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

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016

Induction of decision trees

Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439

A taxonomy of DDoS attack and DDoS defense mechanisms

Mirković, J., & Reiher, P. L. (2004) · ACM SIGCOMM Computer Communication Review · doi:10.1145/997150.997156 · Cited by 2,028

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

Shiravi, A., Shiravi, H., Tavallaee, M., & Ghorbani, A. A. (2012) · Computers & Security · doi:10.1016/j.cose.2011.12.012 · Cited by 1,345

Characterization of Tor Traffic using Time based Features

Lashkari, A. H., Gil, G. D., Mamun, M., & Ghorbani, A. A. (2017) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006105602530262 · Cited by 926

Towards a Reliable Intrusion Detection Benchmark Dataset

Sharafaldin, I., Gharib, A., Lashkari, A. H., & Ghorbani, A. A. (2017) · Software Networking · doi:10.13052/jsn2445-9739.2017.009 · Cited by 309

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

Jin, S., & Yeung, D. S. (2004) · International Conference on Communications · doi:10.1109/icc.2004.1312847 · Cited by 142

A survey and taxonomy of DoS attacks in cloud computing

Masdari, M., & Jalali, M. (2016) · Security and Communication Networks · doi:10.1002/sec.1539 · Cited by 132

Towards Detection of DDoS Attacks in IoT with Optimal Features Selection

Kumari, P., Jain, A. K., Pal, Y., Singh, K., & Singh, A. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11419-2 · Cited by 6

Hybrid Machine Learning Algorithm for Intrusion Detection Systems

Pansare, S., Malik, A., & Batra, I. (2024) · International Conference on Communication, Computer Sciences and Engineering (IC3SE) · doi:10.1109/ic3se62002.2024.10593622 · Cited by 6

A novel multi-modal incremental tensor decomposition for anomaly detection in large-scale networks

Fan, R., Fan, Q., Li, X., Wang, P., Jing, X., Jin, X., Yao, S., & Liu, P. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121210 · Cited by 6

Enhancing Network Security: A Hybrid Approach for Detection and Mitigation of Distributed Denial-of-Service Attacks Using Machine Learning

Shohan, N. J., Tanbhir, G., Elahi, F., Ullah, A., & Sakib, M. N. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64064-3_7 · Cited by 6

Leveraging Prefix Structure to Detect Volumetric DDoS Attack Signatures with Programmable Switches

Misa, C., Durairajan, R., Gupta, A. K., Rejaie, R., & Willinger, W. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00267 · Cited by 6

Line rate botnet detection with SmartNIC-embedded feature extraction

Patetta, M., Secci, S., & Taktak, S. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110809 · Cited by 6

Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization—Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm

Han, D., Li, H., & Fu, X. (2024) · Sensors · doi:10.3390/s24196179 · Cited by 6

Federated Learning Approach to Classification of DDoS Attacks by 2D-Convolutional Neural Network

Badidová, B., Očkay, M., Javurek, M., Ondrejková, M. B., Krammer, P. H., & Forgáč, R. (2024) · New Trends in Signal Processing (NTSP) · doi:10.23919/ntsp61680.2024.10726294 · Cited by 6

TinyML-Based Intrusion Detection System for Handling Class Imbalance in IoT-Edge Domain Using Siamese Neural Network on MCU

Fusco, P., Montefusco, A., Rimoli, G. P., Palmieri, F., & Ficco, M. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87769-8_34 · Cited by 6

Securing the Internet of Robotic Things (IoRT) against DDoS Attacks: A Federated Learning with Differential Privacy Clustering Approach

Nkoom, M., Hounsinou, S. G., & Crosby, G. V. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104493 · Cited by 9

DDOS Attack Detection Accuracy Improvement in Software Defined Network (SDN) Using Ensemble Classification

Shirmarz, A., Ghaffari, A., Mohammadi, R., & Akleylek, S. (2021) · doi:10.1109/iscturkey53027.2021.9654403 · Cited by 6

DDoS Attack Detection with Packet Continuity Based on LSTM Model

Chu, H., & Yan, C.-Y. (2021) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.1109/ecice52819.2021.9645650 · Cited by 6

