Cited by 1,275Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingNetwork Packet Processing and Optimization

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

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

Sharafaldin, Iman, Lashkari, Arash Habibi, Hakak, Saqib, Ghorbani, Ali A.·2019·2019 International Carnahan Conference on Security Technology (ICCST)

DOI 10.1109/ccst.2019.8888419

Open this paper in CoChat — free account

Abstract

Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic. Although many statistical methods have been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. On the other hand, the evaluation of new detection algorithms and techniques heavily relies on the existence of well-designed datasets. In this paper, first, we review the existing datasets comprehensively and propose a new taxonomy for DDoS attacks. Secondly, we generate a new dataset, namely CICDDoS2019, which remedies all current shortcomings. Thirdly, using the generated dataset, we propose a new detection and family classificaiton approach based on a set of network flow features. Finally, we provide the most important feature sets to detect different types of DDoS attacks with their corresponding weights.

Integrity signals

Registry

Crossref · 10.1109/ccst.2019.8888419

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
Self-citation
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
Self-citation
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
Self-citation
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

Flagged first, then by citations

Show all 17 references

Research works printed but not linked: 5

1987, Request for comments: 1001 (rfc1001), Protocol Standard for a NetBIOS Service on a TCP/UDP Transport Concepts and Methods
2016, Request for comments: 7766 (rfc7766), DNS Transport over TCP - Implementation Requirements
2017, CICFlowMeter
0, The CAIDA UCSD ”DDoS Attack 2007” Dataset
0, DARPA 2000 Intrustion Detection Scenario Specific Data Sets

