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Journal articles on the topic 'Ddos'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Abdelhaq, Maha, Raed Alsaqour, Mada Alaskar, et al. "The resistance of routing protocols against DDOS attack in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4844. http://dx.doi.org/10.11591/ijece.v10i5.pp4844-4852.

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A Mobil Ad hoc Network (MANET) is a wireless multi-hop network with various mobile, self-organized and wireless infrastructure nodes. MANET characteristics such as openness restricted resources and decentralization impact node efficiency and made them easy to be affected by various security attacks, especially Distributed Denial of Service (DDoS) attacks. The goal of this research is to implement a simulation model called DDoS Attack Simulation Model (DDoSM) in Network Simulator 2(NS-2) and to examine the effect of DDoS Attack on various routing protocol types in MANET namely: Zone Routing Pro

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Maha, Abdelhaq, Alsaqour Raed, Alaskar Mada, et al. "The resistance of routing protocols against DDOS attack in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4844–52. https://doi.org/10.11591/ijece.v10i5.pp4844-4852.

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A Mobil Ad hoc Network (MANET) is a wireless multi-hop network with various mobile, self-organized and wireless infrastructure nodes. MANET characteristics such as openness restricted resources and decentralization impact node efficiency and made them easy to be affected by various security attacks, especially Distributed Denial of Service (DDoS) attacks. The goal of this research is to implement a simulation model called DDoS Attack Simulation Model (DDoSM) in Network Simulator 2(NS-2) and to examine the effect of DDoS Attack on various routing protocol types in MANET namely: Zone Routing Pro

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3

Mazur, Katarzyna, Bogdan Ksiezopolski, and Radoslaw Nielek. "Multilevel Modeling of Distributed Denial of Service Attacks in Wireless Sensor Networks." Journal of Sensors 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5017248.

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The growing popularity of wireless sensor networks increases the risk of security attacks. One of the most common and dangerous types of attack that takes place these days in any electronic society is a distributed denial of service attack. Due to the resource constraint nature of mobile sensors, DDoS attacks have become a major threat to its stability. In this paper, we established a model of a structural health monitoring network, being disturbed by one of the most common types of DDoS attacks, the flooding attack. Through a set of simulations, we explore the scope of flood-based DDoS attack

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4

Hunt, Nicholas, Tom Bergan, Luis Ceze, and Steven D. Gribble. "DDOS." ACM SIGARCH Computer Architecture News 41, no. 1 (2013): 499–508. http://dx.doi.org/10.1145/2490301.2451170.

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5

Hunt, Nicholas, Tom Bergan, Luis Ceze, and Steven D. Gribble. "DDOS." ACM SIGPLAN Notices 48, no. 4 (2013): 499–508. http://dx.doi.org/10.1145/2499368.2451170.

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6

Jing, Hengchang, and Jian Wang. "Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features." Security and Communication Networks 2022 (March 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/1401683.

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Network available and accessible is of great importance to the Internet of things (IoT) devices. In this study, a novel machine learning method is presented to predict the occurrence of distributed denial-of-service (DDoS) attacks. Firstly, a structure of edges and vertices within graph theory is created to simultaneously extract traffic data characteristics. Eight characteristics of traffic data are selected as input variables. Secondly, the principal component analysis (PCA) model is adopted to extract DDoS and normal communication features further. Then, DDoSs are detected by fuzzy C-means

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7

Acharya, Saket, and Nitesh Pradhan. "DDoS Simulation and Hybrid DDoS Defense Mechanism." International Journal of Computer Applications 163, no. 9 (2017): 20–24. http://dx.doi.org/10.5120/ijca2017913736.

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8

Abdullayeva, Fargana J. "Convolutional Neural Network-Based Automatic Diagnostic System for AL-DDoS Attacks Detection." International Journal of Cyber Warfare and Terrorism 12, no. 1 (2022): 1–15. http://dx.doi.org/10.4018/ijcwt.305242.

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Distributed denial of service (DDoS) attacks are one of the main threats to information security. The purpose of DDoS attacks at the network (IP) and transport (TCP) layers is to consume the network bandwidth and deny service to legitimate users of the target system. Application layer DDoS attacks (AL-DDoS) can be organized against many different applications. Many of these attacks target HTTP, in which case their goal is to deplete the resources of web services. Various schemes have been proposed to detect DDoS attacks on network and transport layers. There are very few works being done to de

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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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10

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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11

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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12

Muhammad, Arif Wirawan, Imam Riadi, and Sunardi Sunardi. "Deteksi Serangan DDoS Menggunakan Neural Network dengan Fungsi Fixed Moving Average Window." JISKA (Jurnal Informatika Sunan Kalijaga) 1, no. 3 (2017): 115. http://dx.doi.org/10.14421/jiska.2017.13-03.

