Relevant bibliographies by topics / DDoS attack detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'DDoS attack detection'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "DDoS attack detection"

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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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Dissertations / Theses on the topic "DDoS attack detection"

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Saied, Alan. "Distributed Denial of Service (DDoS) attack detection and mitigation." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/distributed-denial-of-service-ddos-attack-detection-and-mitigation(eaa45e51-f602-46da-a37a-75c3ae71d2db).html.

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A Distributed Denial of Service (DDoS) attack is an organised distributed packet-storming technique that aims to overload network devices and the communication channels between them. Its major objective is to prevent legitimate users from accessing networks, servers, services, or other computer resources. In this thesis, we propose, implement and evaluate a DDoS Detector approach consisting of detection, defence and knowledge sharing components. The detection component is designed to detect known and unknown DDoS attacks using an Artificial Neural Network (ANN) while the defence component prev
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Yu, Yue. "Resilience Strategies for Network Challenge Detection, Identification and Remediation." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10277.

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The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfig
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Goldschmidt, Patrik. "Potlačení DoS útoků s využitím strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449294.

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Útoky typu odoprenia služby (DDoS) sú v dnešných počítačových sieťach stále frekventovanejším bezpečnostným incidentom. Táto práca sa zameriava na detekciu týchto útokov a poskytnutie relevantných informácii za účelom ich mitigácie v reálnom čase. Spomínaná funkcionalita je dosiahnutá s využitím techník prúdového dolovania z dát a strojového učenia. Výsledkom práce je sada nástrojov zastrešujúca celý proces strojového učenia - od vlastnej extrakcie príznakov cez predspracovanie dát až po export natrénovaného modelu pripraveného na nasadenie v produkcii. Experimentálne výsledky vyhodnotené na v
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Abdelaty, Maged Fathy Youssef. "Robust Anomaly Detection in Critical Infrastructure." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/352463.

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Critical Infrastructures (CIs) such as water treatment plants, power grids and telecommunication networks are critical to the daily activities and well-being of our society. Disruption of such CIs would have catastrophic consequences for public safety and the national economy. Hence, these infrastructures have become major targets in the upsurge of cyberattacks. Defending against such attacks often depends on an arsenal of cyber-defence tools, including Machine Learning (ML)-based Anomaly Detection Systems (ADSs). These detection systems use ML models to learn the profile of the normal behavio
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Syed, Naeem Firdous. "IoT-MQTT based denial of service attack modelling and detection." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2303.

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Internet of Things (IoT) is poised to transform the quality of life and provide new business opportunities with its wide range of applications. However, the bene_ts of this emerging paradigm are coupled with serious cyber security issues. The lack of strong cyber security measures in protecting IoT systems can result in cyber attacks targeting all the layers of IoT architecture which includes the IoT devices, the IoT communication protocols and the services accessing the IoT data. Various IoT malware such as Mirai, BASHLITE and BrickBot show an already rising IoT device based attacks as well a
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Кульчицький, Б. В., та Л. М. Куперштейн. "До проблеми формування набору даних для дослідження DDoS-атак". Thesis, ВНТУ, 2019. http://ir.lib.vntu.edu.ua//handle/123456789/24232.

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В роботі розглянуто підходи щодо перевірки запропонованих методів виявлення атак. Проаналізовано наявні набори даних, які використовуються для створення систем виявлення DDoS-атак. Також, проаналізовано декілька інструментів, що використовуються для реалізації чи моделювання DDoS-атак для збору даних.<br>The paper considers approaches to checking the proposed method of detecting attacks. The existing datasets that scientists use to create DDoS-attack detection systems are analyzed. Also, there are several tools used to implement or simulate DDoS-attacks for data collection
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Náčin, Peter. "Detekce útoku SlowDrop." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442391.

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The diploma thesis is focused on the detection of a slow DoS attack named SlowDrop. The attack tries to imitate a legitimate person with a slow internet connection and does not show a new strong signature, so the attack is difficult to detect. The diploma thesis is based on the work of Ing. Mazanek in which the SlowDrop attack script was created. At the theoretical level, the issue of DoS attacks is described in general, but also in particular. Furthermore, the work develops methods for solving the problem of SlowDrop attack detection. The methods are then defined in detail and tested in a sim
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Guerid, Hachem. "Systèmes coopératifs décentralisés de détection et de contre-mesures des incidents et attaques sur les réseaux IP." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0079/document.

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La problématique des botnets, réseaux de machines infectées par des logiciels malveillants permettant de les contrôler à distance, constitue une préoccupation majeure du fait du nombre de machines infectées et des menaces associées: attaque par déni de service distribué (DDoS), spam, vol de données bancaires. Les solutions de lutte contre les botnets proposées présentent des limitations majeures dans le contexte d'un opérateur réseau (contraintes de volumétrie et de passage à l'échelle, respect de la confidentialité et de la vie privée des utilisateurs). Cette thèse propose quatre contribution
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Damour, Gabriel. "Information-Theoretic Framework for Network Anomaly Detection: Enabling online application of statistical learning models to high-speed traffic." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252560.

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With the current proliferation of cyber attacks, safeguarding internet facing assets from network intrusions, is becoming a vital task in our increasingly digitalised economies. Although recent successes of machine learning (ML) models bode the dawn of a new generation of intrusion detection systems (IDS); current solutions struggle to implement these in an efficient manner, leaving many IDSs to rely on rule-based techniques. In this paper we begin by reviewing the different approaches to feature construction and attack source identification employed in such applications. We refer to these ste
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Guerid, Hachem. "Systèmes coopératifs décentralisés de détection et de contre-mesures des incidents et attaques sur les réseaux IP." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0079.

