Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy

Random Forests

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A taxonomy of DDoS attack and DDoS defense mechanisms

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Toward developing a systematic approach to generate benchmark datasets for intrusion detection

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Characterization of Tor Traffic using Time based Features

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Towards a Reliable Intrusion Detection Benchmark Dataset

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Discriminating DDoS Attacks from Flash Crowds Using Flow Correlation Coefficient

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A covariance analysis model for DDoS attack detection

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A survey and taxonomy of DoS attacks in cloud computing

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Contrast Duality of Adversarial Learningin Network Intrusion: A Review

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DDoSAE: a novel deep autoencoder framework for low-rate DDoS attack detection

Kukkala, V. R., Bhargavi, K., Ramadevi, Y., Sravan, P., Gudimilla, P., & Sireesha, N. (2025) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-025-07213-z · Cited by 1

AlignAD-VAE: A Variational Autoencoder with MMD-Based Dataset Alignment for Network Anomaly Detection

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PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

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Memory-Efficient Multi-Island Jaya Feature Selection for Large-Scale DDoS Attack Detection

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DDoS attack detection using deep transfer learning and FFT-based data transformation

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Machine Learning Based Hybrid DDoS Attack Prediction

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Application of SMOTE for Improving the Training of Intrusion Detection Models with Imbalanced Classes

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A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks

Ganeshan, S., & Ramasamy, R. K. (2026) · Future Internet · doi:10.3390/fi18020109 · Cited by 1

Enhancing Intrusion Detection via Interpretable Inter-Flow Spatio-Temporal Graphs and Intra-Flow Features

Zhao, X., He, M., Yang, L., Zhao, J., & Wang, X. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3664905 · Cited by 1

Multi-Layer Traffic Analysis Framework for DDoS Attacks in Software-Defined IoT Networks

Balaji, K., & Balachandra, M. (2026) · Future Internet · doi:10.3390/fi18030164 · Cited by 1

One Model to Catch Them All: Autoencoder-Based Anomaly Detection in Next-Generation Networks

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A Real-Time IoT Vulnerability Detection Framework with Hybrid Discovery and CVE Correlation

Minhass, S., Chauhan, R., & Kaur, H. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.16423 · Cited by 1

Meta-heuristic intrusion detection: grey wolf optimization (GWO) for attack classification in network traffic

Jha, R. S., & Saini, H. (2026) · OPSEARCH · doi:10.1007/s12597-026-01164-4 · Cited by 1

A comprehensive review of DDoS attack prevention, detection, and mitigation in IoT and SDN-IoT networks

Sutradhar, S., Chowdhury, K., Deb, S., Sarkar, J. L., Kumar, C., & Sahu, A. K. (2026) · Discover Internet of Things · doi:10.1007/s43926-026-00341-7 · Cited by 1

Distributed Denial-of-Service Attack Vector Identification in IoT Networks Using Wavelet Transform and Hybrid Deep Learning

Behdadnia, T., Thoelen, K., García-Alfaro, J., & Deconinck, G. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3692254 · Cited by 1

A Domain-Informed Hierarchical Federated Learning Framework for DDoS Detection in WSN for Critical Infrastructure

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Proactive DoS and DDoS Attack Detection Through Behavior-Based Threat Intelligence

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A Four-Stage Bayesian Incremental Learning Framework for Intrusion Detection in Real-World IoT Environment

Li, J., Wang, Y., Zhao, A., Yan, H., Gu, Z., & Jia, Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3710860 · Cited by 1

One-Dimensional Convolutional Neural Network for Detection and Mitigation of DDoS Attacks in SDN

Alshra’a, A. S., & Seitz, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98978-1_2 · Cited by 1

Time Series Similarity for Detecting DDoS Flooding Attack

Hussain, F. A., & Nashat, D. (2022) · Assiut University Journal of Multidisciplinary Scientific Research · doi:10.21608/aunj.2022.129373.1004 · Cited by 1

Cyber Threats Prediction Model using Advanced Data Science Approaches

Ajmal, M. A., Imran, M., Raza, M. A., & Raza, A. (2022) · Journal of Computing & Biomedical Informatics · doi:10.56979/302/2022/52 · Cited by 1

