Relevant bibliographies by topics / Distributed Denial of Service Attacks (DDoS)

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Distributed Denial of Service Attacks (DDoS)'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Distributed Denial of Service Attacks (DDoS)"

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Kotey, Seth, Eric Tchao, and James Gadze. "On Distributed Denial of Service Current Defense Schemes." Technologies 7, no. 1 (January 30, 2019): 19. http://dx.doi.org/10.3390/technologies7010019.

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Distributed denial of service (DDoS) attacks are a major threat to any network-based service provider. The ability of an attacker to harness the power of a lot of compromised devices to launch an attack makes it even more complex to handle. This complexity can increase even more when several attackers coordinate to launch an attack on one victim. Moreover, attackers these days do not need to be highly skilled to perpetrate an attack. Tools for orchestrating an attack can easily be found online and require little to no knowledge about attack scripts to initiate an attack. Studies have been done severally to develop defense mechanisms to detect and defend against DDoS attacks. As defense schemes are designed and developed, attackers are also on the move to evade these defense mechanisms and so there is a need for a continual study in developing defense mechanisms. This paper discusses the current DDoS defense mechanisms, their strengths and weaknesses.
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Sambangi, Swathi, and Lakshmeeswari Gondi. "A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression." Proceedings 63, no. 1 (December 25, 2020): 51. http://dx.doi.org/10.3390/proceedings2020063051.

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The problem of identifying Distributed Denial of Service (DDos) attacks is fundamentally a classification problem in machine learning. In relevance to Cloud Computing, the task of identification of DDoS attacks is a significantly challenging problem because of computational complexity that has to be addressed. Fundamentally, a Denial of Service (DoS) attack is an intentional attack attempted by attackers from single source which has an implicit intention of making an application unavailable to the target stakeholder. For this to be achieved, attackers usually stagger the network bandwidth, halting system resources, thus causing denial of access for legitimate users. Contrary to DoS attacks, in DDoS attacks, the attacker makes use of multiple sources to initiate an attack. DDoS attacks are most common at network, transportation, presentation and application layers of a seven-layer OSI model. In this paper, the research objective is to study the problem of DDoS attack detection in a Cloud environment by considering the most popular CICIDS 2017 benchmark dataset and applying multiple regression analysis for building a machine learning model to predict DDoS and Bot attacks through considering a Friday afternoon traffic logfile.
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K.M, Akhil, Rahul C.T, and Athira V.B. "Distributed Denial of Service (DDoS) Attacks and Defence Mechanism." International Journal of Computer Science and Mobile Computing 10, no. 3 (March 30, 2021): 83–88. http://dx.doi.org/10.47760/ijcsmc.2021.v10i03.010.

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Denial of Service (DoS) attacks is one of the major threats to Internet sites and one of the major security problems Internet faces today. The nature of threats caused by Distributed Denial of Service (DDoS) attacks on networks. With little or no warning, a DDoS attack could easily destroy its victim's communication and network resources in a short period of time. This paper outlines the problem of DDoS attacks and developing a classification of DDoS attacks and DDoS defense mechanisms. Important features of each attack and defense system category are described and advantages and disadvantages of each proposed scheme are outlined. The goal of the paper is to set a certain order of existence methods of attack and defense mechanisms, for the better understanding DDoS attacks can be achieved with more effective methods and means of self-defense can be developed.
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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 (August 1, 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. We soon will witness the 5G mobile revolution but there are reports that 5G networks are also falling prey to DDoS attacks and hence, the realization of DoS attack as a threat needs to be understood. The paper targets to assess the DDoS attack threat. It identifies the impact of attack and also reviews existing Indian laws.
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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 problem, assessing the performance and the lifetime of the network under the attack condition. To conduct our research, we utilized the Quality of Protection Modeling Language. With the proposed approach, it was possible to examine numerous network configurations, parameters, attack options, and scenarios. The results of the carefully performed multilevel analysis allowed us to identify a new kind of DDoS attack, the delayed distributed denial of service, by the authors, referred to as DDDoS attack. Multilevel approach to DDoS attack analysis confirmed that, examining endangered environments, it is significant to take into account many characteristics at once, just to not overlook any important aspect.
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Asad, Muhammad, Muhammad Asim, Talha Javed, Mirza O. Beg, Hasan Mujtaba, and Sohail Abbas. "DeepDetect: Detection of Distributed Denial of Service Attacks Using Deep Learning." Computer Journal 63, no. 7 (July 3, 2019): 983–94. http://dx.doi.org/10.1093/comjnl/bxz064.