Sorted by citations

Items 501–600 of 1,001

A Real Network Environment Dataset for Traffic Analysis
Jiang, W., Zhang, B., Zhu, Q., Liao, C., & Wang, W. (2025) · Scientific Data · doi:10.1038/s41597-025-04876-2 · Cited by 10
Big knowledge-based semantic correlation for detecting slow and low-level advanced persistent threats
Lajevardi, A. M., & Amini, M. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00532-9 · Cited by 9
Generating Synthetic Tabular Data for DDoS Detection Using Generative Models
Saka, S., Al-Ataby, A., & Selis, V. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00196 · Cited by 9
Introducing a Comprehensive, Continuous, and Collaborative Survey of Intrusion Detection Datasets
Bönninghausen, P., Uetz, R., & Henze, M. (2024) · Cyber Security Experimentation and Test Workshop (CSET) · doi:10.1145/3675741.3675754 · Cited by 9
DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming
Feng, S., Mohammady, M., Wang, H., Li, X., Qin, Z., & Hong, Y. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00124 · Cited by 9
Causal Genetic Network Anomaly Detection Method for Imbalanced Data and Information Redundancy
Zeng, Z., Liu, X., Dai, M., Zheng, J., Deng, X., Zeng, D., & Chen, J. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3455768 · Cited by 9
Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems
Ahmed, M., Alasad, Q., Yuan, J. S., & Alawad, M. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8120191 · Cited by 9
Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning
Babbar, H., Rani, S., & Driss, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0314695 · Cited by 9
Towards Robust Cyber Attack Taxonomies: A Survey with Requirements, Structures, and Assessment
Santos, P. R. D. P. F., Resende, P. A. A., Gondim, J. J. C., & Drummond, A. C. (2025) · ACM Computing Surveys · doi:10.1145/3717606 · Cited by 9
F-OSFA: A Fog Level Generalizable Solution for Zero-Day DDOS Attacks Detection
Minhas, M. R., Shafi, Q. M., Khan, S. A., Ahmad, T., Ullah, S., Buriro, A., & Yaqub, M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3557822 · Cited by 9
Advancing Predictive Security for Consumer Applications in Beyond 5G/6G Networks With Annotated Datasets
Xilouris, G. K., Vekraki, A., Christopoulou, M. I., Kourtis, M., Markakis, E., & Trakadas, P. T. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3567151 · Cited by 9
DDoS Attack Detection in IoT: A Comparative Resource and Performance Analysis of Deep Learning and Machine Learning Models
A.Abualhassan, A., Rashid, I., Binbeshr, F., & Imam, M. (2025) · IEEE Access · doi:10.1109/access.2025.3583855 · Cited by 9
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows
Miguel-Diez, A., Campazas, A., Álvarez-Aparicio, C., Esteban, G., & Guerrero‐Higueras, Á. M. (2025) · Logic Journal of IGPL · doi:10.1093/jigpal/jzaf020 · Cited by 9
ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning
Hernandez-Suarez, A., Sánchez-Pérez, G., Toscano, K., Perez-Meana, H. M., Olivares-Mercado, J., Portillo-Portillo, J., Benítez-García, G., Orozco, A. L. S., & Villalba, L. J. G. (2023) · Sensors · doi:10.3390/s23031231 · Cited by 9
Effective Industrial Internet of Things Vulnerability Detection Using Machine Learning
Nwakanma, C. I., Ahakonye, L. A. C., Njoku, J. N., Eze, J., & Kim, D. (2022) · Information Technology for Education and Development (ITED) · doi:10.1109/ited56637.2022.10051622 · Cited by 9
ACapsule Q-Learning Based Reinforcement Model for Intrusion Detection System on Smart Grid
Khoei, T. T., & Kaabouch, N. (2023) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit57321.2023.10187374 · Cited by 9
ONOS DDoS Defender: A Comparative Analysis of Existing DDoS Attack Datasets using Ensemble Approach
Aslam, N., Srivastava, S., & Gore, M. M. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10848-9 · Cited by 9
Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion
Goud, K. S., & Rao, G. S. (2024) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect60202.2024.10469528 · Cited by 9
Gradient Boosting classifier performance evaluation using Generative Adversarial Networks
Lavanya, P., Singh, R. P., Kumaran, U. S., & Kumar, P. D. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.285 · Cited by 9
An Investigation on Open-RAN Specifications: Use Cases, Security Threats, Requirements, Discussions
Park, H., Nguyen, T., & Park, L. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.052394 · Cited by 11
Detecting DDoS Attacks Through Decision Tree Analysis: An EDA Approach with the CIC DDoS 2019 Dataset