A Novel Analysis of Network Traffic in Distributed Denial of Service (DDoS) Attack to Improve Accuracy Using Extreme Learning Machine Algorithm over Regression Algorithm

Dharanidharan, E., & Parthipan, V. (2022) · Advances in parallel computing · doi:10.3233/apc220036 · Cited by 1

Implementing a Deep Learning Algorithm for Detection of Denial of Service Attacks

Garcia, J. F. C., & Blandón, G. E. T. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-19951-6_4 · Cited by 1

Hiatus: Unsupervised Generative Approach for Detection of DoS and DDoS Attacks

Muneeswaran, S., Sachidananda, V., Patil, R. S., Peng, H., Liu, M., & Gurusamy, M. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-25538-0_28 · Cited by 1

Threat Mitigation Model with Low False Alarm Rate Based on Hybrid Deep Belief Network

Bassene, A., & Gueye, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-29419-8_18 · Cited by 1

Power Communication System DDoS Attack Detection Based on ARIMA-CNN Model

Pengbo, Y., Qiuping, M., & Tianyang, W. (2023) · Panda Forum on Power and Energy (PandaFPE) · doi:10.1109/pandafpe57779.2023.10140789 · Cited by 1

A Novel Approach of DDOS Attack Classification with Optimizing the Ensemble Classifier Using A Hybrid Firefly and Particle Swarm Optimization (HFPSO)

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Boosting Algorithms-Based Intrusion Detection System: A Performance Comparison Perspective

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Data-driven Secure Authentication for Smart Grid IoT Networks

Singhal, S., & Tripathi, S. (2023) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct57959.2023.10234815 · Cited by 1

Anomaly Detection and Resolution on the Edge: Solutions and Future Directions

Forough, J., Bhuyan, M. H., & Elmroth, E. (2023) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose58276.2023.00034 · Cited by 1

RepuFilter: Prevention of Untrusted Packet Spread Based on Trust Evaluation in Wireless Networks

Bao, H., Zhang, X., Wang, G., Zhang, M., Wang, Y., & Zhao, Y. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278691 · Cited by 1

ABORDANDO EL DESEQUILIBRIO DE DATOS EN CLASIFICACIÓN DE ATAQUES DE DENEGACIÓN DE SERVICIO DISTRIBUIDO (DDOS)

Acosta-Tejada, D. E., Sánchez-Galán, J. E., & Torres-Batista, N. (2023) · Congreso Nacional de Ciencia y Tecnología – APANAC · doi:10.33412/apanac.2023.3922 · Cited by 1

Security Implications on Container Clouds

G, M. K. T., Karthik, D. U., Samarth, S., Rajath, R., & Niyati, V. (2023) · International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS) · doi:10.1109/icrais59684.2023.10367062 · Cited by 1

Implementing a Machine Learning based Hybrid Model to Counter Attacks in Mobile Edge Computing

Chaki, E. S., & Velempini, M. (2023) · doi:10.59200/icarti.2023.008 · Cited by 1

Protecting NextG Military Networks with Convolutional Neural Networks

Paolini, E., Perotto, G., Valcarenghi, L., Civerchia, F., Maggiani, L., & Andriolli, N. (2023) · IEEE International Workshop on Technologies for Defense and Security (TechDefense) · doi:10.1109/techdefense59795.2023.10380876 · Cited by 1

Intrusion Detection in IOT Networks using Machine Learning Techniques

Hadi, A. A., & Abdullah, K. M. (2024) · Journal of Electronics Computer Networking and Applied Mathematics · doi:10.55529/jecnam.42.1.18 · Cited by 1

Evaluating the Performance of Long Short-Term Memory for Web Attack Detection

Momu, S. A., Siddiqui, R. R., Hoque, M. S., & Kawsur, M. M. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441062 · Cited by 1

Unveiling the Effectiveness of CNN-based Models for Multiclass DDoS Attack Detection and Classification: A Comparative Analysis

Dhirta, T., & Sharma, A. (2023) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai59313.2023.10452429 · Cited by 1