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La problématique des botnets, réseaux de machines infectées par des logiciels malveillants permettant de les contrôler à distance, constitue une préoccupation majeure du fait du nombre de machines infectées et des menaces associées: attaque par déni de service distribué (DDoS), spam, vol de données bancaires. Les solutions de lutte contre les botnets proposées présentent des limitations majeures dans le contexte d'un opérateur réseau (contraintes de volumétrie et de passage à l'échelle, respect de la confidentialité et de la vie privée des utilisateurs). Cette thèse propose quatre contribution
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Books on the topic "DDoS attack detection"

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Bhattacharyya, Dhruba Kumar, and Jugal Kumar Kalita. DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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Bhattacharyya, Dhruba Kumar, and Jugal Kumar Kalita. DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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DDoS Attacks: Evolution, Detection, Prevention, Reaction, and Tolerance. Taylor & Francis Group, 2016.

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Book chapters on the topic "DDoS attack detection"

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Yu, Shui. "DDoS Attack Detection." In Distributed Denial of Service Attack and Defense. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9491-1_3.

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Szynkiewicz, Paweł. "Signature-Based Detection of Botnet DDoS Attacks." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_6.

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AbstractThe distributed denial of service (DDoS) attack is an attempt to disrupt the proper availability of a targeted server, service or network. The attack is achieved by corrupting or overwhelming the target’s communications with a flood of malicious network traffic. In the current era of mass connectivity DDoS attacks emerge as one of the biggest threats, staidly causing greater collateral damage and heaving a negate impacting on the integral Internet Infrastructure. DDoS attacks come in a variety of types and schemes, they continue to evolve, steadily becoming more sophisticated and large
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Swati, Jadhav, Pise Nitin, Shruti Singh, Akash Sinha, Vishal Sirvi, and Shreyansh Srivastava. "DDoS Attack Detection Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5997-6_34.

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Antad, Sonali M., Rucha Uplenchwar, Pratham Gajbhiye, Dakshata Wasnik, and Omkar Pawar. "DDoS Attack Detection Using Machine Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2697-7_42.

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Smriti, Smriti, K. HariBabu, and Sanyam Garg. "DDoS Attack Detection in Data Plane." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87784-1_22.

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Wang, An, Aziz Mohaisen, Wentao Chang, and Songqing Chen. "Capturing DDoS Attack Dynamics Behind the Scenes." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20550-2_11.

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Shalini, P. V., V. Radha, and Sriram G. Sanjeevi. "DDoS Attack Detection in SDN Using CUSUM." In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8767-2_26.

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Mouli, V. S. A. Chandra, P. Subba Rao, Shubhashish Jena, et al. "DDOS Attack Detection Using Time Based Features." In Computing, Communication and Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003581215-3.

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Anis, Adeeba, and Md Shohrab Hossain. "DDoS Attack Detection Using Ensemble Machine Learning." In Artificial Intelligence and Sustainable Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0327-2_39.

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Zeng, Fantao, Jieren Cheng, Zhuyun Cao, Yue Yang, and Victor S. Sheng. "AcLGB: A Lightweight DDoS Attack Detection Method." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_16.

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Conference papers on the topic "DDoS attack detection"

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S, Jaya Praveena, and S.Sudha. "ARP Spoofing Attack Detection to Prevent DDoS Attack." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10987987.

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Chi, Kaiwen, Xiaohui Xie, Yannan Hu, et al. "E-DDoS: An Evaluation System for DDoS Attack Detection." In 2024 IEEE 32nd International Conference on Network Protocols (ICNP). IEEE, 2024. https://doi.org/10.1109/icnp61940.2024.10858578.

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Jadhav, Swati, Vaibhavi Bhosale, Gauri Choudhari, Rishita Bura, and Manasi Bhavik. "DDoS Attack Detection in Blockchain Networks." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810961.

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Aggarwal, Saransh, Bhagrajyoti Behera, Murari Kumar Singh, and Ajeet Kumar Sharma. "Optimizing DDoS Attack Detection Using Machine Learning." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932452.

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Mahmoodi, Meisam, and Seyed Mahdi Jameii. "Utilizing Large Language Models for DDoS Attack Detection." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10688345.

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Liu, Cuilian, and Sirong Zhong. "DDoS Attack Detection Method Based on Machine Learning." In 2024 IEEE 15th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2024. http://dx.doi.org/10.1109/icsess62520.2024.10719386.

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Mishra, Amit Kumar, Siddhant Thapliyal, Junedh Siddiqui, Rohit Bhatt, Keshav Naithani, and Ankit Joshi. "A Review: DDoS Attack Detection Using Clustering Algorithms." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730005.

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Zhi, Haiyou, Jingxian Li, Mengyue Yu, Jin Wang, Ziyan Hu, and Yihan Li. "DDoS Attack Detection Method Based on Improved Bagging." In 2024 4th International Conference on Communication Technology and Information Technology (ICCTIT). IEEE, 2024. https://doi.org/10.1109/icctit64404.2024.10928599.

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Sravya, Mudigonda Lakshmi, Mridhulaa S S, Sindhu Ravindran, and Saidatul Ardeenawatie. "ACNN-LSTM Based DDoS Attack Detection and Classification." In 2024 1st International Conference on Sustainability and Technological Advancements in Engineering Domain (SUSTAINED). IEEE, 2024. https://doi.org/10.1109/sustained63638.2024.11073967.

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Zhang, Ruo, Guiqin Yang, and Wei Zhang. "DDoS Attack Detection System Based on GBDT Under SDN." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733143.

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