Relevant bibliographies by topics / Application layer DDoS

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

Academic literature on the topic 'Application layer DDoS'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Application layer DDoS"

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

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Existing application-layer distributed denial of service (AL-DDoS) attack detection methods are mainly targeted at specific attacks and cannot effectively detect other types of AL-DDoS attacks. This study presents an application-layer protocol communication model for AL-DDoS attack detection, based on the explicit duration recurrent network (EDRN). The proposed method includes model training and AL-DDoS attack detection. In the AL-DDoS attack detection phase, the output of each observation sequence is updated in real time. The observation sequences are based on application-layer protocol keywords and time intervals between adjacent protocol keywords. Protocol keywords are extracted based on their identification using regular expressions. Experiments are conducted using datasets collected from a real campus network and the CICDDoS2019 dataset. The results of the experiments show that EDRN is superior to several popular recurrent neural networks in accuracy, F1, recall, and loss values. The proposed model achieves an accuracy of 0.996, F1 of 0.992, recall of 0.993, and loss of 0.041 in detecting HTTP DDoS attacks on the CICDDoS2019 dataset. The results further show that our model can effectively detect multiple types of AL-DDoS attacks. In a comparison test, the proposed method outperforms several state-of-the-art approaches.
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Ravichandran, S., and M. Umamaheswari. "Design and Development of Collaborative Detection and Taxonomy of DDoS Attacks Using ESVM." Asian Journal of Computer Science and Technology 6, no. 2 (2017): 27–32. http://dx.doi.org/10.51983/ajcst-2017.6.2.1783.

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Distributed Denial of Service (DDoS) assault is a ceaseless basic risk to the web. Application layer DDoS Attack is gotten from the lower layers. Application layer based DDoS assaults utilize honest to goodness HTTP asks for after foundation of TCP three-way handshaking and overpowers the casualty assets, for example, attachments, CPU, memory, circle, database transfer speed. Arrange layer based DDoS assaults sends the SYN, UDP and ICMP solicitations to the server and debilitates the transfer speed. An oddity discovery system is proposed in this paper to identify DDoS assaults utilizing Enhanced Support Vector Machine (ESVM). The Application layer DDoS Attack, for example, HTTP Flooding, DNS Spoofing and Network layer DDoS Attack, for example, Port Scanning, TCP Flooding, UDP Flooding, ICMP Flooding, Land Flooding. Session Flooding is taken as test tests for ESVM. The Normal client gets to conduct characteristics is taken as preparing tests for ESVM. The movement from the testing tests and preparing tests are Cross Validated and the better arrangement exactness is acquired. Application and Network layer DDoS assaults are arranged with order exactness of 99 % with ESVM.
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Beitollahi, Hakem, and Geert Deconinck. "Tackling Application-layer DDoS Attacks." Procedia Computer Science 10 (2012): 432–41. http://dx.doi.org/10.1016/j.procs.2012.06.056.

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Di, Xiao Qiang, Hua Min Yang, and Hui Qi. "Low-Rate Application-Layer DDoS Attacks Detection by Principal Component Analysis (PCA) through User Browsing Behavior." Applied Mechanics and Materials 397-400 (September 2013): 1945–48. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1945.

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Application-layer distributed denials of service (DDoS) attacks are becoming ever more challenging to internet service security, since firewall and intrusion detection system work on network layer while these attacks are launched on application layer. In contrast to prior work focusing on detection of high-rate DDoS attacks at static web sites, we propose a novel approach to detect low-rate application-layer DDoS attacks at dynamic web sites. A feature matrix is introduced to characterize user browsing behavior. Principal component analysis (PCA) is applied to profile the user browsing behavior pattern. Outliers from this pattern are used to identify anomaly users. Experiments are conducted to validate our approach. Experimental results show that our approach is accurate to detect low-rate application-layer DDoS attacks.
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Ranjan, S., R. Swaminathan, M. Uysal, A. Nucci, and E. Knightly. "DDoS-Shield: DDoS-Resilient Scheduling to Counter Application Layer Attacks." IEEE/ACM Transactions on Networking 17, no. 1 (2009): 26–39. http://dx.doi.org/10.1109/tnet.2008.926503.