Analyzing detection algorithms for cybersecurity in financial institutions

Dhashanamoorthi, B. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.11.2.0478 · Cited by 1

An ensemble-based approach for effective distributed denial of service attack detection in software defined networking

Ahmed, M. M., & Abdulkader, H. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp2019-2026 · Cited by 1

ENHANCED NETWORK SECURITY: A DATA MINING APPROACH TO INTRUSION DETECTION

Hwung, T. C., & Yusof, Y. (2024) · Journal of Digital System Development · doi:10.32890/jdsd2024.2.1.9 · Cited by 1

Fortifying Network Security: Pioneering Hybrid Machine Learning for BotNet Attack Detection

Varun, M. V., Gurudeep, R., Yogitha, S., Raju, Y., & Patil, R. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.61765 · Cited by 1

Classification of Distributed Denial of Service Attacks Using Machine Learning Methods

İnce, U., & Karaduman, G. (2024) · NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University · doi:10.46572/naturengs.1450965 · Cited by 1

Generating a Distributed Network Attack Dataset Based on Weather Station IoT Cyber Physical Systems

Johnson, D., & Roy, K. (2024) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd62167.2024.10645281 · Cited by 1

NetSentry: Scalable Volumetric DDoS Detection with Programmable Switches

Pan, J., He, K., Zhang, L., Liu, Z., Zhang, X., & Cui, Y. (2024) · International Workshop on Quality of Service · doi:10.1109/iwqos61813.2024.10682912 · Cited by 1

Detecção de Ataques de Negação de Serviço Distribuídos com Algoritmos de Aprendizado de Máquina

Silva, R. R., Henriques, F. D. R., Moraes, I. M., & Mascarenhas, D. M. (2024) · Simpósio Brasileiro de Cibersegurança (SBSeg) · doi:10.5753/sbseg.2024.241684 · Cited by 1

A DDoS Identification Method for Unbalanced Data CVWGG

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Network Analysis of Multisource Packet Capture Files Using Machine Learning

Khanum, F., Lakshmi, P. S., & Reddy, H. V. (2024) · International Conference on Circuits, Control, Communication and Computing (I4C) · doi:10.1109/i4c62240.2024.10748490 · Cited by 1

Resource Consumption Analysis of Distributed Machine Learning for the Security of Future Networks

Hoque, M. M., Ahmad, I., & Tahir, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.085 · Cited by 1

Leveraging In-band Network Telemetry for Automated DDoS Detection in Production Programmable Networks: The AmLight Use Case

Sahin, H., Bezerra, J., Brito, I., Frez, R., Chergarova, V., López, L. F., & Ibarra, J. E. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00113 · Cited by 1

ENNigma: A framework for Private Neural Networks

Barbosa, P., Amorim, I., Maia, E., & Praça, I. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107719 · Cited by 1

DoS-Dam: a Hierarchical Method for Identifying and Mitigating DDoS Attacks in High-Speed Network Traffic

Hao, Y., Chang, L., Wei, G., Qi, H., Yang, G., Cuil, J., Li, G., & Song, Y. (2024) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp61940.2024.10858563 · Cited by 1

Identification of DDoS Attacks Through SHAP-Based Feature Analysis and Transformer Models: A Multivariate Time Series Data Approach

Hernandez, D. V., Chen, M., & Lai, Y. (2024) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon61640.2024.10902992 · Cited by 1

Deep Learning Model with Optimization Strategies for DDoS Attack Detection in Cloud Computing

Radhika., P., & Kamalakkannan, S. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10914724 · Cited by 1

HFL-AD: A Hierarchical Federated Learning Framework for Solving Data Contamination in DDoS Detection

Huang, H., Gui, J., Hong, J., & Hua, C. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00328 · Cited by 1

SPR: Malicious traffic detection model for CTCS-3 in railways

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Shannon entropy based DDoS attacks detection using combination of machine learning based feature importance techniques

Ali, B. H., Sulaiman, N., Al-Haddad, S. A. R., Atan, R. B., Hassan, S. L. M., Al-Khafaji, A. J. A., Sjarif, N. N. A., & Al-Hashimi, A. S. (2025) · AIP conference proceedings · doi:10.1063/5.0257765 · Cited by 1