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Abstract At the advent of advanced wireless technology and contemporary computing paradigms, Distributed Denial of Service (DDoS) attacks on Web-based services have not only increased exponentially in number, but also in the degree of sophistication; hence the need for detecting these attacks within the ocean of communication packets is extremely important. DDoS attacks were initially projected toward the network and transport layers. Over the years, attackers have shifted their offensive strategies toward the application layer. The application layer attacks are potentially more detrimental and stealthier because of the attack traffic and the benign traffic flows being indistinguishable. The distributed nature of these attacks is difficult to combat as they may affect tangible computing resources apart from network bandwidth consumption. In addition, smart devices connected to the Internet can be infected and used as botnets to launch DDoS attacks. In this paper, we propose a novel deep neural network-based detection mechanism that uses feed-forward back-propagation for accurately discovering multiple application layer DDoS attacks. The proposed neural network architecture can identify and use the most relevant high level features of packet flows with an accuracy of 98% on the state-of-the-art dataset containing various forms of DDoS attacks.
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Dhanapal, A., and P. Nithyanandam. "The Slow HTTP Distributed Denial of Service Attack Detection in Cloud." Scalable Computing: Practice and Experience 20, no. 2 (May 2, 2019): 285–98. http://dx.doi.org/10.12694/scpe.v20i2.1501.

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Cloud computing became popular due to nature as it provides the flexibility to add or remove the resources on-demand basis. This also reduces the cost of investments for the enterprises significantly. The adoption of cloud computing is very high for enterprises running their online applications. The availability of online services is critical for businesses like financial services, e-commerce applications, etc. Though cloud provides availability, still these applications are having potential threats of going down due to the slow HTTP Distributed Denial of Service (DDoS) attack in the cloud. The slow HTTP attacks intention is to consume all the available server resources and make it unavailable to the real users. The slow HTTP DDoS attack comes with different formats such as slow HTTP headers attacks, slow HTTP body attacks and slow HTTP read attacks. Detecting the slow HTTP DDoS attacks in the cloud is very crucial to safeguard online cloud applications. This is a very interesting and challenging topic in DDoS as it mimics the slow network. This paper proposed a novel method to detect slow HTTP DDoS attacks in the cloud. The solution is implemented using the OpenStack cloud platform. The experiments conducted exhibits the accurate results on detecting the attacks at the early stages. The slowHTTPTest open source tool is used in this experiment to originate slow HTTP DDoS attacks.
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Zeebaree, Subhi R. M., Karzan H. Sharif, and Roshna M. Mohammed Amin. "Application Layer Distributed Denial of Service Attacks Defense Techniques : A review." Academic Journal of Nawroz University 7, no. 4 (December 21, 2018): 113. http://dx.doi.org/10.25007/ajnu.v7n4a279.

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Currently distributed denial of service (DDoS) is the most sever attack that effect on the internet convenience. The main goal of these attacks is to prevent normal users from accessing the internet services such as web servers. However the more challenge and difficult types to detect is application layer DDoS attacks because of using legitimate client to create connection with victims. In this paper we give a review on application layer DDoS attacks defense or detection mechanisms. Furthermore, we summarize several experimental approaches on detection techniques of application layer DDoS attacks. The main goal of this paper is to get a clear view and detailed summary of the recent algorithms, methods and techniques presented to tackle these serious types of attacks.
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Alosaimi, Wael, Michal Zak, Khalid Al-Begain, Roobaea Alroobaea, and Mehedi Masud. "Mitigation of Distributed Denial of Service Attacks in the Cloud." Cybernetics and Information Technologies 17, no. 4 (November 27, 2017): 32–51. http://dx.doi.org/10.1515/cait-2017-0040.

