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

Random Forests

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Scikit-learn: Machine Learning in Python

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Induction of decision trees

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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

Yu, S., Zhou, W., Jia, W., Guo, S., Xiang, Y., & Tang, F. (2011) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2011.262 · Cited by 222

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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Efficient DDoS Detection and Mitigation in Cloud Data Centers Using eBPF and XDP

Chen, Z., Kong, H., Ding, S., Lv, Q., & Wei, G. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00258 · Cited by 4

Solving Data Contamination in DDoS Detection: A Method Based on Hierarchical Federated Learning

Gui, J., Ji, R., Huang, H., Hong, J., & Hua, C. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3587185 · Cited by 4

Encapcap: Transforming Network Traces to Virtual Networks

Spiekermann, D., & Keller, J. (2021) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft51509.2021.9492602 · Cited by 4

Evaluation of one-class algorithms for anomaly detection in home networks

Melo, P. H., de Resende, A. A. M., Miani, R. S., & Rosa, P. F. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00108 · Cited by 4

A fast instance reduction algorithm for intrusion detection scenarios

Herrera-Semenets, V., Hernández-León, R., & van den Berg, J. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107963 · Cited by 4

DDoS Intrusion Detection Model for IoT Networks using Backpropagation Neural Network

Almotiri, J. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130682 · Cited by 4

Feasibility Evaluation of Compact Flow Features for Real-time DDoS Attacks Classifications

Sidiq, M. F., Iryani, N., Basuki, A. I., Haris, A. I., & Ferianda, R. A. (2022) · IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) · doi:10.1109/comnetsat56033.2022.9994323 · Cited by 4

Construct an Efficient DDoS Attack Detection System Based on RF-C4.5-GridSearchCV

Gharkan, D. K., & Abdulrahman, A. A. (2022) · Iraqi International Conference on Communication and Information Technologies (IICCIT) · doi:10.1109/iiccit55816.2022.10010645 · Cited by 4

A Lightweight Flow-based DDoS Detection Approach using Dual Convolutional Kernels

Liang, Q., Liu, C., Zhong, Y., & Ren, X. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055469 · Cited by 4

Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection

D’hooge, L., Verkerken, M., Wauters, T., Turck, F. D., & Volckaert, B. (2023) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp3020008 · Cited by 4

Experimenting Ensemble Machine Learning for DDoS Classification: Timely Detection of DDoS Using Large Scale Dataset

Amaad, H., & Mughal, H. (2023) · International Conference on Advancements in Computational Sciences (ICACS) · doi:10.1109/icacs55311.2023.10089656 · Cited by 4

Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset

Alashhab, Z. R., Anbar, M., Rihan, S. D. A., Alabsi, B. A., & Ateeq, K. (2023) · Applied Sciences · doi:10.3390/app13169086 · Cited by 4

DDoS attack detection method based on One-Hot coding and improved ResNet18

Lu, H., Ying, B., Che, X., Jin, Z., Wang, M., & Wu, S. (2023) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit59027.2023.10210725 · Cited by 4

An IDS for DDoS Attacks in SDN using VGG-Based CNN Architecture

Muhammad, M., Alshra’a, A. S., & German, R. (2023) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom58365.2023.10271630 · Cited by 4

DDoS Attack Detection Based on Information Entropy Feature Extraction in Software Defined Networks

Ji, W., Yang, Y., Zhang, Y., Wang, Y., Tian, M., & Qiu, Y. (2023) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana60121.2023.00082 · Cited by 4

DDoS Attack Detection Using Ensemble Machine Learning Approach

Arya, A., Kumar, A., & Ahmad, S. S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306750 · Cited by 4

An Autonomous System for Predicting DDoS Attacks on Local Area Networks and the Internet

Brito, D., de Neira, A. B., Borges, L. F., & Nogueira, M. (2023) · IEEE Latin-American Conference on Communications · doi:10.1109/latincom59467.2023.10361878 · Cited by 4

Deep Learning-Powered Multiclass Classification of DDoS Attacks on 6G-Connected IoT Devices

Kumari, P., & Jain, A. K. (2023) · International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) · doi:10.1109/mosicom59118.2023.10458778 · Cited by 4

Interpretable Deep Learning for DDoS Defense: A SHAP-based Approach in Cloud Computing

Ouhssini, M., Afdel, K., Akouhar, M., Agherrabi, E., & Abarda, A. (2024) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc62074.2024.10616654 · Cited by 4

qIDS: Sistema de Detecção de Ataques baseado em Aprendizado de Máquina Quântico Híbrido

Abreu, D. M. D., Rothenberg, C. R. E., & Abelém, A. (2024) · doi:10.5753/sbrc.2024.1353 · Cited by 4

Taxonomy of DDoS attacks and their defense mechanisms in IoT

Pandey, N., & Mishra, P. K. (2021) · Journal of scientific research · doi:10.37398/jsr.2021.650523 · Cited by 4

A Deep Learning Approach to Create DNS Amplification Attacks

Mathews, J., Chatterjee, P., Banik, S. M., & Nance, C. (2022) · doi:10.1145/3535782.3535838 · Cited by 3

Encrypted Network Traffic Analysis

Cherukuri, A. K., Thaseen, I. S., Li, G., & Liu, X. (2024) · SpringerBriefs in computer science · doi:10.1007/978-3-031-62909-9_2 · Cited by 3

Eye-Net: A Low-Complexity Distributed Denial of Service Attack-Detection System Based on Multilayer Perceptron

Khantouchi, R., Gasmi, I., & Ferrag, M. A. (2024) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan13040045 · Cited by 3

GAMPALv2: An Anomaly Detection Mechanism for Internet Traffic by Predicting Flow Size Range from Time Features

WAKUI, T., Teraoka, F., & KONDO, T. (2025) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2024ntp0002 · Cited by 3

E-DDoS: An Evaluation System for DDoS Attack Detection

Chi, K., Xie, X., Hu, Y., Zhao, D., Xie, Y., Zhang, L., & Cui, Y. (2024) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp61940.2024.10858578 · Cited by 4

CHC-DDoS: A DDoS Attacks Detection Scheme Using Host-Connection Graph Representation and GCN

Saidane, A., Kamel, A. E., & Youssef, H. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87784-1_29 · Cited by 3

Deep Learning-Based DDoS Detection in SDN Networks with Explainable AI Transparency

Raihan, M. Z., & Islam, M. S. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022440 · Cited by 3

CT-SSSA: Malicious traffic augmentation based on classifier transGAN and spatial-channel synergistic self-attention

Cai, S., Zhao, X., Chen, J., Zhao, Y., Chen, J., & Chen, L. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114285 · Cited by 3

FELACS: Federated learning with adaptive client selection for IoT DDoS attack detection

Anley, M. B., Coscia, P., Genovese, A., & Piuri, V. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104642 · Cited by 3

Enhancing 5G Network Security: A Deep Learning Framework for Real-Time DDoS Detection and Explainable Threat Analysis

Albashayreh, A., Al-Sharaeh, S. H., Tashtoush, Y. M., & Zahariev, P. (2025) · IEEE Access · doi:10.1109/access.2025.3613386 · Cited by 3

CDBi‐LSTM: A Hybrid Deep Learning Model With Attention‐Based Fusion for Efficient DDoS Detection in IoT Environments

Quansah, A. J. K., Tchao, E. T., Keelson, E., Agbemenu, A. S., Nunoo‐Mensah, H., & Akowuah, B. Y. (2025) · IET Communications · doi:10.1049/cmu2.70094 · Cited by 3

Machine Learning Based Detection of Denial of Wallet Attacks in Serverless Computing

Ghaleh, A. M., & Kashi, S. S. (2025) · SN Computer Science · doi:10.1007/s42979-025-04545-6 · Cited by 3

A hybrid machine learning approach for detecting DDoS attacks in software-defined networks

Mahar, I. A., Wu, L., Aziz, K., Chakrabarti, P., Ahmed, N., Ladan, M., & Javed, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-35458-w · Cited by 3

SentinelSphere: An AI-driven cybersecurity platform integrating real-time threat detection with security awareness education

Tantaroudas, N. D., Karachalios, I., & McCracken, A. J. (2026) · Open Research Europe · doi:10.12688/openreseurope.22957.1 · Cited by 3

Leveraging programmable data plane for network intrusion detection: A survey

Fida, M.-R., & Ahmed, A. H. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112202 · Cited by 3

A Survey of Application Layer Distributed Denial of Service Detection: Approaches, Models, and Datasets

Tsion, F., Beyene, A. M., & Gemechu, A. Y. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11528-3 · Cited by 3

Employing multi-format log analysis to detect Distributed Denial-of-Service (DDoS) attacks in Software-Defined Networks (SDN)

Bhattacharya, S., Nayek, A. K., & Sen, M. (2026) · Discover Networks · doi:10.1007/s44354-026-00020-7 · Cited by 3

Resilient Cybersecurity and DDoS Attack Classification for AMI Smart Meter Networks in Smart Grid Environments

Raj, R. D. A., Krishna, A., Rajasekar, B., Pallakonda, A., Yanamala, R. M. R., Pouresmaeil, E., & Aghaei, J. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2026.3687494 · Cited by 3

An Information Entropy and Ensemble Learning Approach for DR-DOS Detection within Aviation Networks

Whitworth, H., Al–Rubaye, S., Tsourdos, A., Jiggins, J., Silverthorn, N., & Khan, I. (2022) · Integrated Communications, Navigation and Surveillance Conference (ICNS) · doi:10.1109/icns54818.2022.9771492 · Cited by 3

A Review on Machine Learning Techniques for DDoS Attack Detection in IoT

Gupta, A., Tyagi, O., Uniyal, V., Singhal, S., & Jha, V. (2022) · International Conference on Artificial Intelligence and Speech Technology (AIST) · doi:10.1109/aist55798.2022.10064846 · Cited by 3

Application‐Layer DDoS Attack Detection Using Explicit Duration Recurrent Network‐Based Application‐Layer Protocol Communication Models

Xie, B., Wang, Y., Wen, G., & Xu, X. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/2632678 · Cited by 7

Poster: Continual Network Learning

Cicco, N. D., Sadi, A. A., Grasselli, C., Melis, A., Antichi, G., & Tornatore, M. (2023) · Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication · doi:10.1145/3603269.3610855 · Cited by 3

An Autoencoder-based Efficient Scheme for DDoS Detection

Shrivastav, U., Kumar, M., & Kumar, S. (2023) · International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) · doi:10.1109/ic2e357697.2023.10262806 · Cited by 3

Comparison of Data Cleansing Methods for Network DDoS Attacks Mitigation

Jamal, A., Attar, A. E., Chbib, F., & Khatoun, R. (2023) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit58514.2023.10284093 · Cited by 3

DDoS Attacks Detection in IoV using ML-based Models with an Enhanced Feature Selection Technique

Albishi, O. A., & Abdullah, M. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150282 · Cited by 3

ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

Xiang, Y., Li, D., Meng, X., Dong, C., & Qin, G. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.047143 · Cited by 3

SDT-IDS: Spatial Data Transformation for Elevating Intrusion Detection Efficiency in IoT Networks

Tsai, H.-P., Nguyen, V.-L., Chiewnawintawat, N., & Hwang, R. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615938 · Cited by 3

AGAE: Unsupervised Anomaly Detection for Encrypted Malicious Traffic

Wang, H., Wang, Y., Gu, Z., & Jia, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7241-4_28 · Cited by 3

Deep Learning for Network Intrusion Detection in Virtual Networks

Spiekermann, D., Eggendorfer, T., & Keller, J. (2024) · Electronics · doi:10.3390/electronics13183617 · Cited by 3

Unsupervised Security Threats Identification for Heterogeneous Events

Jang, Y. I., Choi, S., Min, B.-G., & Choi, Y. (2024) · Electronics · doi:10.3390/electronics13204061 · Cited by 3

Perbandingan Support Vector Machine, Random Forest Classifier, dan K-Nearest Neighbour dalam Pendeteksian Anomali pada Jaringan DDos

Haeruddin, H., Erick, E., & Aripradono, H. W. (2025) · JTIM Jurnal Teknologi Informasi dan Multimedia · doi:10.35746/jtim.v7i1.628 · Cited by 3

Resource-Aware Adaptive Federated Learning for Enhanced DDoS Detection in Vehicular Ad Hoc Networks

Nakayiza, H. L., Ahakonye, L. A. C., Kim, D., & Lee, J. (2024) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc62082.2024.10826874 · Cited by 3

Enhancing Transparency in DDoS Detection on SDN Using Explainable ML Models

Raihan, Z., & Islam, M. S. (2024) · doi:10.1109/icict64387.2024.10839660 · Cited by 3

Overcoming Dataset Imbalances and Computational Challenges in IoT Intrusion Detection: A SMOTE-Enhanced Transformer-Based Model

Venkatasubramanian, S., Kumar, S. K. B. P., & Babu, B. P. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968199 · Cited by 3

A comprehensive overview of machine learning for intrusion detection in software-defined networking

Yzzogh, H., & Benaboud, H. (2025) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-025-00604-6 · Cited by 3

Deep Learning Techniques for Network Intrusion Detection: A Comparative Survey

AbdelHalim, A. P., Abo‐alian, A., & Badr, N. L. (2025) · International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences · doi:10.21608/ijicis.2025.395907.1404 · Cited by 3

AI-Enabled Intrusion Detection of Cyber Security Flood Attacks on Smart meters

Garcia, M. G., & Kumar, S. (2025) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot65859.2025.11105336 · Cited by 3

Random Forest Detector and Classifier of Multiple IoT-based DDoS Attacks

Ivanova, V., Tashev, T., & Draganov, I. R. (2022) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2022.19.4 · Cited by 3

HollywooDDoS: Detecting Volumetric Attacks in Moving Images of Network Traffic

Kopmann, S., Heseding, H., & Zitterbart, M. (2022) · Conference on Local Computer Networks · doi:10.1109/lcn53696.2022.9843465 · Cited by 4

Distributed Denial of Service Attack Detection and Prevention Model for IoT based Computing Environment using Ensemble Machine Learning Approach

Ogini, N. O., Adigwe, W., & Ogwara, N. O. (2022) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2022.14403 · Cited by 3

Detection of Malicious Network Activity by Artificial Neural Network

Turčaník, M., & Baráth, J. (2023) · Advances in Military Technology · doi:10.3849/aimt.01794 · Cited by 3

DDoS Attacks Detection Using a Deep Neural Network Model

Mittal, M., Kumar, K., & Behal, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35644-5_13 · Cited by 3

Joint Selfattention-SVM DDoS Attack Detection and Defense Mechanism Based on Self-Attention Mechanism and SVM Classification for SDN Networks

Man, W., Yang, G., & Shurui, F. E. N. G. (2023) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2023eap1057 · Cited by 3

DDoS Attack Detection in WSN Using Modified BGRU With MFO Model

Venkatasubramanian, S., & Mohankumar, R. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-0502-7.ch014 · Cited by 3

Predição de Ataques DDoS pela Correlação de Séries Temporais via Padrões Ordinais

Albano, L., Borges, L. F., de Neira, A. B., & Nogueira, M. (2023) · Simpósio Brasileiro de Cibersegurança (SBSeg) · doi:10.5753/sbseg.2023.233476 · Cited by 3

EVMNet: Classification of DDoS Attacks using ELM-Enhanced Deep Learning Model with Feature Fusion

Ambekar, N. G., Samal, S., Thokchom, S., & Yogita, Y. (2023) · IEEE India Conference · doi:10.1109/indicon59947.2023.10440676 · Cited by 3

ResNet-Based Detection of SYN Flood DDoS Attacks

Bazzi, H., Nassar, A. H., Haidar, I. M., Haidar, A. M., & Doughan, Z. F. (2024) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/ic2pct60090.2024.10486707 · Cited by 3

Leveraging Federated Learning and Variational Autoencoders for an Enhanced Anomaly Detection System

Nugraha, B., Kota, K., & Bauschert, T. (2024) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft60951.2024.10588934 · Cited by 3

BCDM: An Early-Stage DDoS Incident Monitoring Mechanism Based on Binary-CNN in IPv6 Network

Wang, Y., Wang, X., Ni, Q., Yu, W., & Huang, M. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3431701 · Cited by 3

Multifaceted DDoS Attack Prediction by Multivariate Time Series and Ordinal Patterns

Borges, L. F., de Neira, A. B., Albano, L., & Nogueira, M. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615918 · Cited by 3

AdaFlow: Efficient In-Network Traffic Classification using Programmable Switches

Mittal, S., Kotha, H., Krishna, M. A., & Tammana, P. (2024) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking62109.2024.10619766 · Cited by 3

Online and Collaboratively Mitigating Multi-Vector DDoS Attacks for Cloud-Edge Computing

Leng, S., Guo, Y., Zhang, L., Hao, F., Cao, X., Li, F., & Kou, W. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10623052 · Cited by 3

Enhancing IoT Security: Federated Learning with GANs for Effective Attack Detection

Saeed, B., Arshad, S., Saeed, S. M. U., & Azam, M. A. (2023) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast59916.2023.10713010 · Cited by 3

Machine Learning and Large Language Models-based Techniques for Cyber Threat Detection: A Comparative Study

Abdennebi, A., Morsli, R., Kara, N., & Ould‐Slimane, H. (2024) · Conference on Cloud and Internet of Things (CIoT) · doi:10.1109/ciot63799.2024.10756998 · Cited by 3

Lightweight DDoS Attack Detection Using Bayesian Space-Time Correlation

Mendonça, G., Leão, R. M. M., Silva, E. D. S. E., & Towsley, D. (2025) · IEEE Access · doi:10.1109/access.2025.3553742 · Cited by 3

Performance Analysis of Machine Learning Techniques for Server Health Monitoring Using Time Series Data Against DDOS Attacks

Kumar, S. R., & Kumari, V. V. (2025) · IEEE Access · doi:10.1109/access.2025.3553558 · Cited by 3

Intrusion Detection System: The Use of Neural Network Packet Classification

Ruiz, N., Tavera, B., & Abuzneid, A. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00239 · Cited by 3

A Secured Blockchain-based Information-Centric Network

Abdellah, A. S., Saif, S. M., ElDeeb, H. E., Abd-Elrahman, E., & Taher, M. (2022) · Journal of Computer Science · doi:10.3844/jcssp.2022.266.280 · Cited by 3

FedSA: Arrefecimento Simulado Federado para a Aceleração da Detecção de Intrusão em Ambientes Colaborativos

Neto, H. N. C., Mattos, D. M. F., & Fernandes, N. C. (2021) · doi:10.5753/sbrc.2021.16727 · Cited by 3

A DDoS Detection Method with Feature Set Dimension Reduction

Li, M., Qin, Y., & Zhou, H. (2022) · Communications in computer and information science · doi:10.1007/978-981-16-9576-6_25 · Cited by 3

Anomaly Traffic Detection with Verifiable Interpretation in Industrial Networks

Zhu, H., Tian, J., Tian, Z., Zhu, S., Li, H., & Zhang, Y. (2021) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00083 · Cited by 3

Anomaly Detection Techniques for Different DDoS Attack Types

Gniewkowski, M., Maciejewski, H., & Surmacz, T. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-06746-4_7 · Cited by 3

DTEXNet: Artificial Intelligence-Based Combination Scheme for DDoS Attacks Detection

Elgendy, S., Attawiya, M., Haridy, O., Farag, A., & Branco, P. (2022) · doi:10.21428/594757db.76ee7dbd · Cited by 3

Engenharia de Sinais Precoces de Alerta Para a Predição de Ataques DDoS

de Neira, A. B., Borges, L. F., de Araujo, A. M., & Nogueira, M. (2023) · doi:10.5753/wgrs.2023.724 · Cited by 3

Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

Nawaz, G., Junaid, M., Akhunzada, A., Gani, A., Nawazish, S., Yaqub, A., Ahmed, A. R., & Ajab, H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.026952 · Cited by 2

Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms

Ghazi, M. R., & Raghava, N. S. (2024) · Electronic Research Archive · doi:10.3934/era.2024060 · Cited by 2

Unraveling IoT Traffic Patterns: Leveraging Principal Component Analysis for Network Anomaly Detection and Optimization

Boswell, B., Barrett, S., & Dorai, G. (2024) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs60797.2024.10527310 · Cited by 2

Comparing Hyperbolic Graph Embedding models on Anomaly Detection for Cybersecurity

Miliani, M. Y. T., Sadat, S. A., Haddad, M., Seba, H., & Amrouche, K. (2024) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3664476.3670445 · Cited by 2

In-network Reinforcement Learning for Attack Mitigation using Programmable Data Plane in SDN

Ganesan, A., & Saraç, K. (2024) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637621 · Cited by 2

A Comparative Evaluation of Machine Learning Techniques for Detecting Malicious Network Traffic

Tayal, P., Kumar, R., & Hemlata (2024) · Communications in computer and information science · doi:10.1007/978-3-031-71481-8_15 · Cited by 2

DDoS Attacks Detection using Ensemble Learning

Bhadauria, S., Aildasani, N., Kushwaha, J. P., & Gauttam, H. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724315 · Cited by 2

Layered feature optimization framework based on Filtering, Embedding and data Balancing (L-FEB) for efficient DDoS attack detection

Bhatia, R., & Sharma, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127230 · Cited by 2

Adversarial Evasion Attack Detection using Ensemble Classifiers in P4 Data Plane

Ganesan, A., & Saraç, K. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09948-7 · Cited by 2

Building robust lightweight network intrusion detection: a multi-pronged approach with DSSTE and deep learning for securing edge-enabled industrial IoT applications

Yousuf, R. M., & Ramaiah, M. (2025) · Cluster Computing · doi:10.1007/s10586-025-05171-w · Cited by 2

An FCM-based hybrid method for DDoS attack detection in resource-constrained devices

Keshavamurthy, P., & Kulkarni, S. S. (2025) · Annals of Telecommunications · doi:10.1007/s12243-025-01130-z · Cited by 2

A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance

Hallaji, E., Shanmugam, V., Razavi‐Far, R., & Saif, M. (2025) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece64018.2025.11364502 · Cited by 2

Multi-Scenario Cloud–Edge Collaborative DDoS Detection in LLM-Enabled AIoT

Huang, A., Yan, J., Fan, X., & Zhou, H. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3637740 · Cited by 4

An intelligent light-weight intrusion detection system for edge-centric smart logistics system

Jamal, M. B., Adil, M., Javeed, D., & Zhao, Y. (2025) · Cluster Computing · doi:10.1007/s10586-025-05590-9 · Cited by 2

CIC-DDoS2019 Dataset

Talukder, M. A. (2023) · Data Archiving and Networked Services (DANS) · doi:10.17632/ssnc74xm6r · Cited by 4

A Review of DDoS Evaluation Dataset: CICDDoS2019 Dataset

Saheb, M. C. P., Yadav, M. S., Sallagundla, B., Pujari, J. J., & Maddala, J. B. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-3691-5_34 · Cited by 34

Developing Realistic Distributed Denial of Service (DDoS) Dataset for Machine Learning-based Intrusion Detection System

Hadi, H. J., Hayat, U., Musthaq, N., Hussain, F. B., & Cao, Y. (2022) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms58070.2022.10062034 · Cited by 20

Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization

Shafi, M., Lashkari, A. H., Rodriguez, V., & Nevo, R. (2024) · Information · doi:10.3390/info15040195 · Cited by 45

Multiple Clustering Method for Real-Time Distributed Denial-of-Service Attack Detection

Talmale, G. R., & Shrawankar, U. N. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-6307-6_58