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Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume, intensitas, dan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi serangan DDoS, berdasarkan pada karakteristik aktivitas jaringan menggunakan neural network dengan fungsi fixed moving average window (FMAW) sebagai metode deteksi. Data pelatihan dan pengujian diambil dari CAIDA DDoS Attack 2007 dan simulasi mandiri. Pengujian terhadap metode neural network dengan fungsi fixed moving average window (FMAW

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13

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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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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15

Shieh, Chin-Shiuh, Thanh-Tuan Nguyen, Wan-Wei Lin, et al. "Detection of Adversarial DDoS Attacks Using Generative Adversarial Networks with Dual Discriminators." Symmetry 14, no. 1 (2022): 66. http://dx.doi.org/10.3390/sym14010066.

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DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems’ security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack–adversarial DDoS attack. There are established works on ML-b

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16

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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17

Solichin, Achmad, and Ludi Nugroho. "Deteksi Dini Gangguan Jaringan Distributed Denial Of Service (DDOS) Menggunakan Metode Shannon Entropy Pada Software Defined Network (SDN)." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 3 (2024): 461–74. http://dx.doi.org/10.25126/jtiik.938188.

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Software Defined Networking (SDN) adalah arsitektur jaringan baru yang memisahkan antara control dan data plane. Aspek keamanan utama dalam control plane salah satunya adalah serangan DoS dan DDoS. Serangan DDoS mengakibatkan terjadinya penurunan performa jaringan yang berjalan sangat lambat. Serangan DDoS dilakukan dengan menyusupi dan membanjiri bandwidth ke sumber daya target, sehingga dapat menyebabkan penolakan layanan bagi pengguna yang mengaksesnya. Tak hanya itu, serangan DDoS menyebabkan penurunan sumber daya jaringan seperti kapasitas memory dan CPU. Akibatnya kerusakan signifikan pa

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18

Fakiha, Bandr. "DETECTING DISTRIBUTED DENIAL OF SERVICES USING MACHINE LANGUAGE LEARNING TECHNIQUES." Journal of Southwest Jiaotong University 57, no. 5 (2022): 675–88. http://dx.doi.org/10.35741/issn.0258-2724.57.5.55.

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Vulnerabilities caused by cyberattacks impact negatively on the increased dependence of society on information and communication technologies (ICT) to conduct personal and business functions. An example of such an attack is the distributed denial of service (DDoS). This attack can disrupt business communication with clients and frustrate staff because of its potential to reduce connectivity and exchange of information between companies and their clients. To prevent these attacks, their modus operandi needs to be examined. Studies also must examine the latest trends of tactics used by DDoS atta

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19

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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20

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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21

Lestari, Wulan Sri. "DETEKSI SERANGAN DDoS MENGGUNAKAN Q-LEARNING." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 648–58. http://dx.doi.org/10.35957/jatisi.v9i1.1473.

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Distributed Denial of Service Attack (DDoS) adalah serangan dengan mengkompilasi beberapa sistem di internet dengan zombie/agen yang terinfeksi dan membentuk jaringan botnet. Serangan DDoS mengakibatkan kerugian finansial, hilangnya produktivitas, kerusakan merek, penurunan peringkat kredit dan asuransi serta terganggunya hubungan pelanggan, dan pemasok. Selain itu, teknologi IoT juga rentan terhadap serangan DDoS berskala besar. Untuk mencegah terjadinya serangan DDoS maka dibutuhkan model yang dapat mendeteksi adanya serangan DDoS. Pada penelitian ini, kami mengusulkan Deep Q-Network (DQN) u

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22

Ravichandran, S., and M. Umamaheswari. "Design and Development of Collaborative Detection and Taxonomy of DDoS Attacks Using ESVM." Asian Journal of Computer Science and Technology 6, no. 2 (2017): 27–32. http://dx.doi.org/10.51983/ajcst-2017.6.2.1783.

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Distributed Denial of Service (DDoS) assault is a ceaseless basic risk to the web. Application layer DDoS Attack is gotten from the lower layers. Application layer based DDoS assaults utilize honest to goodness HTTP asks for after foundation of TCP three-way handshaking and overpowers the casualty assets, for example, attachments, CPU, memory, circle, database transfer speed. Arrange layer based DDoS assaults sends the SYN, UDP and ICMP solicitations to the server and debilitates the transfer speed. An oddity discovery system is proposed in this paper to identify DDoS assaults utilizing Enhanc

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23

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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Pangestu, Raka Nugraha, Risma Yanti, and Herlina Harahap. "Implementasi Keamanan Jaringan Berbasis VPN dan Anti - DDoS dalam Melindungi Server Linux dari Serangan Hammer." Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) 3, no. 1 (2021): 27–33. http://dx.doi.org/10.35447/jikstra.v3i1.357.

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Although there are many security methods that can be used to block DDoS attacks, for now these methods are arguably less effective, because many hackers have known bugs from the system or security methods. From the background of this problem, testing the security of Linux Mint servers, Linux Mint without VPN and Anti DDoS, implementing network security from hammer attacks on Linux Mint Linux servers with VPN and Anti-DDoS, and testing DDoS and VPN attacks have been successfully implemented. on linux mint. Anti-DDoS and VPN are able to prevent DDoS attacks on Linux servers by blocking unknown I

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SARAH, NAIEM, MARIE MOHAMED, E. KHEDR AYMAN, and M. IDREES AMIRA. "DISTRIBUTED DENIAL OF SERVICES ATTACKS AND THEIR PREVENTION IN CLOUD SERVICES." Journal of Theoretical and Applied Information Technology 100, no. 4 (2022): 1170–81. https://doi.org/10.5281/zenodo.6856824.

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Distributed Denial of services (DDOS) attacks are one of the most famous attacks that affect the availability of a service making it a serious problem especially when it comes to cloud computing as it is becoming a bigger part of our lives. Throughout this paper, we first discussed the DDOS types, categories, and approaches in terms of the targeted area of the cloud or the intensity of the attacks whether it’s the normal DDOS, the Low-rate DDOS, or Economic-DOS (EDOS). We then presented a comparative analysis between the recent studies discussing the DDOS attacks in cloud. Prevention of

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AL-Adaileh, Mohammad A., Mohammed Anbar, Yung-Wey Chong, and Ahmed Al-Ani. "Proposed statistical-based approach for detecting distribute denial of service against the controller of software defined network (SADDCS)." MATEC Web of Conferences 218 (2018): 02012. http://dx.doi.org/10.1051/matecconf/201821802012.

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Software-defined networkings (SDNs) have grown rapidly in recent years be-cause of SDNs are widely used in managing large area networks and securing networks from Distributed Denial of Services (DDoS) attacks. SDNs allow net-works to be monitored and managed through centralized controller. Therefore, SDN controllers are considered as the brain of networks and are considerably vulnerable to DDoS attacks. Thus, SDN controller suffer from several challenges that exhaust network resources. For SDN controller, the main target of DDoS attacks is to prevent legitimate users from using a network resou

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Zhang, Chunming. "Impact of Defending Strategy Decision on DDoS Attack." Complexity 2021 (March 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/6694383.

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Distributed denial-of-service (DDoS) attack is a serious threat to cybersecurity. Many strategies used to defend against DDoS attacks have been proposed recently. To study the impact of defense strategy selection on DDoS attack behavior, the current study uses logistic function as basis to propose a dynamic model of DDoS attacks with defending strategy decisions. Thereafter, the attacked threshold of this model is calculated. The existence and stability of attack-free and attacked equilibria are proved. Lastly, some effective strategies to mitigate DDoS attacks are suggested through parameter

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Sasikumar, Harrsheeta. "DDoS Attack Detection and Classification using Machine Learning Models with Real-Time Dataset Created." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 145–53. http://dx.doi.org/10.35940/ijrte.e5217.019521.

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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 detection accuracy using machine learning

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29

Singh, Rajeev, and T. P. Sharma. "Present Status of Distributed Denial of Service (DDoS) Attacks in Internet World." International Journal of Mathematical, Engineering and Management Sciences 4, no. 4 (2019): 1008–17. http://dx.doi.org/10.33889/ijmems.2019.4.4-080.

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Distributed Denial of Service (DDoS) attack harms the digital availability in Internet. The user’s perspective of getting quick and effective services may be badly hit by the DDoS attackers. There are several reports of DDoS attack incidences that have caused devastating effects on the user and web services in the Internet world. In the present digital world dominated by wireless, mobile and IoT devices, the numbers of users are increasing day by day. Most of the users are novice and therefore their devices either fell prey to DDoS attacks or unknowingly add themselves to the DDoS attack Army.

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Narote, Prof Amit, Vamika Zutshi, Aditi Potdar, and Radhika Vichare. "Detection of DDoS Attacks using Concepts of Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 390–94. http://dx.doi.org/10.22214/ijraset.2022.43723.

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Abstract: Distributed Denial-of-Service (DDoS) assaults are the terrorizing preliminaries on the Internet that exhaust the organization transmission capacity. Analysts have presented different safeguard components including assault counteraction, traceback, response, identification, and portrayal against DDoS assaults, however the quantity of these assaults builds consistently, and the ideal answers for this issue have escaped us up to this point. An order of identification approaches against DDoS assaults is given the point of giving profound understanding into the DDoS problem. Although the

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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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32

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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Praba. J., Jeba, and R. Sridaran. "LCDT-M: Log-Cluster DDoS Tree Mitigation Framework Using SDN in the Cloud Environment." International Journal of Computer Network and Information Security 15, no. 2 (2023): 62–72. http://dx.doi.org/10.5815/ijcnis.2023.02.05.

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In the cloud computing platform, DDoS (Distributed Denial-of-service) attacks are one of the most commonly occurring attacks. Research studies on DDoS mitigation rarely considered the data shift problem in real-time implementation. Concurrently, existing studies have attempted to perform DDoS attack detection. Nevertheless, they have been deficient regarding the detection rate. Hence, the proposed study proposes a novel DDoS mitigation scheme using LCDT-M (Log-Cluster DDoS Tree Mitigation) framework for the hybrid cloud environment. LCDT-M detects and mitigates DDoS attacks in the Software-Def

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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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ЯНКО, Аліна, Андрій ПРОКУДІН, Ілля ФІЛЬ та Олег КРУК. "ВИЯВЛЕННЯ АТАК ТИПУ LDDOS ЗА ДОПОМОГОЮ SDN МЕРЕЖ З ЕЛЕМЕНТАМИ МАШИННОГО НАВЧАННЯ". MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, № 4 (28 листопада 2024): 287–96. https://doi.org/10.31891/2219-9365-2024-80-36.

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Стаття присвячується виявленню розподілених атак на відмову в обслуговуванні (DDoS), які є серйозною загрозою для комп’ютерних мереж. У даному дослідженні розглянуто можливість виявлення атак типу low-rate DDoS з використанням машинного навчання на основі програмно-конфігурованих мереж (SDN). Технології машинного навчання (ML) та глибинного навчання (DL) у поєднанні з SDN демонструють значний потенціал у ефективній протидії цим мережевим загрозам. Попередні дослідження переважно зосереджувались на високочастотних DDoS-атаках, ігноруючи низькочастотні DDoS-атаки, які схожі на легітимний трафік,

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Widagdo, Gede Barkah, and Charles Lim. "Analysis of Hybrid DDoS Defense to Mitigate DDoS Impact." Advanced Science Letters 23, no. 4 (2017): 3633–39. http://dx.doi.org/10.1166/asl.2017.9004.

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Mirkovic, Jelena, and Peter Reiher. "A taxonomy of DDoS attack and DDoS defense mechanisms." ACM SIGCOMM Computer Communication Review 34, no. 2 (2004): 39–53. http://dx.doi.org/10.1145/997150.997156.

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Onyeabor, Uchechukwu Solomon, and Joshua Ojo Nehinbe. "An exhaustive study of DDOS attacks and DDOS datasets." International Journal of Internet Technology and Secured Transactions 10, no. 3 (2020): 268. http://dx.doi.org/10.1504/ijitst.2020.10028403.

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Nehinbe, Joshua Ojo, and Uchechukwu Solomon Onyeabor. "An exhaustive study of DDOS attacks and DDOS datasets." International Journal of Internet Technology and Secured Transactions 10, no. 3 (2020): 268. http://dx.doi.org/10.1504/ijitst.2020.107075.

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Mishra, Nivedita, Sharnil Pandya, Chirag Patel, et al. "Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications." Sensors 21, no. 23 (2021): 8071. http://dx.doi.org/10.3390/s21238071.

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Distributed denial-of-service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, t

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Sachdeva, Monika, and Krishan Kumar. "A Traffic Cluster Entropy Based Approach to Distinguish DDoS Attacks from Flash Event Using DETER Testbed." ISRN Communications and Networking 2014 (May 13, 2014): 1–15. http://dx.doi.org/10.1155/2014/259831.

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The detection of distributed denial of service (DDoS) attacks is one of the hardest problems confronted by the network security researchers. Flash event (FE), which is caused by a large number of legitimate requests, has similar characteristics to those of DDoS attacks. Moreover DDoS attacks and FEs require altogether different handling procedures. So discriminating DDoS attacks from FEs is very important. But the research involving DDoS detection has not laid enough emphasis on including FEs scenarios in the experiments. In this paper, we are using traffic cluster entropy as detection metric

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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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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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Qamar, Roheen. "Deep Defense: Using Radial Basis Neural Networks for Mitigating the DDoS Attacks." Sukkur IBA Journal of Computing and Mathematical Sciences 8, no. 2 (2025): 25–36. https://doi.org/10.30537/sjcms.v8i2.1473.

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The SDN architecture supports the detection and mitigation of DDoS attacks as soon as possible, which is difficult in a conventional network. The SDN controller identifies DDoS attacks in their early phases and mitigates their impact on the entire network using proper identification patterns and detection schemes. This study presents a feature set for distinguishing DDoS attacks from regular traffic. It provides a system paradigm for detecting DDoS assaults using an SDN controller. The system model's detection module is built on an RBF neural network, which is then compared to other methodolog

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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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Ulemale, Tejaswini. "Review on Detection of DDOS Attack using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 764–68. http://dx.doi.org/10.22214/ijraset.2022.40742.

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Abstract: Now days due to digitalization the drastic increment towards Computer technology and Computer network is increased rapidly. Various types of works are turned into Online Mode so due to increment in network connection various attack are been perform. One of the dangerous and powerful attack is Distributed Denial of Service [DDOS] is one of the major issues in the computer network. The Attacker target the server with the help of DDOS and tries to interrupt normal traffic. The Denial-ofservice consist of subclass which includes Distributed denial of service. Therefore, to prevent the DD

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Tekleselassie, Hailye. "A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning." MATEC Web of Conferences 348 (2021): 01012. http://dx.doi.org/10.1051/matecconf/202134801012.

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This research presents a novel combined learning method for developing a novel DDoS model that is expandable and flexible property of deep learning. This method can advance the current practice and problems in DDoS detection. A combined method of deep learning with knowledge-graph classification is proposed for DDoS detection. Whereas deep learning algorithm is used to develop a classifier model, knowledge-graph system makes the model expandable and flexible. It is analytically verified with CICIDS2017 dataset of 53.127 entire occurrences, by using ten-fold cross validation. Experimental outco

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Zhao, Yuntao, Hengchi Liu, and Yongxin Feng. "An Algorithm of Traffic Perception of DDoS Attacks against SOA Based on Time United Conditional Entropy." Journal of Electrical and Computer Engineering 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/2579274.

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DDoS attacks can prevent legitimate users from accessing the service by consuming resource of the target nodes, whose availability of network and service is exposed to a significant threat. Therefore, DDoS traffic perception is the premise and foundation of the whole system security. In this paper the method of DDoS traffic perception for SOA network based on time united conditional entropy was proposed. According to many-to-one relationship mapping between the source IP address and destination IP addresses of DDoS attacks, traffic characteristics of services are analyzed based on conditional

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Suharti, Sri, Anton Yudhana, and Imam Riadi. "Forensik Jaringan DDoS menggunakan Metode ADDIE dan HIDS pada Sistem Operasi Proprietary." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 21, no. 3 (2022): 567–82. http://dx.doi.org/10.30812/matrik.v21i3.1732.

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Forensik jaringan sangat dibutuhkan dalam mempertahankan kinerja jaringan komputer dari serangan Distributed Denial of Service (DDoS). Penelitian ini bertujuan untuk mendapatkan bukti digital keakurasian tool DDoS, keberhasilan metode HIDS dan implementasi firewall pada Network layer dalam menghentikan DDoS. Metode penelitian ini menerapkan ADDIE (Analyze, Design, Develop, Implement and Evaluate) dan Host-Based Intrusion Detection System (HIDS) Snort pada simulasi jaringan berbasis lokal dan luas. Hasil pengujian menyatakan Slowloris merupakan DDoS paling melumpuhkan web server IIS pada sistem

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