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P., Ashvini, Anushree P., Bhagyashree N., Kiran S., and K. S. Kumavat. "DDOS Attack Prevention on Application Layer." International Journal of Computer Applications 127, no. 10 (2015): 22–25. http://dx.doi.org/10.5120/ijca2015906509.

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

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The increasing advancement of technologies and communication infrastructures has been posing threats to the internet services. One of the most powerful attack weapons for disrupting web-based services is the distributed denial of service (DDoS) attack. The sophisticated nature of attack tools being created and used for launching attacks on target systems makes it difficult to distinguish between normal and attack traffic. Consequently, there is a need to detect application layer DDoS attacks from network traffic efficiently. This paper proposes a detection system coined eXtreme gradient boosting (XGB-DDoS) using a tree-based ensemble model known as XGBoost to detect application layer DDoS attacks. The Canadian institute for cybersecurity intrusion detection systems (CIC IDS) 2017 dataset consisting of both benign and malicious attacks was used in training and testing of the proposed model. The performance results of the proposed model indicate that the accuracy rate, recall, precision rate, and F1-score of XGB-DDoS are 0.999, 0.997, 0.995, and 0.996, respectively, as against those of k-nearest neighbor (KNN), support vector machine (SVM), principal component analysis (PCA) hybridized with XGBoost, and KNN with SVM. So, the XGB-DDoS detection model did better than the models that were chosen. This shows that it is good at finding application layer DDoS attacks.
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Ni, Tongguang, Xiaoqing Gu, Hongyuan Wang, and Yu Li. "Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis." Journal of Control Science and Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/821315.

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Distributed denial of service (DDoS) attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS) nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI). By approximating the adaptive autoregressive (AAR) model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM) classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively.
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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 directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
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Dissertations / Theses on the topic "Application layer DDoS"

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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.<br>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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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.<br>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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Lee, Ming-fang, and 李明舫. "A Framework for Defending Application Layer DDoS Attacks Using a Back-Propagation Neural Network." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/46135312237380714209.

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碩士<br>大同大學<br>資訊工程學系(所)<br>94<br>The paper studies the application layer DDoS attack problem. The attackers use random requests from a predefined word pool to a web server as a search engine. The server will be slowed, if there is no defending mechanism. We evaluate two approaches to overcome the problem. Then, we discuss the performance with the false negative ratio, false positive ratio, and error ratio. We propose the artificial intelligence(AI)-based algorithm which is based on the back-propagation neural network. Then, we compare it with the statistical algorithm what we proposed previously. The former solves DDoS attacks with two phases. In the first phase, we train the neural network with the samples. Then, we use the trained neural network to separate all users in the second phase. In the statistical approach, three phases are employed to solve the DDoS attack problem. The first phase uses the repeated elements as the signature to decide the suspects from all users. The second phase is to identify an attacker among all suspects using their request logs. Then, the third phase uses the history of the identified attacker to classify all users into legitimate users and attackers. The two approaches can be built on either firewall or server to prevent the application layer DDoS attack with a limited pool. As our simulation results show the two approaches share approximately the same accuracy rate which is about 86%. However, their implementation and operational costs are somehow different. They are indicated as that classification times and needing phases are different.
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Book chapters on the topic "Application layer DDoS"

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Sreenivasarao, Sadhu. "Application Layer DDOS Attack Detection and Defense Methods." In Proceedings of Emerging Trends and Technologies on Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3097-2_1.

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Yu, Jie, Chengfang Fang, Liming Lu, and Zhoujun Li. "A Lightweight Mechanism to Mitigate Application Layer DDoS Attacks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10485-5_13.

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Mohamed, Mohamed Aly, and Nashwa Abdelbaki. "HTTP Application Layer DDoS Attack Mitigation Using Resources Monitor." In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64861-3_20.

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Brown, Eric, John Fisher, Aaron Hudon, Erick Colston, and Wei Lu. "Multiclassification Analysis of Volumetric, Protocol, and Application Layer DDoS Attacks." In Advanced Information Networking and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57942-4_39.

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Ye, Xi, Wushao Wen, Yiru Ye, and Qin Cen. "An OTP-Based Mechanism for Defending Application Layer DDoS Attacks." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23226-8_51.

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Miu, TungNgai, Chenxu Wang, Daniel Xiapu Luo, and Jinhe Wang. "Modeling User Browsing Activity for Application Layer DDoS Attack Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59608-2_42.

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Sharma, Ankita, and Anshu Bhasin. "Critical Investigation on Application Layer-DDoS Attacks: Taxonomy and Parameter Efficacy." In Proceedings of ICETIT 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30577-2_82.

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Zolotukhin, Mikhail, and Timo Hämäläinen. "Data Stream Clustering for Application-Layer DDoS Detection in Encrypted Traffic." In Cyber Security: Power and Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75307-2_8.

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Zhang, Mi, Wei Zhang, and Kuan Fan. "Application Layer DDoS Detection Model Based on Data Flow Aggregation and Evaluation." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31968-6_5.

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Chinnaiah, Balarengadurai. "Protection of DDoS Attacks at the Application Layer: HyperLogLog (HLL) Cardinality Estimation." In Cognitive Informatics and Soft Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1056-1_46.

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Conference papers on the topic "Application layer DDoS"

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Sharif, Dyari Mohammed. "High-Accurate Application-Layer DDoS Attack Detection Using Machine Learning." In 2023 International Conference on Engineering Applied and Nano Sciences (ICEANS). IEEE, 2023. http://dx.doi.org/10.1109/iceans58413.2023.10629670.

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Sharif, Dyari Mohammed. "Application-Layer DDoS Detection via Efficient Machine Learning and Feature Selection." In 2023 International Conference on Engineering Applied and Nano Sciences (ICEANS). IEEE, 2023. http://dx.doi.org/10.1109/iceans58413.2023.10630487.

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Suman, Padmavathy S, M. Madiajagan, B. J. Job Karuna Sagar, S. Selvi, and Charanjeet Singh. "Detecting Transport and Application Layer DDos Attacks in IoT Devices with Machine and Deep Learning." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10993831.

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Ye, Chengxu, Kesong Zheng, and Chuyu She. "Application layer ddos detection using clustering analysis." In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6526103.

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Singh, Barjinder, Krishan Kumar, and Abhinav Bhandari. "Simulation study of application layer DDoS attack." In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2015. http://dx.doi.org/10.1109/icgciot.2015.7380589.

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Feng, Yebo, Jun Li, and Thanh Nguyen. "Application-Layer DDoS Defense with Reinforcement Learning." In 2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS). IEEE, 2020. http://dx.doi.org/10.1109/iwqos49365.2020.9213026.

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She, Chuyu, Wushao Wen, Zaihua Lin, and Kesong Zheng. "Detection of Application-Layer DDoS by Clustering Algorithm." In 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/aiie-16.2016.25.

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Dantas, Yuri Gil, Vivek Nigam, and Iguatemi E. Fonseca. "A Selective Defense for Application Layer DDoS Attacks." In 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC). IEEE, 2014. http://dx.doi.org/10.1109/jisic.2014.21.

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Cirillo, Michele, Mario Di Mauro, Vincenzo Matta, and Marco Tambasco. "Application-Layer DDOS Attacks with Multiple Emulation Dictionaries." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413570.

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She, Chuyu, Wushao Wen, Kesong Zheng, and Yayun Lyu. "Application-Layer DDoS Detection by K-means Algorithm." In 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/iceeecs-16.2016.16.

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