MAD-HOT: Mixed Rate DDoS Attack Detection in IEEE 802.15 4e/TSCH Networks Using Hoeffding Optimized Trees

Bhale, P., Maurya, D., Sodhi, V., Farooqui, T., Singh, H., & Maurya, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94263-1_23 · Cited by 1

Application of electric influence for efficient crushing of materials in condi-tions of metallurgical processing

Saveliev, S. H., Плотников, В. В., & Babaievska, O. V. (2020) · Jornal of Kryvyi Rih National University · doi:10.31721/2306-5451-2020-1-50-112-118 · Cited by 1

Neighborhood Component Analysis and Artificial Neural Network for DDoS Attack Detection over IoT Networks

Abdullah, R. M., Abdullah, S. M., & Abdullah, S. M. (2021) · International Engineering Conference on Sustainable Technology and Development (IEC) · doi:10.1109/iec52205.2021.9476123 · Cited by 1

AnubisFlow: A Feature Extractor for Distributed Denial of Service Attack Classification

Barzilay, A., Martinelli, C. L., Nogueira, M., Batista, D. M., & Hirata, R. (2021) · International Conference on the Network of the Future (NOF) · doi:10.1109/nof52522.2021.9609918 · Cited by 1

Cyberattacks Predictions Workflow using Machine Learning

Perez, C. E. B., Serrano, J. E., & Martínez-Santos, J. C. (2021) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant53170.2021.9690527 · Cited by 1

Machine learning methods in predicting portmapper DDoS attack

Cai, T. (2022) · International Conference on Electronics and Communication, Network and Computer Technology (ECNCT) · doi:10.1117/12.2628593 · Cited by 1

Research on elephant flow detection method based on information entropy and improved random forest

Hu, X., Miao, X., & Yuan, M. (2022) · International Conference on Algorithms, Microchips, and Network Applications (AMNA) · doi:10.1117/12.2636382 · Cited by 1

An Intelligent Model for DDoS Attack Detection and Flash Event Management

Tinubu, O. C., Sodiya, A. S., Ojesanmi, O. A., Adeleke, E. O., & Timehin, A. A. (2022) · International Journal of Distributed Artificial Intelligence · doi:10.4018/ijdai.301212 · Cited by 1

Estimates of the Complexity of Detecting Types of DDOS Attacks

Ignatev, N., & Navruzov, E. R. (2022) · International Journal of Computing · doi:10.47839/ijc.21.4.2779 · Cited by 3

An efficient IDS using FIS to detect DDoS in IoT networks

Hoang, T.-M., Tran, N.-H., Thai, V.-L., Nguyen, D.-L., & Nguyen, N.-H. (2022) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics56915.2022.10013480 · Cited by 1

Combination of Models for Denial-Of-Service Classification over Different Networks

de Melo, L. H., Bertoli, G. D. C., Saotome, O., Domingues, M. F., & Pereira, L. A. (2022) · Latin-American Symposium on Dependable Computing · doi:10.1145/3569902.3570176 · Cited by 1

On Implementing a Simulation Environment for a Cooperative Multi-agent Learning Approach to Mitigate DRDoS Attacks

Kawazoe, T., & Fukuta, N. (2023) · Studies in computational intelligence · doi:10.1007/978-981-99-0561-4_2 · Cited by 1

Evaluating the Effectiveness of Machine Learning Algorithms in Detecting Distributed Denial of Service Attacks in Mobile Edge Computing

Chaki, E. S., Mapunya, S. S., Chaki, E. S., & Velempini, M. (2022) · International Conference on Intelligent and Innovative Computing Applications · doi:10.59200/iconic.2022.029 · Cited by 1

Isconna: Streaming Anomaly Detection with Frequency and PatternsPreprint

Liu, R., Bhatia, S., & Hooi, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01632 · Cited by 1

A Novel Data Encryption Method Inspired by Adversarial AttacksPreprint

Fernando, P., & Wei‐Kocsis, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06634 · Cited by 1