Empirical Performance Evaluation of Machine Learning based DDoS Attack Detections

Tran, B.-S., Ho, T.-H., Do, T.-X., & Le, K.-H. (2022) · Intelligent systems reference library · doi:10.1007/978-3-030-90119-6_23 · Cited by 6

Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

Dheyab, S. A., Abdulameer, S. M., & Mostafa, S. A. (2022) · Acta Informatica Pragensia · doi:10.18267/j.aip.199 · Cited by 6

Classification performance improvement by enhancing the detection accuracy of DDOS attacks over flash crowd using CROSS GAN (XGAN)

Sekhar, C. H., Rao, K. V., & Prasad, M. H. M. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15151-0 · Cited by 6

Detecting Distributed DoS Attacks on SDN using Machine Learning (ML) Methods

Kumar, K. A., Akhila, K. R., Bhavya, M., & Sai, M. V. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10099680 · Cited by 6

DDoS Detection Using Hybrid Deep Neural Network Approaches

Hnamte, V., & Hussain, J. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126434 · Cited by 6

Intelligent LSTM (iLSTM)-Security Model for HetIoT

Mahadik, S. S., Pawar, P. M., Muthalagu, R., Prasad, N. R., & Mantri, D. S. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10769-7 · Cited by 6

DDoS Attack Forecasting Using Transformers

Alrahmani, Z. A., & Elleithy, K. M. (2023) · 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/cy59711.2023.10361388 · Cited by 6

Sharing, Licensing, Buying, Selling and Operationalizing ML Models: A Deep Learning based Co-operative and Co-ordinated Security usecase

Panchal, D., Musgrove, D., Baran, I., & Lu, D. (2023) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac59571.2023.10368564 · Cited by 6

Ensemble Learning Methods for DDoS Attack Detection in Cloud Environments: A Comprehensive Review

(2024) · International Journal of Science and Engineering Applications · doi:10.7753/ijsea1305.1007 · Cited by 6

DNS flooding attack detection scheme through Machine Learning

Attar, A. E., Khatoun, R., Chbib, F., Fadlallah, A., & Serhrouchni, A. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592588 · Cited by 6

Optimized Ensemble Model with Genetic Algorithm for DDoS Attack Detection in IoT Networks

Saiyed, M. F., & Al‐Anbagi, I. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615607 · Cited by 6

NetSpirit: A Smart Collaborative Learning Framework for DDoS Attack Detection

Xu, K., Zheng, Y., Yao, S. F., Wu, B., & Xu, X. F. (2021) · IEEE Network · doi:10.1109/mnet.001.2100234 · Cited by 6

AI-Based Intrusion Detection System for Secure AI BOX Applications

Chen, J.-L., Chen, Z.-Z., Chang, Y.-S., Li, C.-I., Kao, T.-I., Lin, Y.-T., Xiao, Y.-Y., & Qiu, J.-F. (2023) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic57133.2023.10066986 · Cited by 6

Castles Built on Sand: Observations from Classifying Academic Cybersecurity Datasets with Minimalist Methods

D’hooge, L., Verkerken, M., Wauters, T., Turck, F. D., & Volckaert, B. (2023) · International Conference on Internet of Things, Big Data and Security (IoTBDS) · doi:10.5220/0011853300003482 · Cited by 7

Ptu: Pre-Trained Model for Network Traffic Understanding

Peng, L., Xie, X., Huang, S., Wang, Z., & Cui, Y. (2024) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp61940.2024.10858503 · Cited by 5

A comprehensive survey of manual and dynamic approaches for cybersecurity taxonomy generation

Spyros, A., Kougioumtzidou, A., Papoutsis, A., Darra, E., Kavallieros, D., Tziouvaras, A., Tsikrika, T., Vrochidis, S., & Kompatsiaris, I. Y. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02382-w · Cited by 5

Enhanced Intrusion Detection System Using a Two‐Staged Feature Selection Method

Lalduhsaka, R., & Khan, A. K. (2025) · Security and Privacy · doi:10.1002/spy2.70025 · Cited by 5

SAMMF: A Self-Adaptive Multi-Model Fusion Framework for NWDAF Traffic Anomaly Detection