Zy, A. T., Amali, A., Rifa’i, A. M., Kamalia, A. Z., & Sulaeman, A. A. (2024) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee63424.2024.10730435 · Cited by 9
DETECTION OF DDOS ATTACKS BASED ON DENSE NEURAL NETWORKS, AUTOENCODERS AND PEARSON CORRELATION COEFFICIENT
Li, J. (2020) · DalSpace (Dalhousie University) · Cited by 9
Application Layer Denial of Services Attack Detection Based on StackNet
Smadi, S. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/215932020 · Cited by 9
Application of Whale Optimization Algorithm in DDOS Attack Detection and Feature Reduction
Varma, P. R. K., Raju, K. V. S., & Ruthala, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-33-4305-4_8 · Cited by 9
A Deep Learning Approach for DDoS Attack Detection Using Supervised Learning
Tekleselassie, H. (2021) · MATEC Web of Conferences · doi:10.1051/matecconf/202134801012 · Cited by 9
KCC Method: Unknown Intrusion Detection Based on Open Set Recognition
Xu, S., Li, L., Yang, H., & Tang, J. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00213 · Cited by 9
Ensembling PCA-based Feature Selection with Random Tree Classifier for Intrusion Detection on IoT Network
Alsharif, N. H. (2021) · Proceeding of the Electrical Engineering Computer Science and Informatics · doi:10.23919/eecsi53397.2021.9624298 · Cited by 9
A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning
Teixeira, D., Malta, S., & Pinto, P. (2022) · Future Internet · doi:10.3390/fi14030072 · Cited by 9
Machine learning-based DDoS detection for IoT networks
Xie, Y. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/29/20230972 · Cited by 8
Optimized Machine Learning-Based Intrusion Detection System for Internet of Vehicles
Limouchi, E., & Chan, F. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371867 · Cited by 8
An Artificial Intelligence-based Ensemble Technique for Intrusion Detection and Prevention in IoT Systems
Adebayo, P. O., Abdullahi, M. J., Lawrence, M. O., Ibrahim, Y. A., Silas, F., & Bijik, A. H. (2024) · International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) · doi:10.1109/seb4sdg60871.2024.10629681 · Cited by 8
Detecting DDoS Attacks in Network Traffic Based on Supervised Machine Learning Techniques
Alfathe, M., Mustapha, A., Mohamed, H. L., Mostafa, S. A., Yousif, Y. K., & Al-Shakarchi, A. H. (2024) · Al-Noor Journal for Information Technology and Cybersecurity · doi:10.69513/jncs.v1.i0.a2 · Cited by 8
SAGIN-ID: A rapid intrusion detection method for space-air-ground integrated network based on smart contracts
Xie, N., Yuan, Q., Xie, L., & Di, X. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110084 · Cited by 8
Enhanced Intrusion Detection with Advanced Deep Features and Ensemble Classifier Techniques
Pawan, T., Mainalli, K., Allagi, S., Debnath, S. K., Bagchi, S., Leong, W. Y., & Khan, M. N. (2025) · SN Computer Science · doi:10.1007/s42979-025-03859-9 · Cited by 8
Enhancing security in software defined networks: Privacy-preserving intrusion detection with Homomorphic Encryption
Naresh, V. S., & Ayyappa, D. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104084 · Cited by 10
T‐ VAE : Transformer‐Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data
Li, C., Kiat, Y. C., Jing, J., & Long, C. (2025) · Expert Systems · doi:10.1111/exsy.70078 · Cited by 8
Deep anomaly detection of temporal heterogeneous data in AIOps: a survey
Gui, J., Ma, Z., Zhou, H., Su, Y., Zhang, M., Yu, K., & Wu, X. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2400467 · Cited by 12
A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions
Reddy, D. H. V., & Kumar, K. A. (2026) · Frontiers in Big Data · doi:10.3389/fdata.2026.1878260 · Cited by 8
Application Layer Packet Processing Using PISA Switches
Bütün, İ., Tuncel, Y. K., & Öztoprak, K. (2021) · Sensors · doi:10.3390/s21238010 · Cited by 8
An Autoencoder-Based Approach for DDoS Attack Detection Using Semi-Supervised Learning
Fardusy, T., Afrin, S., Sraboni, I. J., & Dey, U. K. (2023) · International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM) · doi:10.1109/ncim59001.2023.10212626 · Cited by 10
Data-Driven Network Analysis for Anomaly Traffic Detection
Alam, S., Alam, Y., Cui, S., & Akujuobi, C. M. (2023) · Sensors · doi:10.3390/s23198174 · Cited by 8
Uncovering collateral damages and advanced defense strategies in cloud environments against DDoS attacks: A comprehensive review
Verma, P., Bharot, N., Breslin, J. G., Sharma, M., Chaurasia, N., & Vidyarthi, A. (2024) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4934 · Cited by 8
ALBUS: a Probabilistic Monitoring Algorithm to Counter Burst-Flood Attacks
