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Journal articles on the topic 'Flooding distributed denial of service attacks'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Wang, Haojun. "Distributed Denial of Service Attack with Large Language Model." Highlights in Science, Engineering and Technology 138 (May 11, 2025): 132–37. https://doi.org/10.54097/586gg060.

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Distributed Denial of Service (DDoS) attacks take full advantage of distributed networks by sending a relentless barrage of requests to a target server to disrupt the regular operation of the server. The main difference between a DDoS attack and a traditional Denial of Service (DoS) attack is its decentralized nature. This characteristic increases the attack's impact and thus creates incredible difficulty in prevention. Traditional DDoS strategies cover flooding attacks (e.g., TCP SYN and UDP floods), protocol usage techniques (e.g., SYN floods and the infamous Ping of Death), and resource exh

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Ali, Raza. "Detection of DDoS attack in cloud computing and its prevention: a systematic review." i-manager’s Journal on Cloud Computing 9, no. 1 (2022): 1. http://dx.doi.org/10.26634/jcc.9.1.18542.

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Cloud computing is one of the latest and greatest environments for delivering Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) in digital communications infrastructure. Cloud computing helps the user remotely access the required service as needed through the Internet. But this technological advancement, due to its remote availability in the cloud, leads to new attacks. One of the biggest threats to cloud infrastructure is Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks are clearly trying to exploit the availability

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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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Ghazali. "Flooding Distributed Denial of Service Attacks-A Review." Journal of Computer Science 7, no. 8 (2011): 1218–23. http://dx.doi.org/10.3844/jcssp.2011.1218.1223.

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SINGH, SATWINDER, ABHINAV BHANDARI, KRISHAN KUMAR SALUJA, and A. L. SANGAL. "Study to Validate the Performance of Flooding Based Distributed Denial of Service Attacks." International Journal of Computer Networks and Communications Security 8, no. 1 (2020): 1–9. http://dx.doi.org/10.47277/ijcncs/8(1)1.

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Network technology has experienced explosive growth in the past decades. The generally accepted viewpoint in the security world is that no system or network is totally protected which makes network security an important concern. The work done in this paper focuses on Distributed Denial of Service Attacks (DDoS) where legitimate users are prevented from accessing network services. Distributed Denial of Service (DDoS) Attacks has been increasingly found to be disturbing the normal working of organizations causing billions of rupees of losses. Organizations are trying their best to reduce their l

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Ramli, Hartini, and Maharaja Yasin Alifsyah. "Analisis Keamanan Komputer Terhadap Serangan Distributed Denial of Service (DDOS)." Journal of Renewable Energy and Smart Device 1, no. 1 (2023): 25–30. http://dx.doi.org/10.61220/joresd.v1i1.235.

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Distributed Denial of Service (DDoS) is a type of active attack, an attack that can overwhelm a system by flooding a computer or server with network traffic, disrupting user services. The goal of this attack is usually to disable services and disconnect from the compromised computer or network. The impact is very large for companies or agencies that offer services. Victims of these attacks are unable to provide the services they are supposed to. Due to a bug or constraint on the server you are trying to use and one of the ways to deal with these attacks is to use a computer network fir

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R., Ramkumar, Rahul R., and Gowtham Sri. "Anomaly Based Approach for Defending Denial of Service Attack in Web Traffic." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 04 (2015): 1657–64. https://doi.org/10.5281/zenodo.14776346.

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Distributed Denial of Service (DDOS) attacks has become a great threat for internet security. This attackis an advanced form of DOS (Denial of Service) attack. This attack changes its whole origin ID and it gives trouble to find it out and it has become a serious threat for internet security. Almost all traditional services such as bank websites, power resources, medical, educational institutions and military are extended to World Wide Web and thus many people widely use internet services. As many users of Internet is mandatory, network security for attacks are also increasing. Current DDoS at

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Khalaf, Bashar Ahmad, Salama A. Mostafa, Aida Mustapha, et al. "An Adaptive Protection of Flooding Attacks Model for Complex Network Environments." Security and Communication Networks 2021 (April 22, 2021): 1–17. http://dx.doi.org/10.1155/2021/5542919.

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Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the

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Saurabh Kansal. "DISTRIBUTED DENIAL OF SERVICE ATTACK MITIGATION USING REINFORCEMENT LEARNING." Journal of Sustainable Solutions 2, no. 1 (2025): 11–18. https://doi.org/10.36676/j.sust.sol.v2.i1.54.

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Cybersecurity is threatened by Distributed Denial of Service (DDoS) attacks that destabilize network services by flooding systems with wrongful traffic. Unlike more conventional threat countermeasures, they fail to manage dynamic attack trajectories. In contrast, reinforcement learning provides a dynamic approach since systems improve their learning and response to the emerging threats in a real-time exercise. In this paper, reinforcement learning is used to study DDoS attack prevention and the study including the method, data set and measure used is discussed. Primary conclusions confirm stra

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Narote, Prof Amit, Vamika Zutshi, Aditi Potdar, and Radhika Vichare. "D-Dos Attack Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 2303–12. http://dx.doi.org/10.22214/ijraset.2022.41131.

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Abstract: The risk of cyber-attack keeps on growing irrespective of development of new technologies for protection. One of the most frequent cyber-attacks is the DOS attack. A Denial-of-Service (DoS) attack is an attack which tries to shut down a machine or network, by flooding the target with unwanted traffic or triggers a crash by sending it some information, which makes it challenging for the users to access their network. A higher version of DoS attacks is the DDoS attacks that have recently become quite severe in security companies. Many organizations have begun facing these issues. Such

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Cheema, Ammarah, Moeenuddin Tariq, Adnan Hafiz, Muhammad Murad Khan, Fahad Ahmad, and Muhammad Anwar. "Prevention Techniques against Distributed Denial of Service Attacks in Heterogeneous Networks: A Systematic Review." Security and Communication Networks 2022 (May 20, 2022): 1–15. http://dx.doi.org/10.1155/2022/8379532.

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The Distributed Denial of Service (DDoS) attack is one of the most critical issues in network security. These sorts of attacks pose a noteworthy danger to the accessibility of network services for their legitimate users by flooding the bandwidth or network service using various infected computer systems. The targeted servers are overwhelmed with malicious packets or connection requests, causing them to slow down or even crash the server operations which results in preventing genuine users from accessing the service. In this paper, we discussed the detailed classification of DDoS attacks and id

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Dr., Opinder Singh. "A NOVEL MECHANISM FOR DETECTING DENIAL OF SERVICE ATTACKS IN MOBILE ADHOC NETWORKS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 1 (2019): 50–56. https://doi.org/10.5281/zenodo.2537886.

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A mobile adhoc network (MANET) is a type of network, which contains number of mobile devices with wireless network interconnections. In MANET, each node can act as transmitter, router and data sink. MANET has dynamic topology which allows nodes to join and leave the adhoc network at any point of time. MANETs are more vulnerable than wired networks due to its characteristics like dynamic topology, distributed cooperation and open medium. Security issues in mobile adhoc networks are veiled by various techniques that were introduced in past decade. Due to decentralized nature of MANET, the securi

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Asaolu, Opeyemi Oreoluwa, Oluwasanmi Segun Adanigbo, Afeez Adekunle Soladoye, and Nnamdi Stephen Okomba. "Principal Component Analysis-Multilinear Perceptron-based model for Distributed Denial of Service Attack Mitigation." ABUAD Journal of Engineering Research and Development (AJERD) 8, no. 2 (2025): 14–24. https://doi.org/10.53982/ajerd.2025.0802.02-j.

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The increasing occurrence of Distributed Denial of Service (DDoS) attacks has caused significant disruptions in global network services, overwhelming targets by flooding them with requests from various sources. This ease of execution and gaining entry to distributed systems for rent has led to increasing financial losses. This paper addresses the growing challenge of IoT devices-targeted Distributed Denial of Service (DDoS) attacks within 4G networks. In this study, a PCA-MLP (Principal Component Analysis-Multi-Layer Perceptron) intrusion detection model combined with a packet-filtering firewa

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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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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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C. Remegio, Florlyn Mae, and Cristina E. Dumdumaya. "Distributed Denial of service attack mitigation and approaches: a literature review." International Journal Artificial Intelligent and Informatics 2, no. 2 (2022): 86–97. http://dx.doi.org/10.33292/ijarlit.v2i2.39.

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In Distributed Denial of Service (DDoS) attacks on a system for industrial monitoring are usually large numbers of packets. That flood thousands of infected hosts, waste network resources, and increase bandwidth. DDoS leads to a lack of productive leverage over the critical support of infrastructure. This paper includes a thorough review of several DDoS security techniques between 2016 and 2019 to execute such attacks. The question will answer in this review: 1)What kinds of cyber attacks are there? 2)The DDOS geography of cyberattacks? 3) What are the different approaches to cyber-attack miti

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Punarselvam, Dr E. "Blocking Distributed Denial of Service Flooding Attacks with Dynamic Path Detectors." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 1318–22. http://dx.doi.org/10.22214/ijraset.2020.6212.

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Luo, Hongbin, Zhe Chen, Jiawei Li, and Athanasios V. Vasilakos. "Preventing Distributed Denial-of-Service Flooding Attacks With Dynamic Path Identifiers." IEEE Transactions on Information Forensics and Security 12, no. 8 (2017): 1801–15. http://dx.doi.org/10.1109/tifs.2017.2688414.

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Li, Ming, Chi-Hung Chi, Weijia Jia, et al. "Decision Analysis of Statistically Detecting Distributed Denial-of-Service Flooding Attacks." International Journal of Information Technology & Decision Making 02, no. 03 (2003): 397–405. http://dx.doi.org/10.1142/s0219622003000720.

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There are two statistical decision making questions regarding statistically detecting sings of denial-of-service flooding attacks. One is how to represent the distributions of detection probability, false alarm probability and miss probability. The other is how to quantitatively express a decision region within which one may make a decision that has high detection probability, low false alarm probability and low miss probability. This paper gives the answers to the above questions. In addition, a case study is demonstrated.

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Chang, R. K. C. "Defending against flooding-based distributed denial-of-service attacks: a tutorial." IEEE Communications Magazine 40, no. 10 (2002): 42–51. http://dx.doi.org/10.1109/mcom.2002.1039856.

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Marcelo, I. P. Salas. "Attack Taxonomy Methodology Applied to Web Services." Latin-American Journal of Computing 11, no. 1 (2024): 66–79. https://doi.org/10.5281/zenodo.10402238.

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With the rapid evolution of attack techniques and attacker targets, companies and researchers question the applicability and effectiveness of security taxonomies. Although the attack taxonomies allow us to propose a classification scheme, they are easily rendered useless by the generation of new attacks. Web services, owing to their distributed and open nature, present novel security challenges. The purpose of this study is to apply a methodology for categorizing and updating attacks prior to the continuous creation and evolution of new attack schemes on web services. Also, in this research, w

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Alhammadi, Nafea A. Majeed, Mohamed Mabrouk, and Mounir Zrigui. "Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems." Fusion: Practice and Applications 11, no. 1 (2023): 37–56. http://dx.doi.org/10.54216/fpa.110103.

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Data storage, software services, infrastructure services, and platform services are only some of the benefits of today's widespread use of cloud computing. Since most cloud services run via the internet, they are vulnerable to a comprehensive range of attacks that might end it the disclosure of sensitive information. The distributed denial-of-service (DDoS) is amongst the attacks that pose an active threat to the cloud environment and disrupts the provided services for the legitimate participants. The main aim of this review paper is to present the recent trends on sophisticated flooding attac

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Godinho, António, José Rosado, Filipe Sá, Filipe Caldeira, and Filipe Cardoso. "Torrent Poisoning Protection with a Reverse Proxy Server." Electronics 12, no. 1 (2022): 165. http://dx.doi.org/10.3390/electronics12010165.

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A Distributed Denial-of-Service attack uses multiple sources operating in concert to attack a network or site. A typical DDoS flood attack on a website targets a web server with multiple valid requests, exhausting the server’s resources. The participants in this attack are usually compromised/infected computers controlled by the attackers. There are several variations of this kind of attack, and torrent index poisoning is one. A Distributed Denial-of-Service (DDoS) attack using torrent poisoning, more specifically using index poisoning, is one of the most effective and disruptive types of atta

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Qamar, Roheen, Baqar Ali Zardari, Zahid Hussain, Abbas Ali Ghoto, and Aijaz Ahmed Arain. "Detection of Distributed Denial of Service (DDoS) Cyber Attacks through Deep Learning Neural Network." Pakistan Journal of Engineering, Technology and Science 12, no. 2 (2024): 28–38. https://doi.org/10.22555/pjets.v12i2.1068.

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Distributed Denial of Service (DDoS) attacks pose a significant and escalating threat to online stability. By flooding a network with overwhelming traffic, these attacks can cripple website and application performance, making them inaccessible to legitimate users. Their insidious nature adds to the danger, as undetected attacks can cause considerable damage before being brought to light. Computer networks are not immune to other security vulnerabilities, facing challenges like intrusion attempts, traffic congestion, and unauthorized access. These concerns highlight the crucial role of robust n

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Hsu, Fu-Hau, Chia-Hao Lee, Chun-Yi Wang, Rui-Yi Hung, and YungYu Zhuang. "DDoS Flood and Destination Service Changing Sensor." Sensors 21, no. 6 (2021): 1980. http://dx.doi.org/10.3390/s21061980.

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In this paper, we aim to detect distributed denial of service (DDoS) attacks, and receive a notification of destination service, changing immediately, without the additional efforts of other modules. We designed a kernel-based mechanism to build a new Transmission Control Protocol/Internet Protocol (TCP/IP) connection smartly by the host while the users or clients not knowing the location of the next host. Moreover, we built a lightweight flooding attack detection mechanism in the user mode of an operating system. Given that reinstalling a modified operating system on each client is not realis

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Alasri, Abbas, and Rossilawati Sulaiman. "Protection of XML-Based Denail-of-Service and Httpflooding Attacks in Web Services Using the Middleware Tool." International Journal of Engineering & Technology 7, no. 4.7 (2018): 322. http://dx.doi.org/10.14419/ijet.v7i4.7.20570.

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A web service is defined as the method of communication between the web applications and the clients. Web services are very flexible and scalable as they are independent of both the hardware and software infrastructure. The lack of security protection offered by web services creates a gap which attackers can make use of. Web services are offered on the HyperText Transfer Protocol (HTTP) with Simple Object Access Protocol (SOAP) as the underlying infrastructure. Web services rely heavily on the Extended Mark-up Language (XML). Hence, web services are most vulnerable to attacks which use XML as

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S, Nithish Babu, Yogesh V, Mariswaran S, and Gowtham N. "Detection of DDOS Attack using Decision Tree Classifier in SDN Environment." Journal of Ubiquitous Computing and Communication Technologies 5, no. 2 (2023): 193–202. http://dx.doi.org/10.36548/jucct.2023.2.006.

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Software Defined Networking (SDN) is a dynamic architecture that employs a variety of applications for making networks more adaptable and centrally controlled. It is easy to attack the entire network in SDN because the control plane and data plane are separated. DDoS attack is major danger to SDN service providers because it can shut down the entire network and stop services to all customers at any time. One of the key flaws of most SDN architectures is lack of susceptibility to DDoS attacks with its types like TCP flooding, UDP flooding, SYN flooding, ICMP flooding and DHCP flooding for detec

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Kong, Bin, Kun Yang, Degang Sun, Meimei Li, and Zhixin Shi. "Distinguishing flooding distributed denial of service from flash crowds using four data mining approaches." Computer Science and Information Systems 14, no. 3 (2017): 839–56. http://dx.doi.org/10.2298/csis161230032k.

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Flooding Distributed Denial of Service (DDoS) attacks can cause significant damage to Internet. These attacks have many similarities to Flash Crowds (FCs) and are always difficult to distinguish. To solve this issue, this paper first divides existing methods into two categories to clarify existing researches. Moreover, after conducting an extensive analysis, a new feature set is concluded to profile DDoS and FC. Along with this feature set, this paper proposes a new method that employs Data Mining approaches to discriminate between DDoS attacks and FCs. Experiments are conducted to evaluate th

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Almaiah, Mohammed Amin, Rana Alrawashdeh, Tayseer Alkhdour, Romel Al-Ali, Gaith Rjoub, and Theyazan Aldahyani. "Detecting DDoS attacks using machine learning algorithms and feature selection methods." International Journal of Data and Network Science 8, no. 4 (2024): 2307–18. http://dx.doi.org/10.5267/j.ijdns.2024.6.001.

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A Distributed Denial of Service (DDoS) attack occurs when an attacker tries to disrupt a network, service or website by flooding huge numbers of packets on the internet traffic. Detecting DDoS attacks serves the goal of spotting and addressing them promptly to reduce their effects on the network, system or service being targeted. Detecting Distributed Denial of Service (DDoS) attacks is crucial, for people, companies and network managers. The detection of DDoS attacks has ranging uses in industries such as network security safeguarding websites, managing cloud services ensuring the security of

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Boyanov, Petar. "REVEALING ANOMALIES BY NETWORK PACKET FLOODING ON BUILT FTP AND OPENSSH SERVERS IN CONTROLLED LAB ENVIRONMENT." Journal Scientific and Applied Research 27, no. 1 (2024): 138–56. http://dx.doi.org/10.46687/jsar.v27i1.414.

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This scientific paper investigates the effects of network packet flooding on FTP (port 21) and SSH (port 22) protocols, aiming to reveal and document anomalies in server behavior under high-load conditions. By simulating packet flooding in a controlled lab environment, an analysis on vulnerabilities and anomalies unique to each protocol is conducted in order to the improve defensive capabilities. The results provide guidance on best practices to secure FTP and OpenSSH services against malicious traffic, such as Distributed Denial of Service (DDoS) attacks, supporting wider network security str

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Sadiq, Amin, Hassan Jamil Syed, Asad Ahmed Ansari, Ashraf Osman Ibrahim, Manar Alohaly, and Muna Elsadig. "Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF." Applied Sciences 13, no. 8 (2023): 4700. http://dx.doi.org/10.3390/app13084700.

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Kubernetes is an orchestration tool that runs and manages container-based workloads. It works as a collection of different virtual or physical servers that support multiple storage capacities, provide network functionalities, and keep all containerized applications active in a desired state. It also provides an increasing fleet of different facilities, known as microservices. However, Kubernetes’ scalability has led to a complex network structure with an increased attack vector. Attackers can launch a Denial of service (DoS) attack against servers/machines in Kubernetes by producing fake traff

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Zargar, Saman Taghavi, James Joshi, and David Tipper. "A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks." IEEE Communications Surveys & Tutorials 15, no. 4 (2013): 2046–69. http://dx.doi.org/10.1109/surv.2013.031413.00127.

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Liu, Liang, Weiqing Yu, Zhijun Wu, and Silin Peng. "XGBoost-Based Detection of DDoS Attacks in Named Data Networking." Future Internet 17, no. 5 (2025): 206. https://doi.org/10.3390/fi17050206.

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Named Data Networking (NDN) is highly susceptible to Distributed Denial of Service (DDoS) attacks, such as Interest Flooding Attack (IFA) and Cache Pollution Attack (CPA). These attacks exploit the inherent data retrieval and caching mechanisms of NDN, leading to severe disruptions in data availability and network efficiency, thereby undermining the overall performance and reliability of the system. In this paper, an attack detection method based on an improved XGBoost is proposed and applied to the hybrid attack pattern of IFA and CPA. Through experiments, the performance of the new attacks a

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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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Malliga, S., P. S. Nandhini, and S. V. Kogilavani. "A Comprehensive Review of Deep Learning Techniques for the Detection of (Distributed) Denial of Service Attacks." Information Technology and Control 51, no. 1 (2022): 180–215. http://dx.doi.org/10.5755/j01.itc.51.1.29595.

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(Distributed) Denial of Service (DoS/DDoS) attacks are performed to bring down a target by flooding it withuseless traffic. Because the DoS/DDoS attackers often change their styles and attack patterns, the nature andcharacteristics of these attacks need to be examined cautiously. Developing mechanisms to detect this menaceis a challenging task. Recently, deep learning has played a major role in the growth of intrusion detection solutions. In recent years, significant attempts have been made to construct deep learning models for counteringDoS/DDoS threats. In this review, we provide a taxonomy

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S. Abdulkarem, Huda, and Ammar D. Alethawy. "DDOS ATTACK DETECTION AND MITIGATION AT SDN ENVIROMENT." Iraqi Journal of Information & Communications Technology 4, no. 1 (2021): 1–9. http://dx.doi.org/10.31987/ijict.4.1.115.

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Abstract- Software-Defined Networking (SDN) is a promising sample that allows the programming behind the network’s operation with some abstraction level from the underlying networking devices .the insistence to detect and mitigate Distributed Denial of Service (DDoS) which introduced by network devices tries to discover network security weaknesses and the negative effects of some types of Distributed Denial of Service (DDoS) attacks. An SDN-based generic solution to mitigate DDoS attacks when and where they originate. Briefly, it compares at runtime the expected trend of normal traffic against

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Khan, Inam Ullah, Asrin Abdollahi, Ryan Alturki, et al. "Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks." Wireless Communications and Mobile Computing 2021 (September 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/1542657.

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The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by

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Ibrahim Gide, Aisha, and Abubakar Aminu Mu'azu. "NOVEL APPROACH FOR ADDRESSING IOT NETWORKS VULNERABILITIES IN DETECTION AND CLASSIFICATION OF DOS/DDOS ATTACKS." International Journal of Software Engineering and Computer Systems 10, no. 1 (2024): 50–59. http://dx.doi.org/10.15282/ijsecs.10.1.2024.5.0123.

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The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to ad

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Wang, Licheng, Yun Pan, Mianxiong Dong, Yafang Yu, and Kun Wang. "Economic Levers for Mitigating Interest Flooding Attack in Named Data Networking." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/4541975.

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As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we addre

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Maestre, Vidal Jorge, Orozco Ana Lucila Sandoval, and Villalba Luis Javier García. "Adaptive artificial immune networks for mitigating DoS flooding attacks." Swarm and Evolutionary Computation 38 (February 1, 2018): 94–108. https://doi.org/10.5281/zenodo.10613651.

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Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deploymen

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Duan, Qi, Ehab Al-Shaer, Samrat Chatterjee, Mahantesh Halappanavar, and Christopher Oehmen. "Proactive routing mutation against stealthy Distributed Denial of Service attacks: metrics, modeling, and analysis." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 15, no. 2 (2017): 219–30. http://dx.doi.org/10.1177/1548512917731002.

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Infrastructure Distributed Denial of Service (IDDoS) attacks continue to be one of the most devastating challenges facing cyber systems. The new generation of IDDoS attacks exploits the inherent weakness of cyber infrastructure, including the deterministic nature of routing, skewed distribution of flows, and Internet ossification to discover the network critical links and launch highly stealthy flooding attacks that are not observable at the victim’s end. In this paper, first, we propose a new metric to quantitatively measure the potential susceptibility of any arbitrary target server or domai

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Zaman, Ali, Salman A. Khan, Nazeeruddin Mohammad, Abdelhamied A. Ateya, Sadique Ahmad, and Mohammed A. ElAffendi. "Distributed Denial of Service Attack Detection in Software-Defined Networks Using Decision Tree Algorithms." Future Internet 17, no. 4 (2025): 136. https://doi.org/10.3390/fi17040136.

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A software-defined network (SDN) is a new architecture approach for constructing and maintaining networks with the main goal of making the network open and programmable. This allows the achievement of specific network behavior by updating and installing software, instead of making physical changes to the network. Thus, SDNs allow far more flexibility and maintainability compared to conventional device-dependent architectures. Unfortunately, like their predecessors, SDNs are prone to distributed denial of service (DDoS) attacks. These attack paralyze networks by flooding the controller with bog

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Jose, Ancy Sherin, Latha R. Nair, and Varghese Paul. "Towards Detecting Flooding DDOS Attacks Over Software Defined Networks Using Machine Learning Techniques." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 3837–65. http://dx.doi.org/10.47059/revistageintec.v11i4.2411.

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Distributed Denial of Service Attack (DDoS) has emerged as a major threat to cyber space. A DDoS attack aims at exhausting the resources of the victim causing financial and reputational damages to it. The availability of free software make launching of DDoS attacks easy. The difficulty in differentiating a DDoS traffic from a legitimate traffic burst such as a flash crowd makes DDoS difficult to be identified. A wide range of techniques have been used in conventional networks to detect and mitigate DDoS attacks. Though the advent of Software Defined Networking (SDN) makes a network easy to be

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P. Narode, Miss Priyanka, and Prof I. R. Shaikh. "Review on EM-CURE Algorithm for Detection DDOS Attack." International Journal Of Engineering And Computer Science 7, no. 01 (2018): 23386–489. http://dx.doi.org/10.18535/ijecs/v7i1.04.

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Distributed Denial of Service attack (DoS attack) is a cyber attack where the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the internet. Denial of service is typically accomplished by flooding the targeted machine or resource with superfluous requests in an attempt to overload systems and prevent some or all legitimate requests from being fulfilled. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol,

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Poongodi, M., and S. Bose. "A Firegroup Mechanism to Provide Intrusion Detection and Prevention System Against DDos Attack in Collaborative Clustered Networks." International Journal of Information Security and Privacy 8, no. 2 (2014): 1–18. http://dx.doi.org/10.4018/ijisp.2014040101.

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Distributed Denial of Service (DDOS) attacks are the major concern for security in the collaborative networks. Although non DDOS attacks are also make the network performances poor, the effect of DDOS attacks is severe. In DDOS attacks, flooding of the particular node as victim and jam it with massive traffic happens and the complete network performance is affected. In this paper, a novel Intrusion Detection and Prevention System is designed which detects the flooding DDOS attacks based on Firecol and prevents the attacks based on Dynamic Growing Self Organizing Tree (DGSOT) for collaborative

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Jun, Jae-Hyun, Min-Jun Kim, Jeong-Hyun Cho, Cheol-Woong Ahn, and Sung-Ho Kim. "Detection Method of Distributed Denial-of-Service Flooding Attacks Using Analysis of Flow Information." Journal of the Institute of Webcasting, Internet and Telecommunication 14, no. 1 (2014): 203–9. http://dx.doi.org/10.7236/jiibc.2014.14.1.203.

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Ponnuviji, N. P., and M. Vigilson Prem. "An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3765–69. http://dx.doi.org/10.1166/jctn.2020.9317.

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Cloud Computing has revolutionized the Information Technology by allowing the users to use variety number of resources in different applications in a less expensive manner. The resources are allocated to access by providing scalability flexible on-demand access in a virtual manner, reduced maintenance with less infrastructure cost. The majority of resources are handled and managed by the organizations over the internet by using different standards and formats of the networking protocols. Various research and statistics have proved that the available and existing technologies are prone to threa

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Rahmad, Gunawan, Ab Ghani Hadhrami, Khamis Nurulaqilla, Al Amien Januar, and Ismanto Edi. "Deep learning approach to DDoS attack with imbalanced data at the application layer." TELKOMNIKA 21, no. 05 (2023): 1060–67. https://doi.org/10.12928/telkomnika.v21i5.24857.

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A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access d

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Kaur, Jeewanjot, and Taranjit Singh Aulakh. "A Mitigation Technique for DoS Attack in Wireless Network Based Gradient Matrix and Firefly." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 1 (2018): 136. http://dx.doi.org/10.23956/ijarcsse.v8i1.546.

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In computing, a denial-of-service attack (DoS attack) is a cyber-attack where the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the Internet. In a distributed denial-of-service attack (DDoS attack), the incoming traffic flooding the victim originates from many different sources. This effectively makes it impossible to stop the attack simply by blocking a single source. In this research a generalized model for detection has been created by studying the existing models and algori

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Bahashwan, Abdullah Ahmed, Mohammed Anbar, Selvakumar Manickam, Taief Alaa Al-Amiedy, and Iznan H. Hasbullah. "A deep learning approach to detect DDoS flooding attacks on SDN controller." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1245. https://doi.org/10.11591/ijeecs.v38.i2.pp1245-1255.

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Software-defined networking (SDN), integrated into technologies like internet of things (IoT), cloud computing, and big data, is a key component of the fourth industrial revolution. However, its deployment introduces security challenges that can undermine its effectiveness. This highlights the urgent need for security-focused SDN solutions, driving advancements in SDN technology. The absence of inherent security countermeasures in the SDN controller makes it vulnerable to distributed denial of service (DDoS) attacks, which pose a significant and pervasive threat. These attacks specifically tar

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