Zhang, M., Tang, H., Yang, J., Qiu, H., Zhao, Y., Xu, M., & Bai, Y. (2025) · IEEE Networking Letters · doi:10.1109/lnet.2025.3579358 · Cited by 5

ProtoMAML: A Hybrid Meta-Learning Approach Integrating Prototypical Networks for Data-Efficient DDoS Attack Detection

Alfatemi, A., Rahouti, M., Bhuiyan, M. Z. A., Solyman, A., & Aledhari, M. (2025) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc65282.2025.11059701 · Cited by 5

DDoS Defense Strategy Based on Blockchain and Unsupervised Learning Techniques in SDN

Peng, S., Tian, J., Zheng, X., Chen, S., & Shu, Z. (2025) · Future Internet · doi:10.3390/fi17080367 · Cited by 5

Intrusion detection systems in IoT: A detailed review of threat categories, detection strategies, and future technologies

Aydın, B., Aydin, H., & Görmüş, S. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104291 · Cited by 8

Enabling Real-Time, Explainable DDoS Mitigation via On-Premise Large Language Models and Flow Analysis

Wondimu, H., Alfatemi, A., Rahouti, M., Chehri, A., Weichbroth, P., & Ghani, N. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.260 · Cited by 5

Deep Learning Utilization for DDoS Attack Detection with Federated Learning: A Case Study on the CICDDoS2019 Dataset

Alsarhan, A., Barhoush, M. M., Khassawneh, B., AL-Essa, M., Aljaidi, M., & Al-Na’amneh, Q. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.14119 · Cited by 5

Intrusion Detection and Prevention for Intra- and Inter-Vehicle Networks: A Comprehensive Survey

El-Fatyany, A., Mahmood, J., Berwo, M. A., Wang, X., Lu, L., Xue, Q., & Ren, K. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3674515 · Cited by 5

Detecting and mitigating DDoS attacks with AI: a survey

Apostu, A., Gheorghe, S., Hiji, A. I., Cleju, N., Pătraşcu, A., Rusu, C., Ionescu, R. T., & Irofti, P. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11639-x · Cited by 11

Deep learning approach to DDoS attack with imbalanced data at the application layer

Gunawan, R., Ghani, H. A., Khamis, N., Amien, J. A., & Ismanto, E. (2023) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v21i5.24857 · Cited by 5

CyberGuard: Detecting Adversarial DDoS Attacks in SDN using WGAN-CNN-GRU

Goud, K. S., & Giduturi, S. R. (2024) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect60202.2024.10468690 · Cited by 5

Computer vision based distributed denial of service attack detection for resource-limited devices

Gebrye, H., Wang, Y., & Li, F. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109716 · Cited by 7

An Efficient Explainable Artificial Intelligence (XAI)-Based Framework for a Robust and Explainable IDS

Nugraha, B., Jnanashree, A. V., & Bauschert, T. (2024) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet64211.2024.10851760 · Cited by 5

Integrated Game Theoretic and Honeypot Based Distributed Denial of Service Attack Detection and Mitigation in Advanced Metering Infrastructure

Raja, D. J. S., Hemavathi, N., Ramakrishnan, S., & Arulmozhi, P. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3556910 · Cited by 5

A DDoS attack detection method based on improved transformer and temporal feature enhancement

Fan, Y., Ma, H., Zhang, Y., Li, S., Guo, X., & Wang, B. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07440-2 · Cited by 5

Intrusion Detection for Modern DDos Attacks Classification Based on Convolutional Neural Networks

Chen, W., Zhang, H., Zhou, X., & Weng, Y. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-79474-3_4 · Cited by 6

Hybrid Deep Neural Architecture for Detection of DDoS Attacks in Cloud Computing

Bhardwaj, A., Mangat, V., & Vig, R. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-0730-1_5 · Cited by 5

A behavioral model for characterizing flooding distributed denial of service attacks

Tinubu, O. C., Sodiya, A. S., & Ojesanmi, O. A. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01097-3 · Cited by 5