Scherrer, S., Vliegen, J., Sateesan, A., Hsiao, H., Mentens, N., & Perrig, A. (2023) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds60354.2023.00025 · Cited by 8
MVDet: Encrypted malware traffic detection via multi-view analysis
Cui, S., Han, X., Dong, C., Liu, Y., Liu, S., Lü, Z., & Liu, Y. (2024) · Journal of Computer Security · doi:10.3233/jcs-230024 · Cited by 8
Evaluation of UDP-Based DDoS Attack Detection by Neural Network Classifier with Convex Optimization and Activation Functions
Dasari, K. B., Mekala, S., & Kaka, J. R. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290321 · Cited by 8
DDoS Attack Detection and Mitigation in 5G Networks using P4 and SDN
Andrade, D. P., Akkaya, K., Perez-Pons, A., Uluagac, A. S., & Sahin, A. (2024) · Conference on Local Computer Networks · doi:10.1109/lcn60385.2024.10639648 · Cited by 8
Transformer Neural Networks for Intrusion Diagnostic Unit (IDU) and Anomaly Detection in Distributed Energy Resources (DERs)
Balaji, S. R. A., Hassan, T., Ramchandra, A. R., Ranganathan, P., Plathottam, S. J., & Jhala, K. (2024) · International Conference on Electrical Engineering and Green Energy (CEEGE) · doi:10.1109/ceege62093.2024.10744069 · Cited by 8
An augmented K‐means clustering approach for the detection of distributed denial‐of‐service attacks
Marvi, M., Arfeen, A., & Uddin, R. (2021) · International Journal of Network Management · doi:10.1002/nem.2160 · Cited by 8
Design and evaluation of adaptive system for big data cyber security analytics
Ullah, F., Babar, M. A., & Aleti, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117948 · Cited by 8
MOCA: A Network Intrusion Monitoring and Classification System
Fuhr, J., Wang, F., & Tang, Y. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2030032 · Cited by 8
A fast anomaly network traffic detection method based on the constrained k-nearest neighbor
Zhu, Z., Xie, Y., Xu, Y., & Hu, W. (2023) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence56041.2023.10048869 · Cited by 8
A comparative study of classification techniques in data mining algorithms used for medical diagnosis based on DSS
Ahmed, A. S., & Salah, H. A. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v12i5.4804 · Cited by 8
A Review on DDoS Attacks Classifying and Detection by ML/DL Models
Alqahtani, H. M., & Abdullah, M. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150283 · Cited by 8
A Framework for Intrusion Detection Models for IoT Networks using Deep LearningPreprint
Bajpai, S., & Sharma, K. D. (2022) · Research Square · doi:10.21203/rs.3.rs-2010844/v1 · Cited by 8
A dataset collected in real-world industrial control systems for network attack detection
Zhou, X., Cheng, Z., Wang, C., Wang, S., Tao, C., Zhou, Z., Chen, X., Luo, J., Wang, D., & Zhou, H. (2026) · Scientific Data · doi:10.1038/s41597-026-06738-x · Cited by 8
A Dynamic Spatiotemporal Deep Learning Solution for Cloud–Edge Collaborative Industrial Control System Distributed Denial of Service Attack Detection
Cao, Z., Liu, B., Gao, D., Zhou, D., Han, X., & Cao, J. (2025) · Electronics · doi:10.3390/electronics14091843 · Cited by 7
Detection of Distributed Denial of Service Attacks Using Entropy on Sliding Window with Dynamic Threshold
Saharan, S., Gupta, V., Vora, N., & Maheshwari, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-99584-3_37 · Cited by 7
Packet Continuity DDoS Attack Detection for Open Fronthaul in ORAN System
Chang, J.-E., Chiu, Y.-C., Ma, Y., Li, Z., & Shao, C. (2024) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms59830.2024.10575799 · Cited by 7
Hybrid Deep Learning Model for Detecting DDoS Attacks in IoT Networks
Jyothsna, V., Vardhireddy, C. K. R., Thangella, H., Sarangula, K., Tamidilapati, R., & Pydala, B. (2024) · Advances in computer science research · doi:10.2991/978-94-6463-471-6_42 · Cited by 7
DDoSBERT: Fine-tuning variant text classification bidirectional encoder representations from transformers for DDoS detection
Le, T.-T.-H., Heo, S., Cho, J., & Kim, H. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111150 · Cited by 7
FIGS: A Realistic Intrusion-Detection Framework for Highly Imbalanced IoT Environments
Anbiaee, Z., Dadkhah, S., & Ghorbani, A. A. (2025) · Electronics · doi:10.3390/electronics14142917 · Cited by 7
Self-citation
Efficient DDoS Detection with Minimal Features: High Accuracy Using CIC-DDoS2019
Hasan, M. A., Eaman, A., & Hassan, E. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.07.164 · Cited by 8
Enhanced cybersecurity for smart grids: Detecting protocol-specific DDoS attacks on Modbus networks
