service (DDoS) has become a common attack type in cyber security. Apart from the conventional DDoS attacks, software-defined networks also face some other typical DDoS attacks, such as flow-table attack or controller attack. One of the most recent solutions to detect a DDoS attack is using machine learning algorithms to classify the traffic. Purpose: Analysis of applying machine learning algorithms in order to prevent DDoS attacks in software-defined network. Results: A comparison of six algorithms (random forest, decision tree, naive Bayes, support vector m

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Sravan Kumar G, Et al. "The Investigative Study on the Performance Analysis of SMOTE employed Machine Learning Classifier Models to DDoS Attack Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 702–8. http://dx.doi.org/10.17762/ijritcc.v11i9.8862.

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Distributed Denial of Service (DDoS) attack, a severe attack on the network services during the contemporary era, is categorized under active attacks in security attacks. The impact of this attack on the organization or individual resources leads to massive loss in terms of finance, reputation. Therefore, detecting Distributed DDoS attacks is vital in ensuring the availability and integrity of online services of an organization. The work in this paper employed machine learning techniques, complemented by Synthetic Minority Over-sampling Technique (SMOTE), to tackle the inherent challenge of im

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Fatkieva, R. R., A. S. Sudakov, and A. S. Nersisyan. "Key Characteristics of Network Traffic to Identify DDoS Attacks." LETI Transactions on Electrical Engineering & Computer Science 17, no. 8 (2024): 65–80. http://dx.doi.org/10.32603/2071-8985-2024-17-8-65-80.

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Modern methods of analyzing and protecting network infrastructure against DDoS (Distributed Denial of Service) attacks are discussed. A DDoS detection model has been developed using statistical techniques, which highlights the main stages of the attacks and key characteristics of network traffic that are crucial for detecting an attack. Potential and attack power are introduced as main concepts in assessing DDoS activity. To identify the type of attack, it is suggested to increase the sensitivity of the model by identifying key characteristics that distinguish between different attack stages.

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Manish Kumar Rajak and Dr. Ravindra Tiwari. "A Framework for Detecting Distributed Denial of Services Attack in Cloud Enviorment using Machine Learning Techniques." Journal of Advances and Scholarly Researches in Allied Education 21, no. 1 (2024): 175–79. http://dx.doi.org/10.29070/hc5qzn85.

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Distributed Denial of Service (DDoS) persists in Online Applications as One of those significant threats. Attackers can execute DDoS by the more natural steps. Then with the high productivity to slow the consumer access services down. To detect an attack on DDoS and using machine learning algorithms. The Overseen to detect and mitigate the attack, machine learning algorithms such as Naive Bayes, decision tree, k-nearest neighbours (k-NN) and random forest are used. There are three steps: gathering information, preprocessing and feature Extraction in "Normal or DDoS" classification algorithm fo

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Rajak, Manish Kumar, and Ravindra Tiwari. "Framework for Detecting Distributed Denial of Services Attack in Cloud Environment." International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (2024): 43–48. http://dx.doi.org/10.15680/ijircce.2024.1203507.

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Distributed Denial of Service (DDoS) persists in Online Applications as One of those significant threats. Attackers can execute DDoS by the more natural steps. Then with the high productivity to slow the consumer access services down. To detect an attack on DDoS and using machine learning algorithms. The Overseen to detect and mitigate the attack, machine learning algorithms such as Naive Bayes, decision tree, k-nearest neighbours (k-NN) and random forest are used. There are three steps: gathering information, preprocessing and feature Extraction in "Normal or DDoS" classification algorithm fo

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Asmaa A. Alhussain. "DDoS Detection by Using Machine Learning." Journal of Information Systems Engineering and Management 10, no. 54s (2025): 142–51. https://doi.org/10.52783/jisem.v10i54s.11045.