Dataset: Large-scale Urban IoT Activity Data for DDoS Attack EmulationPreprint

Hekmati, A., Grippo, E., & Krishnamachari, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01842 · Cited by 1

Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity

Alrowaily, M. (2020) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1

A Detailed Review on Security Issues in Layered Architectures and Distributed Denial Service of Attacks Over IoT Environment

Ganesarathinam, R., Singaravelu, M., & Pooja, K. (2022) · doi:10.1002/9781119836636.ch5 · Cited by 1

Distributed Network Traffic Classification Based on Heterogeneous Federated EnsemblePreprint

Deng, Q., Liao, N., & Deng, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4820781 · Cited by 1

Dynamic Honeypot Conversion for Enhanced IoT SecurityPreprint

Commey, D., Nkoom, M., Hounsinou, S. G., & Crosby, G. V. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5079213 · Cited by 1

DDoS Attacks Detection and Classification based on Deep Learning ModelPreprint

Ferhi, W., Moussaoui, D., Hadjila, M., Bouidaine, A. B., & Bensenouci, D. (2023) · Research Square · doi:10.21203/rs.3.rs-3013492/v1 · Cited by 1

Explainable Artificial Intelligence for Industrial Cybersecurity: A Review of Methods, Operational Integration, and Research Challenges

Mohamed, A. S., Fritz, C., Saber, A. M., Ma, Y., Kaur, R., Al-Darwbi, M., Friedrich, D., & Kundur, D. (2026) · arXiv · doi:10.48550/arxiv.2609.00171

Neural network method for early detection of DDoS-attack

Saitgareev, N. N. S. R. B. (2020) · Jornal of Kryvyi Rih National University · doi:10.31721/2306-5451-2020-1-50-106-112

Identificação Antecipada de Botnets por Aprendizagem de Máquina

de Neira, A. B., Medeiro, A., & Nogueira, M. (2020) · doi:10.5753/sbrc.2020.12333

Two-Stage Classification Technique for Malicious DNS Identification

Amaizu, G. C., Agron, D. J. S., Lee, J., & Kim, D.-S. (2021) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic51459.2021.9415225

Análise de Características do Tráfego de Rede para Detecção de Ataques DDoS em Ambientes IoT

Costa, W. L., Portela, A. L. C., & Gomes, R. L. (2021) · Anais do Computer on the Beach · doi:10.14210/cotb.v12.p217-224

Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks

Freas, C. B., Shah, D., & Harrison, R. W. (2021) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops50388.2021.9473824

Tensor based machine learning frameworks for intrusion detection in the physical and network layers of cyber-physical systems

Maranhão, J. P. A. (2021) · doi:10.26512/2021.04.t.41611

DDoS Attack Detection on Internet o Things using Unsupervised Algorithms

Tekleselase, H. (2021) · International Journal of Fuzzy Logic Systems · doi:10.5121/ijfls.2021.11401

A Data-centric Analysis to Evaluate Suitable Machine-Learning-based Network-Attack Classification Schemes

Hương, T. T., Bac, T. P., Thang, B. D., Long, D. M., Quang, L. A., Dan, N. M., & Việt, N. H. (2021) · doi:10.22937/ijcsns.2021.21.6.23

A Covid-19 kiberbiztonsági kihívásai az első hullám idején

Katona, G. (2021) · Hadmérnök · doi:10.32567/hm.2021.3.12

Um Sistema Inteligente para Detecção de DDoS em Ambientes Inteligentes Baseado em Computação em Nuvem e em Névoa

Costa, W. L., Portela, A. L. C., & Gomes, R. L. (2021) · Anais do Workshop de Computação Urbana (CoUrb) · doi:10.5753/courb.2021.17117

Detecção de Ataques DDoS em redes IoT usando Redes Neurais e Seleção de Características

Portela, A. L. C., Costa, W. L., & Gomes, R. L. (2021) · Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC) · doi:10.5753/sbrc_estendido.2021.17175

Reprodutibilidade e Extensibilidade de Datasets de Rede: um estudo da replicação de traces de pacotes

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