Kanwal, S., Amin, W., Khan, A. A., Rafique, B., Huang, Q., Li, J., & Batool, I. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110629 · Cited by 8
DDoS Attacks Detection in SDN Through Network Traffic Feature Selection and Machine Learning Models
Cuesta, E. P. E., Quintero, J. C. M., & Palma, J. D. A. (2025) · Telecom · doi:10.3390/telecom6030069 · Cited by 11
Advances in machine and deep learning for intrusion detection in VANETs: a comprehensive survey
Chouchene, I., Eltaief, H., & Youssef, H. (2026) · Cluster Computing · doi:10.1007/s10586-026-05987-0 · Cited by 7
A Machine Learning Classification Model Using Random Forest for Detecting DDoS Attacks
Chu, T. S., Si, W., Simoff, S. J., & Nguyễn, Q. V. (2022) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc55209.2022.9851797 · Cited by 7
Intrusion Detection and Prevention Model for Blockchain Based IoMT Applications
Almalki, J. (2024) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.038085 · Cited by 7
A Contribution to DDoS Attack Detection Based on Deep Neural Networks
Badidová, B., Forgáč, R., Očkay, M., & Javurek, M. (2023) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti59972.2023.10382024 · Cited by 7
Unsupervised Feature Engineering Approach to Predict DDoS Attacks
de Neira, A. B., Borges, L. F., de Araujo, A. M., & Nogueira, M. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437029 · Cited by 7
A task offloading approach based on risk assessment to mitigate edge DDoS attacks
Huang, H., Sun, B., & Hu, L. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103789 · Cited by 7
Actively Detecting Multiscale Flooding Attacks & Attack Volumes in Resource-Constrained ICPS
Zahid, F., Kuo, M. M. Y., Sinha, R., Funchal, G., Pedrosa, T., & Leitão, P. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3383520 · Cited by 7
A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection
Sharma, N., Arora, B., Ziyad, S. R., Singh, P. K., & Singh, Y. P. (2024) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2024-0016 · Cited by 10
FAPM: A Fake Amplification Phenomenon Monitor to Filter DRDoS Attacks With P4 Data Plane
Tang, D., Wang, X., Li, K., Yin, C., Liang, W., & Zhang, J. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3449889 · Cited by 7
Next-Gen Security: Enhanced DDoS Attack Detection for Autonomous Vehicles in 6G Networks
Anbalagan, S., Alhakami, W., Bhooma, M. J., Suria, M. V., Dev, K., & Raja, G. (2024) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2024-spring62846.2024.10683431 · Cited by 7
A new DDoS attack detection model based on improved stacked autoencoder and gated recurrent unit for software defined network
Wang, H., Jia, N., He, Y., & Lian, Z. (2025) · The Computer Journal · doi:10.1093/comjnl/bxaf021 · Cited by 7
Enhancing cybersecurity: A high-performance intrusion detection approach through boosting minority class recognition
Asry, C. E., Benchaji, I., Douzi, S., & Ouahidi, B. E. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317346 · Cited by 7
Deep learning based identification of DDoS attacks in industrial application
Bhati, A., Bouras, A., Qidwai, U. A., & Belhi, A. (2020) · World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) · doi:10.1109/worlds450073.2020.9210320 · Cited by 7
A Deep Learning-Based Methodology in Fog Environment for DDOS Attack Detection
Bishnoi, S., Mohanty, S., & Sahoo, B. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418363 · Cited by 7
Discovering Non-Metadata Contaminant Features in Intrusion Detection Datasets
D’hooge, L., Verkerken, M., Wauters, T., Volckaert, B., & Turck, F. D. (2022) · Conference on Privacy, Security and Trust · doi:10.1109/pst55820.2022.9851974 · Cited by 7
Intrusion-Based Attack Detection Using Machine Learning Techniques for Connected Autonomous Vehicle
Bhavsar, M., Roy, K., Liu, Z., Kelly, J. C., & Gokaraju, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08530-7_43 · Cited by 7
MFFAN: Multiple Features Fusion with Attention Networks for Malicious Traffic Detection
Huang, W., Han, X., Zhang, M., Li, M., Liu, W., Yang, Z., & Yang, H. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00061 · Cited by 7
Hybrid of Supervised Learning and Optimization Algorithm for Optimal Detection of IoT Distributed Denial of Service Attacks
Farid, T., & Sirat, M. B. (2023) · International Journal of Innovative Computing · doi:10.11113/ijic.v13n1.329 · Cited by 7
An Assessment of Ensemble Voting Approaches, Random Forest, and Decision Tree Techniques in Detecting Distributed Denial of Service (DDoS) Attacks