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Abstract Cybersecurity attacks resulting in loss of availability of cloud services can have significantly higher impact than those in the traditional stand-alone enterprise setups. Therefore, availability attacks, such as Denial of Service attacks (DoS); Distributed DoS attacks (DDoS) and Economical Denial of Sustainability (EDoS) attacks receive increasingly more attention. This paper surveys existing DDoS attacks analyzing the principles, ways of launching and their variants. Then, current mitigation systems are critically discussed. Based on the identification of the weak points, the paper proposes a new mitigation system named as DDoS-Mitigation System (DDoS-MS) that attempts to overcome the identified gap. The proposed framework is evaluated, and an enhanced version of the proposed system called Enhanced DDoS-MS is presented. In the end, the paper presents some future directions of the proposed framework.
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Akanji, Oluwatobi Shadrach, Opeyemi Aderiike Abisoye, and Mohammed Awwal Iliyasu. "MITIGATING SLOW HYPERTEXT TRANSFER PROTOCOL DISTRIBUTED DENIAL OF SERVICE ATTACKS IN SOFTWARE DEFINED NETWORKS." Journal of Information and Communication Technology 20, Number 3 (June 11, 2021): 277–304. http://dx.doi.org/10.32890/jict2021.20.3.1.

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Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. This paper provides the scholarly community with an approach to boosting service availability in web servers under slow Hypertext Transfer Protocol (HTTP) DDoS attacks through attack detection using Genetic Algorithm and Support Vector Machine which facilitates attack mitigation in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was used to select the Netflow features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained showed that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and a false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented. This study contributes to the ongoing research in detecting and mitigating slow HTTP DDoS attacks with emphasis on the use of machine learning classification and meta-heuristic algorithms.
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Dissertations / Theses on the topic "Distributed Denial of Service Attacks (DDoS)"

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Ramanauskaitė, Simona. "Modelling and Research of Distributed Denial of Service Attacks." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120723_105031-70003.

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In the dissertation the Denial of Service (DoS) attacks and their models are investigated. DoS attack is a type of cyber attacks when an attacker tries to deny a service in the network machine. There are many types of DoS attacks, and therefore the main object of the dissertation is specified as distributed denial of service (DDoS) attacks. DDoS uses multiple agents at the same time to exhaust certain resources of network machine and make it unavailable. The importance of DDoS attacks can be explained on the basis of the following facts: nowadays there are no countermeasures which can ensure full resistance to DDoS; DoS effect can be created even by legitimate users of the systems; internet services become more popular therefore the denial of such a service or diminishing of its quality can cause undesired impact on the other systems or their users. The main objective of this dissertation is creation of model for the estimation of the composite DDoS attack success. This model would allow estimating of network machine resistance to different type and power DDoS attacks. The dissertation consists of eight parts including Introduction, 5 chapters, Conclusions and References. In the introduction, the investigated problem, importance of the thesis and the object of research are defined and the purpose and tasks of the thesis, scientific novelty are described together with the practical significance of results and defended statements. At the end of introduction, author’s... [to full text]
Disertacijoje nagrinėjamos internetinės paslaugos sutrikdymo (angl. DoS – Denial of Service) atakos ir jų modeliavimo priemonės. Tai kibernetinių atakų tipas, kurių metu siekiama tam tikro tinkle veikiančio mazgo teikiamas paslaugas padaryti neprieinamas jų teisėtiems klientams. DoS atakos gali turėti daug skirtingų tipų, todėl šio darbo pagrindinis tyrimų objektas yra srautinė internetinės paslaugos sutrikdymo (angl. DDoS – Distributed Denial of Service) ataka, kuri paslaugos sutrikdymo siekia naudodama bent kelis atakuojančiuosius kompiuterius vienu metu ir kuri siekia išnaudoti visus pasirinkto tipo resursus tą paslaugą teikiančiame mazge. DDoS atakos aktualios dėl šių priežasčių: šiuo metu nėra apsaugos priemonių, leidžiančių patikimai ir užtikrintai apsisaugoti nuo jų keliamos grėsmės; jas gali sukelti net ir teisėti vartotojai, netinkamai elgdamiesi ar esant netinkamai paruoštai sistemai; internete teikiamos paslaugos vis dažniau naudojamos kasdieniniame gyvenime ir jų blokavimas ar kokybės suprastėjimas gali neigiamai paveikti kitų sistemų ar jų vartotojų darbą. Pagrindinis šios disertacijos tikslas – sukurti jungtinį DDoS atakos sėkmės tikimybės vertinimo modelį. Šis modelis leistų įvertinti kompiuterinės technikos sugebėjimą atlaikyti skirtingo tipo ir galingumo DDoS atakas, todėl galėtų būti taikomas prevencijai bei paslaugų tiekėjų kokybės vertinimui. Disertaciją sudaro įvadas, penki skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų... [toliau žr. visą tekstą]
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Namuduri, Sarita. "Distributed Denial of Service Attacks (DDoS)- Consequences and Future." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8055.