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Distributed Denial of Service attack (DDoS) is the most risky attack in network security. DDoS attacks prevent essential services from operating normally for many online applications. With an increasing number of these attacks, the task of detection and mitigation has become increasingly challenging. Among the numerous methods available for detecting Distributed Denial of Service (DDoS) attacks, machine learning techniques have shown great promise in effectively identifying and preventing such attacks. In this project, the machine learning-based model was proposed to detect DDoS attacks. The p

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Hsieh, Chih-Hsiang, Wei-Kuan Wang, Cheng-Xun Wang, Shi-Chun Tsai, and Yi-Bing Lin. "Efficient Detection of Link-Flooding Attacks with Deep Learning." Sustainability 13, no. 22 (2021): 12514. http://dx.doi.org/10.3390/su132212514.

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The DDoS attack is one of the most notorious attacks, and the severe impact of the DDoS attack on GitHub in 2018 raises the importance of designing effective defense methods for detecting this type of attack. Unlike the traditional network architecture that takes too long to cope with DDoS attacks, we focus on link-flooding attacks that do not directly attack the target. An effective defense mechanism is crucial since as long as a link-flooding attack is undetected, it will cause problems over the Internet. With the flexibility of software-defined networking, we design a novel framework and im

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Lysenko, Sergii, Kira Bobrovnikova, Serhii Matiukh, Ivan Hurman, and Oleg Savenko. "Detection of the botnets’ low-rate DDoS attacks based on self-similarity." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3651. http://dx.doi.org/10.11591/ijece.v10i4.pp3651-3659.

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An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowle

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Thapanarath, Khempetch, and Wuttidittachotti Pongpisit. "DDoS attack detection using deep learning." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 382–88. https://doi.org/10.11591/ijai.v10.i2.pp382-388.

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Nowadays, IoT devices are widely used both in daily life and in corporate and industrial environments. The use of these devices has increased dramatically and by 2030 it is estimated that their usage will rise to 125 billion devices causing enormous flow of information. It is likely that it will also increase distributed denial-of-service (DDoS) attack surface. As IoT devices have limited resources, it is impossible to add additional security structures to it. Therefore, the risk of DDoS attacks by malicious people who can take control of IoT devices, remain extremely high. In this paper, we u

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Nashat, Dalia, Fatma A. Hussain, and Xiaohong Jiang. "Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio." Journal of Networking and Network Applications 1, no. 2 (2021): 67–74. http://dx.doi.org/10.33969/j-nana.2021.010204.

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Computer networks are vulnerable to many types of attacks while the Distributed Denial of Service attack (DDoS) serves as one of the top concerns for security professionals. The DDoS flooding attack denies the services by consuming the server resources to prevent the legitimate users from using their desired services. The hardness of detecting this attack lies in sending a stream of packets to the server with spoofed IP addresses, so that the internet routing infrastructure cannot distinguish the spoofed packets. Based on the odds ratio (OR) statistical measurement, in this work we propose a n

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Tay, Wei-Wu, Siew-Chin Chong, and Lee-Ying Chong. "DDoS Attack Detection with Machine Learning." Journal of Informatics and Web Engineering 3, no. 3 (2024): 190–207. http://dx.doi.org/10.33093/jiwe.2024.3.3.12.

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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 whic

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T. Ramya. "Review on DDOS Attacks in IOT Networks." Power System Technology 48, no. 4 (2024): 2400–2428. https://doi.org/10.52783/pst.1137.

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A Distributed Denial of Service (DDoS) attacks are harmful effort to disrupt standard networks, servers, or service operations by flooding the system with excessive Internet traffic. A DDoS attack sends a lot of traffic to the target at once utilizing several hacked computers or devices ("botnets"), robbing genuine users of their service. More connectivity and convenience across a range of industries has resulted from the widespread use of Internet of Things (IoT) devices. IoT networks are vulnerable to attacks due to their inherent vulnerabilities, and one major danger is DDoS attacks. In the

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Cheng, Jieren, Chen Zhang, Xiangyan Tang, Victor S. Sheng, Zhe Dong, and Junqi Li. "Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning." Security and Communication Networks 2018 (October 16, 2018): 1–19. http://dx.doi.org/10.1155/2018/5198685.

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Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the netw

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Kumar, Aman. "Distributed Denial of Service (DDoS) Attack Mitigation using AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5706–14. https://doi.org/10.22214/ijraset.2025.69632.

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Abstract: Distributed Denial of Service(DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emergingtechnologies, suchascloudcomputing, InternetofThings(IoT), artificialintelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Naive Bayes and Random Forest trees are two examples of artificial intelligence techniques that have been used to detect and categorize DDoS

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Han, Biao, Xiangrui Yang, Zhigang Sun, Jinfeng Huang, and Jinshu Su. "OverWatch: A Cross-Plane DDoS Attack Defense Framework with Collaborative Intelligence in SDN." Security and Communication Networks 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/9649643.

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Distributed Denial of Service (DDoS) attacks are one of the biggest concerns for security professionals. Traditional middle-box based DDoS attack defense is lack of network-wide monitoring flexibility. With the development of software-defined networking (SDN), it becomes prevalent to exploit centralized controllers to defend against DDoS attacks. However, current solutions suffer with serious southbound communication overhead and detection delay. In this paper, we propose a cross-plane DDoS attack defense framework in SDN, called OverWatch, which exploits collaborative intelligence between dat

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Harrsheeta, Sasikumar. "DDoS Attack Detection and Classification using Machine Learning Models with Real-Time Dataset Created." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 5 (2021): 145–53. https://doi.org/10.35940/ijrte.E5217.019521.

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<strong>Abstract</strong>: Distributed Denial of Service (DDoS) attack is one of the common attack that is predominant in the cyber world. DDoS attack poses a serious threat to the internet users and affects the availability of services to legitimate users. DDOS attack is characterized by the blocking a particular service by paralyzing the victim’s resources so that they cannot be used to legitimate purpose leading to server breakdown. DDoS uses networked devices into remotely controlled bots and generates attack. The proposed system detects the DDoS attack and malware with high detectio

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Sergii, Lysenko, Bobrovnikova Kira, Matiukh Serhii, Hurman Ivan, and Savenko Oleh. "Detection of the botnets' low-rate DDoS attacks based on self-similarity." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3651–59. https://doi.org/10.11591/ijece.v10i4.pp3651-3659.

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An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system–BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process

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Abdurohman, Maman, Dani Prasetiawan, and Fazmah Arif Yulianto. "Improving Distributed Denial of Service (DDOS) Detection using Entropy Method in Software Defined Network (SDN)." ComTech: Computer, Mathematics and Engineering Applications 8, no. 4 (2017): 215. http://dx.doi.org/10.21512/comtech.v8i4.3902.

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This research proposed a new method to enhance Distributed Denial of Service (DDoS) detection attack on Software Defined Network (SDN) environment. This research utilized the OpenFlow controller of SDN for DDoS attack detection using modified method and regarding entropy value. The new method would check whether the traffic was a normal traffic or DDoS attack by measuring the randomness of the packets. This method consisted of two steps, detecting attack and checking the entropy. The result shows that the new method can reduce false positive when there is a temporary and sudden increase in nor

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Kareem, Morenikeji Kabirat, Olaniyi Dada Aborisade, Saidat Adebukola Onashoga, Tole Sutikno, and Olaniyi Mathew Olayiwola. "Efficient model for detecting application layer distributed denial of service attacks." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2023): 441–50. http://dx.doi.org/10.11591/eei.v12i1.3871.

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The increasing advancement of technologies and communication infrastructures has been posing threats to the internet services. One of the most powerful attack weapons for disrupting web-based services is the distributed denial of service (DDoS) attack. The sophisticated nature of attack tools being created and used for launching attacks on target systems makes it difficult to distinguish between normal and attack traffic. Consequently, there is a need to detect application layer DDoS attacks from network traffic efficiently. This paper proposes a detection system coined eXtreme gradient boosti

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Shieh, Chin-Shiuh, Thanh-Tuan Nguyen, Wan-Wei Lin, Wei Kuang Lai, Mong-Fong Horng, and Denis Miu. "Detection of Adversarial DDoS Attacks Using Symmetric Defense Generative Adversarial Networks." Electronics 11, no. 13 (2022): 1977. http://dx.doi.org/10.3390/electronics11131977.

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DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a critical component of the creation of an integrated defensive system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers have a

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Aladaileh, Mohammad A., Mohammed Anbar, Iznan H. Hasbullah, and Yousef K. Sanjalawe. "Information Theory-based Approaches to Detect DDoS Attacks on Software-defined Networking Controller a Review." International Journal of Education and Information Technologies 15 (April 22, 2021): 83–94. http://dx.doi.org/10.46300/9109.2021.15.9.