A novel, refined dataset for real-time Network Intrusion Detection

Komisarek, M., Pawlicki, M., Mihăilescu, M.-E., Mihai, D., Carabaș, M., Kozik, R., & Choraś, M. (2022) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3538969.3544486 · Cited by 5

On Early Detection of Anomalous Network Flows

Fox, G., & Boppana, R. V. (2023) · IEEE Access · doi:10.1109/access.2023.3291686 · Cited by 6

MAGNETO and DeepInsight: Extended Image Translation with Semantic Relationships for Classifying Attack Data with Machine Learning Models

Dunmore, A., Dunning, A., Jang‐Jaccard, J., Sabrina, F., & Kwak, J. (2023) · Electronics · doi:10.3390/electronics12163463 · Cited by 5

Smart-PKI: A Blockchain-based Distributed Identity Validation Scheme for IoT Devices

Bao, H., Zhang, X., Wang, G., Tian, R., Duan, J., & Zhao, Y. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279752 · Cited by 5

Distributed denial of service attacks classification system using features selection and ensemble techniques

Bagdadi, L., & Messabih, B. (2024) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v34.i3.pp1868-1878 · Cited by 5

An intelligent behavioral-based DDOS attack detection method using adaptive time intervals

Shamekhi, A., Shamsinejad, P., & Javidan, R. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01690-2 · Cited by 5

Relative Frequency-Rank Encoding for Unsupervised Network Anomaly Detection

Kim, M., Jang, W., Hur, J., & Yoon, M. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3391396 · Cited by 5

A Reconfigurable IDS Framework for Encrypted and Non-Encrypted Network Data in Supply Chains

Papadogiannaki, E., Chrysos, G. G., Georgopoulos, K., & Ioannidis, S. (2023) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet60227.2023.10525930 · Cited by 5

Hifoots: A Highly Efficient DDoS Attack Detection Scheme Deployed in Smart IoT Homes

Wang, Y., Zhang, X., Lai, Y., Zhao, Z., & Deng, Y. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3424888 · Cited by 5

An Automated Benchmarking Framework for Anomaly-based Intrusion Detection Systems

Nguyen, H.-C.-T., Nguyen, X.-H., & Le, K.-H. (2024) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr63514.2024.10660867 · Cited by 5

BDIP: An Efficient Big Data-Driven Information Processing Framework and Its Application in DDoS Attack Detection

Fan, Q., Li, X., Wang, P., Jin, X., Yao, S., Miao, S., Li, S., An, M., & Xu, J. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3464729 · Cited by 5

DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

Zhang, A., Liang, J., Li, C., Zhang, Q., & Wang, X. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.058081 · Cited by 5

Hybrid Machine Learning Approaches For Classification DDoS Attack

Djama, A., Maazouz, M., & Kheddar, H. (2024) · International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech) · doi:10.1109/ecte-tech62477.2024.10850919 · Cited by 5

Adapting to the Evolution: Enhancing Intrusion Detection Through Machine Learning in the QUIC Protocol Era

Kadi, A., Khoukhi, L., Viinikka, J., & Fabre, P.-E. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3540753 · Cited by 5

A Deep Learning GRU-BiLSTM for DDoS Attack Detection

Al-Eryani, A. M., Omara, F. A., & Hossny, E. (2025) · SN Computer Science · doi:10.1007/s42979-025-04110-1 · Cited by 8

On adversarial attack detection in the artificial intelligence era: Fundamentals, a taxonomy, and a review

Roken, N. A., Hacid, H., Bouridane, A., & Hussain, A. J. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200554 · Cited by 8

IoT Area Network Simulator For Network Dataset Generation

Pham, V. C., Makino, Y., Pho, K., Lim, Y., & Tan, Y. (2020) · Journal of Information Processing · doi:10.2197/ipsjjip.28.668 · Cited by 5

DDoS Detection on Internet of Things using Unsupervised Algorithms

Tekleselassie, H. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202129701005 · Cited by 5

Denial of Service Attack Detection via Differential Analysis of Generalized Entropy Progressions

Subasi, O., Manzano, J., & BARKER, K. J. (2023) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr57506.2023.10224957 · Cited by 5

FNEL:An Evolving Intrusion Detection System Based on Federated Never-Ending Learning