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Denial of Service and the Distributed Denial of Service Attacks have recently emerged as one of the most newsworthy, if not the greatest, weaknesses of the Internet. This paper attempt to explain how they work, why they are hard to combat today, and what will need to happen if they are to be brought under control. It is divided into eight sections. The first is an overview of the current situation and also brief explanatory of the rest of the chapters being covered. The second is a detailed description of exactly how this attack works, and why it is hard to cope with today; of necessity it includes a description of how the Internet works today. The third section is totally about the different attacks in recent years and how they affected the people or the bigorganizations. The fourth section describes the short-term prospects, the tools which are used to rectify these attacks. The fifth is problems being faced with an explanatory of the percentage of attack in recent years and comparing the problems. The sixth is what can be done today to help alleviate this problem. The seventh section describes the legal actions and also legal actions that can be followed against the attack by the victim; and the eighth section describes the long-term picture, what will change to bring this class of problem under control, if not eliminate it entirely. And finally there are some appendices: a bibliography, giving references to original research work and announcements; a brief article on securing servers; and acknowledgments for the many people who helped make this paper possible.

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Eklund, Martin, and Patrik Ståhlberg. "Distributed denial of service attacks : Protection, Mitigation, and Economic Consequences." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170924.

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Distributed Denial of Service attacks is a problem that constantly threatens companies that rely on the internet for major parts of their business. A successful DDoS attack that manages to penetrate a company’s network can lead to devastating damages in the form of lost income, reduced productivity, increase in costs, and damage to the company’s image and reputation. The different DDoS attacks are many and of different character and often Offer different parts of the network, which makes it very difficult to defend against. It is also very clear that DDoS attacks are increasing in both numbers and size every year. From our experiments we have proven that anyone with little knowledge and limited resources can perform DDoS attacks that will make a website unavailable. This fact should cause companies that base their business on the internet, aware that they are likely to someday be subject to a DDoS attack. From our research we have found a variety of different DDoS solutions on the market that promise to offer protection. Many of which claim to protect against all different types of DDoS attacks. In practice it is impossible to find something that guarantees 100% safety. According to earlier research in the field, there are many different ways of protecting a network against DDoS attacks, e.g. via Software Defined Networking, Hop-Count Filtering, or Kill-bots. Our own tests show that a virtual firewall can offer protection against DDoS attacks on a low scale, but that such a solution has a number of weaknesses. If the firewall does protect the website, the attacker could instead shift to attacking the firewall itself. Our research also shows that the most common motives behind DDoS attacks are criminal purposes. Criminals use DDoS attacks to earn money by offering directed DDoS attacks against websites or by trying to blackmail companies into paying a fee for not being attacked. We have also seen that the economic consequence of DDoS attacks are devastating if not handled with a sufficiently fast response. After investigating the e-commerce company CDON.com we learned that they could potentially lose roughly 36 410 SEK per minute when a DDoS attack is underway against them. In today’s business climate it is important for companies to be able to rely on the internet for their activity and for customers to have easy access to the company’s products and services. However, companies’ websites are being attacked and thus these companies need an explicit plan of how to mitigate such attacks.