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The number of network users and devices has exponentially increased in the last few decades, giving rise to sophisticated security threats while processing users’ and devices’ network data. Software-Defined Networking (SDN) introduces many new features, but none is more revolutionary than separating the control plane from the data plane. The separation helps DDoS attack detection mechanisms by introducing novel features and functionalities. Since the controller is the most critical part of the SDN network, its ability to control and monitor network traffic flow behavior ensures the network fun

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Wang, Jin, Liping Wang, and Ruiqing Wang. "MFFLR-DDoS: An encrypted LR-DDoS attack detection method based on multi-granularity feature fusions in SDN." Mathematical Biosciences and Engineering 21, no. 3 (2024): 4187–209. http://dx.doi.org/10.3934/mbe.2024185.

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<abstract> <p>Low rate distributed denial of service attack (LR-DDoS) is a special type of distributed denial of service (DDoS) attack, which uses the vulnerability of HTTP protocol to send HTTP requests to applications or servers at a slow speed, resulting in long-term occupation of server threads and affecting the normal access of legitimate users. Since LR-DDoS attacks do not need to send flooding or a large number of HTTP requests, it is difficult for traditional intrusion detection methods to detect such attacks, especially when HTTP traffic is encrypted. To overcome the above

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Alzahrani, Ahmed Saeed. "An Efficient DDoS Attack Detecting System using Levenberg-Marquardt Based Deep Artificial Neural Network Approach for IOT." International Journal of Innovative Technology and Exploring Engineering 10, no. 3 (2021): 59–66. http://dx.doi.org/10.35940/ijitee.c8356.0110321.

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The Internet of Things model envisions the widespread interconnection and collaboration of smart devices over the present and future Internet environment. Threats and attacks against IoT devices and services are on the rise due to their rapid development. Distributed-Denial-of-Service (DDoS) attacks are one of the main dangerous malwares that attack targeted organizations through infected devices. Many mechanisms are developed for IoT devices in order to detect DDoS attacks. Nonetheless, the prevailing DDoS Attack Detection (DAD) methods involve time-delay and a lower detection rate. This pape

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Ahmed, Saeed Alzahrani. "An Efficient DDoS Attack Detecting System using Levenberg-Marquardt Based Deep Artificial Neural Network Approach for IOT." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 3 (2021): 59–66. https://doi.org/10.35940/ijitee.C8356.0110321.

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The Internet of Things model envisions the widespread interconnection and collaboration of smart devices over the present and future Internet environment. Threats and attacks against IoT devices and services are on the rise due to their rapid development. Distributed-Denial-of-Service (DDoS) attacks are one of the main dangerous malwares that attack targeted organizations through infected devices. Many mechanisms are developed for IoT devices in order to detect DDoS attacks. Nonetheless, the prevailing DDoS Attack Detection (DAD) methods involve time-delay and a lower detection rate. This pape

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H K, Pradeep, Pavan Kumar, ,. Pradeepa A J, Prashantha S, and Saad Faisal Khan. "Detection Of DDOS Attack Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40595.

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- The Distributed Denial-of-Service (DDoS) attack is one of the most dangerous cyber threats, surpassing traditional Denial-of-Service (DoS) attacks due to its distributed nature, where multiple hosts collectively target a system, rendering its services inaccessible. Addressing this challenge requires an advanced and reliable detection mechanism. This research presents a machine learning-based approach for DDoS attack detection using Logistic Regression, Random Forest, and Neural Network classifiers. The proposed model is trained on a cleaned and pre-processed dataset with feature scaling to e

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Sukma Aji, Davito Rasendriya Rizqullah Putra, Imam Riadi, Abdul Fadlil, and Muhammad Nur Faiz. "A Classification Data Packets Using the Threshold Method for Detection of DDoS." Journal of Innovation Information Technology and Application (JINITA) 6, no. 1 (2024): 28–36. http://dx.doi.org/10.35970/jinita.v6i1.2224.

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Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process n

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