Qin, T., Cheng, G., Chen, W., & Lei, X. (2021) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn53354.2021.00047 · Cited by 5

Prevention of DrDoS Amplification Attacks by Penalizing the Attackers in SDN Environment

Saharan, S., & Gupta, V. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-99587-4_58 · Cited by 5

Self-Organization Map (SOM) Algorithm for DDoS Attack Detection in Distributed Software Defined Network (D-SDN)

Rafiee, M., & Shirmarz, A. (2022) · Journal of Information Systems and Telecommunication (JIST) · doi:10.52547/jist.15644.10.38.120 · Cited by 5

Development and Evaluation of Ensemble Learning Models for Detection of DDOS Attacks in IoT

Yılmaz, Y., & Buyrukoğlu, S. (2022) · Hittite Journal of Science & Engineering · doi:10.17350/hjse19030000257 · Cited by 5

A Lightweight DoS and DDoS Attack Detection Mechanism-Based on Deep Learning

Satpathy, S. P., Mohanty, S., & Kumar, R. (2022) · International Conference on Computational Intelligence and Networks (CINE) · doi:10.1109/cine56307.2022.10037402 · Cited by 5

Deep Reinforcement Learning for Intrusion Detection in Internet of Things: Best Practices, Lessons Learnt, and Open ChallengesPreprint

Moreno, J. F. C., Rizzardi, A., Sicari, S., & Coen‐Porisini, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4482146 · Cited by 5

A Hybrid Deep Learning-Powered SDN-Based Intrusion Detection Architecture for Cognitive IoT Security

Panse, T. G., Gopal, G. V., Manthina, B. S., Battu, H. R., Sunitha, P., & Sailaja, V. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.12564 · Cited by 4

Enhancing cybersecurity in smart and cognitive cities: A systematic mapping of AI-based techniques

Alraddadi, R. T., Alshayeb, M., Mahmood, S., & Niazi, M. (2025) · Array · doi:10.1016/j.array.2025.100606 · Cited by 4

Evaluating Synthetic Malicious Network Traffic Generated by GAN and VAE Models: A Data Quality Perspective

Πεππές, Ν., Alexakis, T., Daskalakis, E., & Adamopoulou, E. F. (2025) · Future Internet · doi:10.3390/fi17120561 · Cited by 4

Delving deep: DDoS attack resilience through deep learning approaches

Likhar, P., Gupta, S. K., Choudhary, J., & Singh, D. P. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02651-8 · Cited by 5

A Comprehensive Review: The Evolving Cat-and-Mouse Game in Network Intrusion Detection Systems Leveraging Machine Learning

Alasad, Q., Ahmed, M., Alahmed, S., Khattab, O. T., Abdulwahhab, S. A., & Yuan, J. S. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6010013 · Cited by 5

Abnormal Traffic Detection Method Based on DCNN‐GRU Architecture in SDN

Duan, X., Wang, K., Fu, Y., Liu, T., Yu, Y., Xu, J., & Wang, L. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/2846238 · Cited by 4

Fine-Grained Detection and Analysis of Unknown Encrypted Malicious Traffic From Mixed Noisy Labels

Meng, Q., Yuan, Q., Shen, M., Lu, S., Li, G., Tao, J., Yu, Y., & Wang, Y. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3697849 · Cited by 4

Distributed Denial of Service Attack Detection Using Hyper Calls Analysis in Cloud

Umamaheswari, K., Subramanian, N., & Subramaniyan, M. (2023) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2023.04.06 · Cited by 4

Detection of DDoS attacks on time based features using Stacking ensemble technique

Shashank, T., & Singh, A. V. (2023) · International Conference On Smart Technologies For Smart Nation (SmartTechCon) · doi:10.1109/smarttechcon57526.2023.10391724 · Cited by 4

Enhancing Network Security in IoT Applications through DDoS Attack Detection Using ML

Salama, A. M., Mohamed, M. A., & AbdElhalim, E. (2024) · MEJ Mansoura Engineering Journal · doi:10.58491/2735-4202.3181 · Cited by 4

Optimizing DDoS Detection with Time Series Transformers

Ejikeme, C. H., Kahani, N., & Ajila, S. A. (2024) · IEEE International Conference on Collaborative Advances in Software and Computing (CASCON) · doi:10.1109/cascon62161.2024.10837833 · Cited by 4

Network-Based Multi-Protocol Attack Detection in Internet-of-Medical-Things Using Explainable Machine Learning

Alani, M. M. (2024) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese63988.2024.10911967 · Cited by 4

Feature attention assisted convolutional stacked sparse auto-encoder model for intrusion detection in network function virtualization environment

Tikhe, G. N., & Patheja, P. S. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104595 · Cited by 4

A Generalized and Real-Time Network Intrusion Detection System Through Incremental Feature Encoding and Similarity Embedding Learning

Alitbi, Z., Seno, S. A. H., Bafghi, A. G., & Zabihzadeh, D. (2025) · Sensors · doi:10.3390/s25164961 · Cited by 4

A survey on 5G private and B5G network threats and safeguarding AI-based security mechanisms through the layered analysis

Tariq, S., Rodríguez, E., Masip‐Bruin, X., Díaz, R., Martrat, J., & Trakadas, P. T. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111594 · Cited by 4

Sec-Llama: a Compact Fine-Tuned LLM for Network Intrusion Detection in Kubernetes Clusters

Abdennebi, A., Kara, N., Lahlou, L., Younis, M., & Ould‐Slimane, H. (2025) · IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) · doi:10.1109/icmlcn64995.2025.11140090 · Cited by 6

Construct an efficient distributed denial of service attack detection system based on data mining techniques

Gharkan, D. K., & Abdulrahman, A. A. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v29.i1.pp591-597 · Cited by 4

A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

Wei, W., Liu, X., Sheng, C., & Feng, A. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055418 · Cited by 4

Intrusion Detection in Cyber Physical Systems Using Multichain

Nalayini, C. M., Katiravan, J., & Sathya, V. (2023) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-8666-5.ch009 · Cited by 4

CAFNet: Compressed Autoencoder-based Federated Network for Anomaly Detection

Tayeen, A. S. M., Misra, S., Cao, H., & Harikumar, J. (2023) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom58377.2023.10356377 · Cited by 4

Denial-of Service (DoS) Attack Detection Using Edge Machine Learning

Huynh, N. S., Cruz, S. D. L., & Perez-Pons, A. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00264 · Cited by 4

Detecting DDoS Attacks on the Network Edge: An Information-Theoretic Correlation Analysis

Araki, R., Sahoo, K. S., Taenaka, Y., Kadobayashi, Y., Elmroth, E., & Bhuyan, M. H. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00080 · Cited by 4

Enhanced Attacks Detection and Mitigation in Software Defined Networks

Forbacha, S. C., Kinteh, M. K., & Hamza, E. M. (2024) · American Journal of Computing and Engineering · doi:10.47672/ajce.2120 · Cited by 4

Application-Layer DDoS Detection via Efficient Machine Learning and Feature Selection

Sharif, D. M. (2023) · International Conference on Engineering Applied and Nano Sciences (ICEANS) · doi:10.1109/iceans58413.2023.10630487 · Cited by 4

Distributed Federated Split Learning Based Intrusion Detection System

Almarshdi, R., Fadel, E. A., Alowidi, N., & Nassef, L. (2024) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2024.056792 · Cited by 5

DDoS Attack Detection in Cloud Computing Using Optimized Elman Neural Network Based on Bacterial Colony Optimization and Centroid Opposition-Based Learning

Kalvikkarasi, S., & Saraswathi, A. (2024) · International Journal of Computer Networks And Applications · doi:10.22247/ijcna/2024/50 · Cited by 4

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tiered Mitigation Strategy

Jha, P., Singh, G., Kumar, A., Agrawal, D., Patel, Y. S., & Forough, J. (2025) · International Conference on Distributed Computing and Networking (ICDCN) · doi:10.1145/3700838.3703687 · Cited by 4

Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems

Kianpisheh, S., Benzaïd, C., & Taleb, T. (2024) · Globecom · doi:10.1109/globecom52923.2024.10901544 · Cited by 6

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