Alsumaidaie, M. S. I., Alheeti, K. M. A., & Alaloosy, A. K. A. N. (2023) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.20.1.2 · Cited by 7
Detection Syn Flood and UDP Lag Attacks Based on Machine Learning Using AdaBoost
Syafiuddin, N. H., Mandala, S., & Cahyani, N. D. W. (2023) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa58501.2023.10276638 · Cited by 7
Windower: Feature Extraction for Real-Time DDoS Detection Using Machine Learning
Goldschmidt, P., & Kučera, J. (2024) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms59830.2024.10575699 · Cited by 6
A Collaborative Programmable LFA Defense Using Temporal Graph Learning in AIoT
Li, Y., Liu, Y., Xia, Y., Zhang, W., Quan, W., Kang, J., & Zhang, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3471615 · Cited by 6
F3: Fast and Flexible Network Telemetry with an FPGA coprocessor
Feng, W., Gao, J., Chen, X., Antichi, G., Basat, R. B., Shao, M. M., Zhang, Y., & Yu, M. (2024) · Proceedings of the ACM on Networking · doi:10.1145/3696397 · Cited by 6
DrLLM: Prompt-Enhanced Distributed Denial-of-Service Resistance Method with Large Language Models
Yin, Z., Liu, S., & Xu, G. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889735 · Cited by 6
Novel Actionable Counterfactual Explanations for Intrusion Detection Using Diffusion Models
Galwaduge, V., & Samarabandu, J. K. (2025) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp5030068 · Cited by 8
AFL-SecNet: An adaptive federated learning framework for secure and privacy-preserving network traffic analysis
Naz, A., Ullah, I., Uzair, M., Khokhar, M. F., Sabir, A., & Khan, R. U. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02155-w · Cited by 6
Fair client selection and encrypted aggregation: a federated learning framework for intrusion detection in resource-constrained networks
Akter, R., Bian, N., Ullah, I., Singh, S., Singh, A., & Iqbal, M. (2026) · Cluster Computing · doi:10.1007/s10586-025-05905-w · Cited by 6
AI infers DoS mitigation rules
Žádník, M., & Carasec, E. (2022) · Journal of Intelligent Information Systems · doi:10.1007/s10844-022-00728-2 · Cited by 6
A novel approach for flow analysis in software-based networks using L-moments theory
Galeano-Brajones, J., Chidean, M. I., Luna, F., & Carmona‐Murillo, J. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.01.022 · Cited by 6
Integrated Feature-Based Network Intrusion Detection System Using Incremental Feature Generation
Kim, T., & Pak, W. (2023) · Electronics · doi:10.3390/electronics12071657 · Cited by 6
Enhanced PSO Algorithm for Detecting DRDoS Attacks on LDAP Servers
(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.1031.61 · Cited by 6
GoGDDoS: A Multi-Classifier for DDoS Attacks Using Graph Neural Networks
Li, Y., Zhou, Z., Li, R., Shi, F., Jiang, G., & Liu, Q. (2023) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc58397.2023.10218316 · Cited by 7
Toward Efficient Neural Networks Through Predictor-Assisted NSGA-III for Anomaly Traffic Detection of IoT
Wang, X., He, M., Wang, J., & Wang, X. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3355433 · Cited by 6
ZF-DDOS: An Enhanced Statistical-Based DDoS Detection Approach using Integrated Z-Score and Fast-Entropy Measures
Hassan, M., Metwally, K., & Elshafey, M. A. (2024) · International Conference on Computing and Informatics (ICCI) · doi:10.1109/icci61671.2024.10485097 · Cited by 6
CIIR: an approach to handle class imbalance using a novel feature selection technique
Thiyam, B., & Dey, S. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02126-2 · Cited by 6
Devising a hybrid approach for near real-time DDoS detection in IoT
Pandey, N., & Mishra, P. K. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109448 · Cited by 6

Authors: Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A.
Affiliations: University of New Brunswick (CA)
ORCID: Sharafaldin, Iman
Lashkari, Arash Habibi
Hakak, Saqib
Ghorbani, Ali A.
Venue: 2019 International Carnahan Conference on Security Technology (ICCST)
Publisher: IEEE
Published: 2019-10
Pages: 1-8
DOI: 10.1109/ccst.2019.8888419
OpenAlex: W2982682021
Cited by: 1,275
References: 17
Citations by year: 2026: 176 · 2025: 251 · 2024: 271 · 2023: 242 · 2022: 167 · 2021: 124 · 2020: 44
FWCI: 72.24
Type: conference paper
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE International Carnahan Conference on Security Technology (ICCST) (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
Keywords: DDoS attacks, DDoS attack detection, CICDDoS2019, network flow features, feature weighting, real-time intrusion detection, malicious traffic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks