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

A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets

Moustafa, N. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.102994 · Cited by 747

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450

InSDN: A Novel SDN Intrusion Dataset

Elsayed, M. S., Le‐Khac, N., & Jurcut, A. D. (2020) · IEEE Access · doi:10.1109/access.2020.3022633 · Cited by 427

Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

Mishra, N., & Pandya, S. (2021) · IEEE Access · doi:10.1109/access.2021.3073408 · Cited by 447

A systematic literature review of methods and datasets for anomaly-based network intrusion detection

Yang, Z., Liu, X., Li, T., Wu, D., Wang, J., Zhao, Y., & Han, H. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102675 · Cited by 410

A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks

Ullah, I., & Mahmoud, Q. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47358-7_52 · Cited by 358

RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System

Wu, Z., Zhang, H., Wang, P., & Sun, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3182333 · Cited by 356

DDoSNet: A Deep-Learning Model for Detecting Network Attacks

Elsayed, M. S., Le‐Khac, N., Dev, S., & Jurcut, A. D. (2020) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom49955.2020.00072 · Cited by 316

FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks

Jia, Y., Zhong, F., Alrawais, A., Gong, B., & Cheng, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2993782 · Cited by 299

A Novel Deep Learning-Based Intrusion Detection System for IoT Networks

Awajan, A. W. (2023) · Computers · doi:10.3390/computers12020034 · Cited by 329

Detection of DDoS attacks with feed forward based deep neural network model

Çil, A. E., Yıldız, K., & Buldu, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114520 · Cited by 302

Ransomware: Recent advances, analysis, challenges and future research directions

Beaman, C., Barkworth, A., Akande, T. D., Hakak, S., & Khan, M. K. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102490 · Cited by 269

A comprehensive study of DDoS attacks over IoT network and their countermeasures

Kumari, P., & Jain, A. K. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103096 · Cited by 294

Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

Ferrag, M. A., Shu, L., Hamouda, D., & Choo, K. R. (2021) · Electronics · doi:10.3390/electronics10111257 · Cited by 277

Deep learning approaches for detecting DDoS attacks: a systematic review

Mittal, M., Kumar, K., & Behal, S. (2022) · Soft Computing · doi:10.1007/s00500-021-06608-1 · Cited by 241

A Secure Intrusion Detection Platform Using Blockchain and Radial Basis Function Neural Networks for Internet of Drones

Heidari, A., Navimipour, N. J., & Ünal, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3237661 · Cited by 243

Deep learning enabled intrusion detection system for Industrial IOT environment

Nandanwar, H., & Katarya, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123808 · Cited by 258

SDN-Based Architecture for Transport and Application Layer DDoS Attack Detection by Using Machine and Deep Learning

Yungaicela-Naula, N. M., Vargas‐Rosales, C., & Pérez-Díaz, J. A. (2021) · IEEE Access · doi:10.1109/access.2021.3101650 · Cited by 210

IoT DoS and DDoS Attack Detection using ResNet

Hussain, F. B., Abbas, S. G., Husnain, M., Fayyaz, U. U., Shahzad, F., & Shah, G. A. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318216 · Cited by 200

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Ahsan, M., Nygard, K. E., Gomes, R., Chowdhury, M. M., Rifat, N., & Connolly, J. F. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2030027 · Cited by 249

A new DDoS attacks intrusion detection model based on deep learning for cybersecurity

Akgün, D., Hızal, S., & Çavuşoğlu, Ü. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102748 · Cited by 193

AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

Wei, Y., Jang‐Jaccard, J., Sabrina, F., Singh, A. S. G., Xu, W. L., & Camtepe, S. A. (2021) · IEEE Access · doi:10.1109/access.2021.3123791 · Cited by 208

Anomaly-based intrusion detection system for IoT application

Bhavsar, M., Roy, K., Kelly, J. C., & Odeyomi, O. T. (2023) · Discover Internet of Things · doi:10.1007/s43926-023-00034-5 · Cited by 213

Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization

Yang, Y., Zheng, K., Wu, B., Yang, Y., & Wang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2977007 · Cited by 193

Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

Novaes, M. P., Carvalho, L. F., Lloret, J., & Proença, M. L. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.047 · Cited by 197

A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

Macas, M., Wu, C., & Fuertes, W. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109032 · Cited by 202

Deep learning for cyber threat detection in IoT networks: A review

Aldhaheri, A., Alwahedi, F., Ferrag, M. A., & Battah, A. A. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.09.003 · Cited by 206

Survey of Machine Learning based intrusion detection methods for Internet of Medical Things

Si-Ahmed, A., Al-Garadi, M. A., & Boustia, N. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110227 · Cited by 201

A GRU deep learning system against attacks in software defined networks

de Assis, M. V. O., Carvalho, L. F., Lloret, J., & Proença, M. L. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102942 · Cited by 185

DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)

Rehman, S. U., Khaliq, M., Imtiaz, S. I., Rasool, A., Shafiq, M., Javed, A. R., Jalil, Z., & Bashir, A. K. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.022 · Cited by 168

Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review

Ali, T. E., Chong, Y.-W., & Manickam, S. (2023) · Applied Sciences · doi:10.3390/app13053183 · Cited by 167

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Nie, L., Wu, Y., Wang, X., Guo, L., Wang, G., Gao, X., & Li, S. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3063538 · Cited by 166

Towards DDoS attack detection using deep learning approach

Aktar, S., & Nur, A. Y. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103251 · Cited by 183

Open RAN security: Challenges and opportunities

Liyanage, M., Braeken, A., Shahabuddin, S., & Ranaweera, P. S. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103621 · Cited by 156

Overview on Intrusion Detection Systems for Computers Networking Security

Diana, L., Dini, P., & Paolini, D. (2025) · Computers · doi:10.3390/computers14030087 · Cited by 211

Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment

Novaes, M. P., Carvalho, L. F., Lloret, J., & Proença, M. L. (2020) · IEEE Access · doi:10.1109/access.2020.2992044 · Cited by 161

Distributed Denial-of-Service (DDoS) Attacks and Defense Mechanisms in Various Web-Enabled Computing Platforms

Singh, A., & Gupta, B. B. (2022) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.297143 · Cited by 147

Near real-time security system applied to SDN environments in IoT networks using convolutional neural network

de Assis, M. V. O., Carvalho, L. F., Rodrigues, J. J. P. C., Lloret, J., & Proença, M. L. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106738 · Cited by 146

Efficient Detection of DDoS Attacks Using a Hybrid Deep Learning Model with Improved Feature Selection

Alghazzawi, D., Bamasag, O. O., Ullah, H., & Asghar, M. Z. (2021) · Applied Sciences · doi:10.3390/app112411634 · Cited by 143

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

Macas, M., Wu, C., & Fuertes, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122223 · Cited by 175

The Cross-Evaluation of Machine Learning-Based Network Intrusion Detection Systems

Apruzzese, G., Pajola, L., & Conti, M. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3157344 · Cited by 142

A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks

Hussain, F., Abbas, S. G., Pires, I. M., Tanveer, S., Fayyaz, U. U., García, N. M., Shah, G. A., & Shahzad, F. (2021) · IEEE Access · doi:10.1109/access.2021.3131014 · Cited by 141

Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network

Salmi, S., & Oughdir, L. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00692-w · Cited by 153

An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks

Zainudin, A., Ahakonye, L. A. C., Akter, R., Kim, D., & Lee, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3196942 · Cited by 134

FLAD: Adaptive Federated Learning for DDoS attack detection

Doriguzzi-Corin, R., & Siracusa, D. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103597 · Cited by 152

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

Khedr, W. I., Gouda, A. E., & Mohamed, E. R. (2023) · IEEE Access · doi:10.1109/access.2023.3260256 · Cited by 144

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

Javeed, D., Saeed, M. S., Ahmad, I., Adil, M., Kumar, P., & Islam, A. K. M. N. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.023 · Cited by 143

An Explainable and Resilient Intrusion Detection System for Industry 5.0

Javeed, D., Gao, T., Kumar, P., & Jolfaei, A. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3283704 · Cited by 127

Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review

Kheddar, H., Himeur, Y., & Awad, A. I. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103760 · Cited by 138

Analysis of Machine Learning Classifiers for Early Detection of DDoS Attacks on IoT Devices

Gaur, V., & Kumar, R. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-05947-3 · Cited by 124

Composite and efficient DDoS attack detection framework for B5G networks

Amaizu, G. C., Nwakanma, C. I., Bhardwaj, S., Lee, J. M., & Kim, D. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107871 · Cited by 128

A Comprehensive Survey on Security Challenges in Different Network Layers in Cloud Computing

Jangjou, M., & Sohrabi, M. K. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09708-9 · Cited by 122

Have You Been a Victim of COVID-19-Related Cyber Incidents? Survey, Taxonomy, and Mitigation Strategies

Hakak, S., Khan, W. Z., Imran, M. A., Choo, K. R., & Shoaib, M. (2020) · IEEE Access · doi:10.1109/access.2020.3006172 · Cited by 120

Optimization of RBF-SVM Kernel Using Grid Search Algorithm for DDoS Attack Detection in SDN-Based VANET

Anyanwu, G. O., Nwakanma, C. I., Lee, J., & Kim, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3199712 · Cited by 119

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

Adedeji, K. B., Abu-Mahfouz, A. M. I., & Kurien, A. M. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12040051 · Cited by 145

A New Method for Flow-Based Network Intrusion Detection Using the Inverse Potts Model

Pontes, C. F. T., de Souza, M. M. C., Gondim, J. J. C., Bishop, M., & Marotta, M. A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3075503 · Cited by 122

SoK

Du, X., Hargreaves, C. J., Sheppard, J., Anda, F., Sayakkara, A., Le‐Khac, N., & Scanlon, M. (2020) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3407023.3407068 · Cited by 130

SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT

Javeed, D., Gao, T., & Khan, M. T. (2021) · Electronics · doi:10.3390/electronics10080918 · Cited by 108

Bandwidth Control Mechanism and Extreme Gradient Boosting Algorithm for Protecting Software-Defined Networks Against DDoS Attacks

Alamri, H. A., & Thayananthan, V. (2020) · IEEE Access · doi:10.1109/access.2020.3033942 · Cited by 106

Apollon: A robust defense system against Adversarial Machine Learning attacks in Intrusion Detection Systems

Paya, A., Arroni, S., García‐Díaz, V., & Gómez, A. G. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103546 · Cited by 114

Distributed Denial of Service Attack Detection for the Internet of Things Using Hybrid Deep Learning Model

Ahmim, A., Maazouzi, F., Ahmim, M., Namane, S., & Dhaou, I. B. (2023) · IEEE Access · doi:10.1109/access.2023.3327620 · Cited by 122

A Survey of Public IoT Datasets for Network Security Research

Keersmaeker, F. D., Cao, Y., Ndonda, G. K., & Sadre, R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3288942 · Cited by 111

Machine Learning in Network Intrusion Detection: A Cross-Dataset Generalization Study

Cantone, M., Marrocco, C., & Bria, A. (2024) · IEEE Access · doi:10.1109/access.2024.3472907 · Cited by 151

A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions

Ceviz, Ö., Şen, S., & Sadioglu, P. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3515051 · Cited by 113

DDoS Detection using Deep Learning

Kumar, D., Pateriya, R. K., Gupta, R. K., Dehalwar, V., & Sharma, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.217 · Cited by 125

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

Abdi, N., Albaseer, A. M., & Abdallah, M. M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3354045 · Cited by 112

A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems

Mohammadian, H., Ghorbani, A. A., & Lashkari, A. H. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110173 · Cited by 95

DoS and DDoS Attack Detection Using Deep Learning and IDS

Shurman, M. M., Khrais, R. M., & Yateem, A. (2020) · The International Arab Journal of Information Technology · doi:10.34028/iajit/17/4a/10 · Cited by 92

Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic

Alzahrani, R. J., & Alzahrani, A. (2021) · Electronics · doi:10.3390/electronics10232919 · Cited by 92

An Efficient IDS Framework for DDoS Attacks in SDN Environment

Varghese, J. E., & Muniyal, B. (2021) · IEEE Access · doi:10.1109/access.2021.3078065 · Cited by 91

A Fine-Grained Access Control and Security Approach for Intelligent Vehicular Transport in 6G Communication System

Zhou, Z., Gaurav, A., Gupta, B. B., Lytras, M. D., & Razzak, I. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3106825 · Cited by 90

Federated learning in intrusion detection: advancements, applications, and future directions

Büyüktanır, B., Altınkaya, Ş., Baydoğmus, G. K., & Yıldız, K. (2025) · Cluster Computing · doi:10.1007/s10586-025-05325-w · Cited by 111

Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid

Khoei, T. T., Aissou, G., Hu, W. C., & Kaabouch, N. (2021) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit51626.2021.9491891 · Cited by 87

Detecting DDoS attacks using adversarial neural network

Mustapha, A., Khatoun, R., Zeadally, S., Chbib, F., Fadlallah, A., Fahs, W., & Attar, A. E. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103117 · Cited by 101

Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along With Random Forest Model

Bakro, M., Kumar, R. R., Husain, M., Ashraf, Z., Ali, A., Yaqoob, S. I., Ahmed, M. N., & Parveen, N. (2024) · IEEE Access · doi:10.1109/access.2024.3353055 · Cited by 87

A feature reduction based reflected and exploited DDoS attacks detection system

Kshirsagar, D. D., & Kumar, S. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-02907-5 · Cited by 86

Detecting Zero-Day Intrusion Attacks Using Semi-Supervised Machine Learning Approaches

Mbona, I., & Eloff, J. H. P. (2022) · IEEE Access · doi:10.1109/access.2022.3187116 · Cited by 85

A novel multi-scale network intrusion detection model with transformer

Xi, C., Wang, H., & Wang, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-74214-w · Cited by 101

SCADA intrusion detection scheme exploiting the fusion of modified decision tree and Chi-square feature selection

Ahakonye, L. A. C., Nwakanma, C. I., Lee, J., & Kim, D.-S. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100676 · Cited by 84

A Few-Shot-Based Model-Agnostic Meta-Learning for Intrusion Detection in Security of Internet of Things

Lu, C., Wang, X., Yang, A., Liu, Y., & Dong, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3283408 · Cited by 84

RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things

Li, J., Liu, M., Xue, Z., Fan, X., & He, X. (2020) · IEEE Access · doi:10.1109/access.2020.2974293 · Cited by 83

Intrusion Detection Scheme With Dimensionality Reduction in Next Generation Networks

Sood, K., Nosouhi, M. R., Nguyen, D. D. N., Jiang, F., Chowdhury, M., & Doss, R. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3233777 · Cited by 90

Unsupervised GAN-Based Intrusion Detection System Using Temporal Convolutional Networks and Self-Attention

de Araujo-Filho, P. F., Naili, M., Kaddoum, G., Fapi, E. T., & Zhu, Z. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3260039 · Cited by 82

IoT-based blockchain intrusion detection using optimized recurrent neural network

Saravanan, V., Madiajagan, M., Rafee, S. M., Sanju, P., Rehman, T. B., & Pattanaik, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16662-6 · Cited by 82

A generalized machine learning model for DDoS attacks detection using hybrid feature selection and hyperparameter tuning

Batchu, R. K., & Seetha, H. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108498 · Cited by 80

DeepSecure: Detection of Distributed Denial of Service Attacks on 5G Network Slicing—Deep Learning Approach

Kuadey, N. A., Maale, G. T., Kwantwi, T., Sun, G., & Liu, G. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3133479 · Cited by 80

RBF-SVM kernel-based model for detecting DDoS attacks in SDN integrated vehicular network

Anyanwu, G. O., Nwakanma, C. I., Lee, J. M., & Kim, D. (2022) · Ad Hoc Networks · doi:10.1016/j.adhoc.2022.103026 · Cited by 79

CANET: A hierarchical CNN-Attention model for Network Intrusion Detection

Ren, K., Yuan, S., Zhang, C., Shi, Y., & Huang, Z. (2022) · Computer Communications · doi:10.1016/j.comcom.2023.04.018 · Cited by 88

Quantum Computing and Machine Learning for Cybersecurity: Distributed Denial of Service (DDoS) Attack Detection on Smart Micro-Grid

Said, D. (2023) · Energies · doi:10.3390/en16083572 · Cited by 78

A hybrid machine learning approach for detecting unprecedented DDoS attacks

Najafimehr, M., Zarifzadeh, S., & Mostafavi, S. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-021-04253-x · Cited by 77

Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System

Husnain, M., Hayat, K., Cambiaso, E., Fayyaz, U. U., Mongelli, M., Akram, H., Abbas, S. G., & Shah, G. A. (2022) · Sensors · doi:10.3390/s22020567 · Cited by 76

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

Rizvi, S., Scanlon, M., McGibney, J., & Sheppard, J. (2022) · IEEE Access · doi:10.1109/access.2022.3214506 · Cited by 87

Detecting DDoS Threats Using Supervised Machine Learning for Traffic Classification in Software Defined Networking

Hirsi, A., Audah‏, L., Salh, A., Alhartomi, M. A., & Ahmed, S. (2024) · IEEE Access · doi:10.1109/access.2024.3486034 · Cited by 85

A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems

Khoei, T. T., & Kaabouch, N. (2023) · Information · doi:10.3390/info14020103 · Cited by 82

A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks

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