Distributed Denial of Service (DDoS) attacker är ett problem som ständigt hotar företag, som förlitar sig till internet för centrala delar av sin verksamhet. En DDoS-attack som lyckas penetrerar ett företags nätverk kan medföra förödande skador i form av förlorade intäkter, minskad produktivitet, ökade kostnader samt skada på företagets rykte/varumärke. DDoS-attackerna är många och av olika karaktär, som attackerar olika delar av ett företags nätverk, vilket leder till att det är svårt att effektivt skydda sig mot DDoS-attacker. Det står också klart att DDoS-attacker ökar både till antalet och storleksmässigt för varje år som går. Utifrån våra egna experiment har vi kunnat bevisa att vem som helst med små medel och begränsade kunskaper kan utföra en DDoS-attack som sänker en webbsida. Ett faktum som gör att alla företag vars verksamhet är baserad på internet bör räkna med att de någon gång bli utsatta för en DDoS-attack. Utifrån våra undersökningar kan vi se att det finns en uppsjö av olika DDoS-skydd på marknaden, skydd som hanterar några problem som DDoS-attacker medför, men det finns inga kompletta skydd som kan garantera 100 % säkerhet. Utifrån tidigare forskning på området framgår det att det finns många olika sätt att skydda sig mot DDoS-attacker, t.ex. genom Software Defined Networks, Hop-Count Filtering eller Kill-bots. Våra egna tester visar på att en virtuell brandvägg kan vara ett sätt att skydda sig mot DDoS-attacker, men testerna visar också att en sådan lösning inte heller är säker då man kan förstöra åtkomsten till webbsidan genom att överbelasta brandväggen.<p> Undersökningen visar också att ett av de vanligaste motiven bakom DDoS-attacker är kriminella ändamål. Kriminella som använder DDoS-attacker för att tjäna pengar genom att erbjuda riktade DDoS-attacker mot websidor eller genom försök att utpressa till betalning med DDoS-attacker som ett hot. Vi har kommit fram till att de ekonomiska konsekvenserna av DDoS-attacker kan vara ödestigna för företag om det inte hanteras i tid. Genom våra egna beräkningar har vi visat att e-handelsföretaget CDON.com riskerar att förlora ca 36 415,90 kr per minut som en DDoS-attack pågår mot företaget. Anledningen till av vi valt att ägnad denna uppsats åt DDoS-problemet, är den skrämmande ökningen av DDoS-attacker som man kan se sker årligen. Attackerna blir flera, de ökar storleksmässigt och de blir allt mer sofistikerade. Attackerna utförs också tillsynes omotiverat i vissa fall, men också välplanerade attacker utförs för att skada företag ekonomiskt. I dagens företagsklimat är det viktigt att företaget har möjlighet att använda sig av internet för att driva verksamheten och göra det enkelt för kunder att ta del av företagets produkter/tjänster. Att företags webbsidor blir utslagen på grund av en DDoS-attacker är idag en verklighet, och en tydlig plan för att hur man ska hantera en sådan incident bör finns på plats inom företag.
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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 prevents forged DDoS packets from reaching the victim. DDoS Detectors are distributed across one or more networks in order to mitigate the strength of a DDoS attack. The knowledge sharing component uses encrypted messages to inform other DDoS Detectors when it detects a DDoS attack. This mechanism increases the efficacy of the detection mechanism between the DDoS Detectors. This approach has been evaluated and tested against other related approaches in terms of Sensitivity, Specificity, False Positive Rate (FPR), Precision, and Detection Accuracy. A major contribution of the research is that this approach achieves a 98% DDoS detection and mitigation accuracy, which is 5% higher than the best result of previous related approaches.
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Khanal, Sandarva, and Ciara Lynton. "Packet Simulation of Distributed Denial of Service (DDoS) Attack and Recovery." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579511.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV
Distributed Denial of Service (DDoS) attacks have been gaining popularity in recent years. Most research developed to defend against DDoS attacks have focused on analytical studies. However, because of the inherent nature of a DDoS attack and the scale of a network involved in the attack, analytical simulations are not always the best way to study DDoS attacks. Moreover, because DDoS attacks are considered illicit, performing real attacks to study their defense mechanisms is not an alternative. For this reason, using packet/network simulators, such as OPNET Modeler, is the best option for research purposes. Detection of an ongoing DDoS attack, as well as simulation of a defense mechanism against the attack, is beyond the scope of this paper. However, this paper includes design recommendations to simulate an effective defense strategy to mitigate DDoS attacks. Finally, this paper introduces network links failure during simulation in an attempt to demonstrate how the network recovers during and following an attack.
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Mekhitarian, Araxi, and Amir Rabiee. "A simulation study of an application layer DDoS detection mechanism." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191145.

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Over the last couple of years the rise of application layer Distributed Denial of Service (DDoS) attacks has significantly increased. Because of this, many issues have been raised on how organizations and companies can protect themselves from intrusions and damages against their systems and services. The consequences from these attacks are many, ranging from revenue losses for companies to stolen personal data. As the technologies are evolving, application layer DDoS attacks are becoming more effective and there is not a concrete solution that entirely protects against them. This thesis focuses on the available defense mechanisms and presents a general overview of different types of application layer DDoS attacks and how they are constructed. Moreover this report provides a simulation based on one of the defense mechanisms mentioned, named CALD. The simulation tested two different application layer DDoS attacks and showed that CALD can detect and differentiate between the two attacks. This report can be used as a general information source for application layer DDoS attacks, how to detect them and how to defend against them. Furthermore the simulation can be used as a basis on how well a relatively small-scaled implementation of CALD can detect DDoS attacks on the application layer.
Under de senaste åren har ökningen av Distributed Denial of Service (DDoS) attacker på applikationslagret ökat markant. På grund av detta har många frågor uppkommit om hur organisationer och företag kan skydda sig mot intrång och skador mot sina system och tjänster. Konsekvenserna av dessa attacker är många, allt från intäktsförluster för företag till stulen personlig data. Eftersom tekniken utvecklas, har DDoS attacker på applikationslagret blivit mer effektiva och det finns inte en konkret lösning för att hindra dem. Denna rapport fokuserar på de tillgängliga försvarsmekanismer och presenterar en allmän översikt över olika typer av DDoS-attacker på applikationslagret och hur de är uppbyggda. Dessutom bidrar den här rapporten med en redovisning av en simulering baserad på en av de försvarsmekanismer som nämns i rapporten, CALD. Simuleringen testade två olika attacker på applikationslagret och visar att CALD kan upptäcka och skilja mellan de två attackerna. Denna rapport kan användas som en allmän informationskälla för DDoSattacker på applikationslagret och hur man försvarar sig mot och upptäcker dessa. Vidare kan simuleringen användas som utgångspunkt på hur väl en relativt småskalig implementering av CALD kan upptäcka DDoS-attacker på applikationslagret.
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Nilsson, Sebastian. "The Current State of DDoS Defense." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3933.

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A DDoS attack is an attempt to bring down a machine connected to the Internet. This is done by having multiple computers repeatedly sending requests to tie up a server making it unable to answer legitimate requests. DDoS attacks are currently one of the biggest security threats on the internet according to security experts. We used a qualitative interview with experts in IT security to gather data to our research. We found that most companies are lacking both in knowledge and in their protection against DDoS attacks. The best way to minimize this threat would be to build a system with redundancy, do a risk analysis and revise security policies. Most of the technologies reviewed were found ineffective because of the massive amount of data amplification attacks can generate. Ingress filtering showed promising results in preventing DDoS attacks by blocking packages with spoofed IP addresses thus preventing amplification attacks.
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Jawad, Dina, and Felicia Rosell. "Speak-up as a Resource Based Defence against Application Layer Distributed Denial-of-Service Attacks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166597.

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Under de senaste åren har antalet DDoS-attacker i Internets applikationsskikt ökat. Detta problem behöver adresseras. Den här rapporten presenterar ett antal existerande metoder för att upptäcka och skydda mot DDoS-attacker i applikationsskiktet. En metod för detta ändamål är att hitta avvikelser av olika typer hos de attackerande klienterna, för att urskilja mellan attackerande och vanliga klienter. Detta är ett brett utforskatförsvarsområde med många positiva resultat, men dessa metoder har ett antal brister, som att de kan resultera i både falska positiva och negativa resultat. En metod som ännu inte har undersökts tillräckligt är resurs-baserat försvar. Det är en metod med mycket potential, eftersom den tydligare kan skilja på goda och onda klienter under en DDoS-attack. Speak-up är en sådan metod och är huvudfokus i denna rapport. För- och nackdelarna med Speak-up har undersökts och resultaten visar på att Speak-up har potential till att bli ett kraftfullt verktyg mot DDoS-attacker. Speak-up har dock sina begränsningar och är därför inte det bästa alternativet under vissa typer av dessa DDoS-attacker.
In recent years, the internet has endured an increase in application layer DDoS attacks. It is a growing problem that needs to be addressed. This paper presents a number of existing detection and protection methods that are used to mitigate application layer DDoS attacks. Anomaly detection is a widely explored area for defence and there have been many findings that show positive results in mitigating attacks. However, anomaly detection possesses a number of flaws, such as causing false positives and negatives. Another method that has yet to become thoroughly examined is resource based defence. This defence method has great potential as it addresses clear differences between legitimate users and attackers during a DDoS attack. One such defence method is called Speak-up and is the center of this paper. The advantages and limitations of Speak-up have been explored and the findings suggest that Speak-up has the potential to become a strong tool in defending against DDoS attacks. However, Speak-up has its limitations and may not be the best alternative during certain types of application layer DDoS attacks.
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Yu, Xuan Hamilton John A. "A defense system on DDOS attacks in mobile ad hoc networks." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2006%20Fall/Dissertations/YU_XUAN_49.pdf.

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Chan, Yik-Kwan Eric, and 陳奕鈞. "Investigation of a router-based approach to defense against Distributed Denial-of-Service (DDoS) attack." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30173309.

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Books on the topic "Distributed Denial of Service Attacks (DDoS)"

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Brooks, Richard R., and İlker Özçelik. Distributed Denial of Service Attacks. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125.

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Gupta, Brij B., and Amrita Dahiya. Distributed Denial of Service (DDoS) Attacks. CRC Press, 2021. http://dx.doi.org/10.1201/9781003107354.

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Distributed Denial of Service Attacks. Taylor & Francis Group, 2020.

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Singh, Rajeev, and Mangey Ram, eds. Distributed Denial of Service Attacks. De Gruyter, 2021. http://dx.doi.org/10.1515/9783110619751.

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Distributed Denial of Service Attack and Defense. Springer, 2013.

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Brooks, Richard, and Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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Brooks, Richard, and Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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Brooks, Richard, and Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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Brooks, Richard, and Ilker Ozcelik. Distributed Denial of Service Attacks: Real-World Detection and Mitigation. Taylor & Francis Group, 2020.

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Using Network Management Systems to Detect Distributed Denial of Service Attacks. Storming Media, 2001.

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Book chapters on the topic "Distributed Denial of Service Attacks (DDoS)"

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Brooks, Richard R., and İlker Özçelik. "DDoS Lab." In Distributed Denial of Service Attacks, 279–317. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-14.

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Brooks, Richard R., and İlker Özçelik. "What is DDoS?" In Distributed Denial of Service Attacks, 5–42. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-2.

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Brooks, Richard R., and İlker Özçelik. "DDoS Research: Testing." In Distributed Denial of Service Attacks, 93–106. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-6.

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Brooks, Richard R., and İlker Özçelik. "Deceiving DDoS Detection." In Distributed Denial of Service Attacks, 139–49. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-9.

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Brooks, Richard R., and İlker Özçelik. "DDoS Research: Traffic." In Distributed Denial of Service Attacks, 75–91. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-5.

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Brooks, Richard R., and İlker Özçelik. "DDoS Research: Evaluation." In Distributed Denial of Service Attacks, 107–13. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-7.

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Demirci, Mehmet. "Security and DDoS in SDN: Opportunities and Challenges." In Distributed Denial of Service Attacks, 223–38. Boca Raton : CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315213125-11.

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Yu, Shui. "An Overview of DDoS Attacks." In Distributed Denial of Service Attack and Defense, 1–14. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9491-1_1.

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Yu, Shui. "Malicious Networks for DDoS Attacks." In Distributed Denial of Service Attack and Defense, 15–29. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9491-1_2.

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Yu, Shui. "DDoS Attack Detection." In Distributed Denial of Service Attack and Defense, 31–53. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9491-1_3.

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Conference papers on the topic "Distributed Denial of Service Attacks (DDoS)"

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Trostle, Jonathan. "Protecting Against Distributed Denial of Service (DDoS) Attacks Using Distributed Filtering." In 2006 Securecomm and Workshops. IEEE, 2006. http://dx.doi.org/10.1109/seccomw.2006.359548.

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Adeshina, Qozeem Adeniyi, and Baidya Nath Saha. "Using Machine Learning to Predict Distributed Denial-of-Service (DDoS) Attack." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.21.

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The IT space is growing in all aspects ranging from bandwidth, storage, processing speed, machine learning and data analysis. This growth has consequently led to more cyber threat and attacks which now requires innovative and predictive security approach that uses cutting-edge technologies in order to fight the menace. The patterns of the cyber threats will be observed so that proper analysis from different sets of data will be used to develop a model that will depend on the available data. Distributed Denial of Service is one of the most common threats and attacks that is ravaging computing devices on the internet. This research talks about the approaches and the development of machine learning classifiers to detect DDoS attacks before it eventually happen. The model is built with seven different selection techniques each using ten machine learning classifiers. The model learns to understand the normal network traffic so that it can detect an ICMP, TCP and UDP DDoS traffic when they arrive. The goal is to build a data-driven, intelligent and decision-making machine learning algorithm model that will use classifiers to categorize normal and DDoS traffic using KDD-99 dataset. Results have shown that some classifiers have very good predictions obtained within a very short time.
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Jiang, Hong, Shuqiao Chen, Hongchao Hu, and Mingming Zhang. "Superpoint-based detection against distributed denial of service (DDoS) flooding attacks." In 2015 IEEE International Workshop on Local and Metropolitan Area Networks (LANMAN). IEEE, 2015. http://dx.doi.org/10.1109/lanman.2015.7114724.

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Alzahrani, Sabah, and Liang Hong. "Detection of Distributed Denial of Service (DDoS) Attacks Using Artificial Intelligence on Cloud." In 2018 IEEE World Congress on Services (SERVICES). IEEE, 2018. http://dx.doi.org/10.1109/services.2018.00031.

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Bhosale, Karuna S., Maria Nenova, and Georgi Iliev. "The distributed denial of service attacks (DDoS) prevention mechanisms on application layer." In 2017 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS). IEEE, 2017. http://dx.doi.org/10.1109/telsks.2017.8246247.

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"Keynote: Detection of and Defense Against Distributed Denial-of-Service (DDoS) Attacks." In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2012. http://dx.doi.org/10.1109/trustcom.2012.341.

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Khadke, Ashwini, Mangala Madankar, and Manish Motghare. "Review on mitigation of distributed Denial of Service (DDoS) attacks in cloud computing." In 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2016. http://dx.doi.org/10.1109/isco.2016.7726917.

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Wang, Zhongqing, and Yue Zhang. "DDoS Event Forecasting using Twitter Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/580.

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Distributed Denial of Service (DDoS) attacks have been significant threats to the Internet. Traditional research in cyber security focuses on detecting emerging DDoS attacks by tracing network package flow. A characteristic of DDoS defense is that rescue time is limited since the launch of attack. More resilient detection and defence models are typically more costly. We aim at predicting the likelihood of DDoS attacks by monitoring relevant text streams in social media, so that the level of defense can be adjusted dynamically for maximizing cost-effect. To our knowledge, this is a novel and challenge research question for DDoS rescue. Because the input of this task is a text stream rather than a document, information should be collected both on the textual content of individual posts. We propose a fine-grained hierarchical stream model to capture semantic information over infinitely long history, and reveal burstiness and trends. Empirical evaluation shows that social text streams are indeed informative for DDoS forecasting, and our proposed hierarchical model is more effective compared to strong baseline text stream models and discrete bag-of-words models.
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Chan, E. Y. K., H. W. Chan, K. M. Chan, V. P. S. Chan, S. T. Chanson, M. M. H. Cheung, C. F. Chong, et al. "IDR: an intrusion detection router for defending against distributed denial-of-service (DDoS) attacks." In 7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/ispan.2004.1300541.

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Sharafaldin, Iman, Arash Habibi Lashkari, Saqib Hakak, and Ali A. Ghorbani. "Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy." In 2019 International Carnahan Conference on Security Technology (ICCST). IEEE, 2019. http://dx.doi.org/10.1109/ccst.2019.8888419.

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Reports on the topic "Distributed Denial of Service Attacks (DDoS)"

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Blackert, W. J., R. L. Hom, A. K. Castner, R. M. Jokerst, and D. M. Gregg. Distributed Denial of Service-Defense Attack Tradeoff Analysis (DDOS-DATA). Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada429339.

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Mirkovic, Jelena. Benchmarks for Evaluation of Distributed Denial of Service (DDOS). Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada477282.

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Mankins, David, Rajesh Krishnan, Ceilyn Boyd, John Zao, and Michael Frentz. Mitigating Distributed Denial of Service Attacks with Dynamic Resource Pricing. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada406438.

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Nir, Y., and V. Smyslov. Protecting Internet Key Exchange Protocol Version 2 (IKEv2) Implementations from Distributed Denial-of-Service Attacks. RFC Editor, November 2016. http://dx.doi.org/10.17487/rfc8019.

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