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DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/sec.1539openalex-snapshot
A survey and taxonomy of DoS attacks in cloud computing
inferredcites →https://doi.org/10.1007/978-3-319-26362-5_28openalex-snapshot
AmpPot: Monitoring and Defending Against Amplification DDoS Attacks
inferredcites →https://doi.org/10.1007/978-981-13-5953-8openalex-snapshot
Emerging Research in Computing, Information, Communication and Applications
inferredcites →https://doi.org/10.1007/978-981-15-0199-9_62openalex-snapshot
Anomaly-Based Intrusion Detection System Using Support Vector Machine
inferredcites →https://doi.org/10.1007/978-981-15-1922-2_13openalex-snapshot
A Multi-location Defence Scheme Against SSDP Reflection Attacks in the Internet of Things
inferredcites →https://doi.org/10.1007/978-981-15-8469-5_5openalex-snapshot
DDOS Detection Using Machine Learning Technique
inferredcites →https://doi.org/10.1007/s00521-016-2317-5openalex-snapshot
Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment
inferredcites →https://doi.org/10.1007/s10489-018-1141-2openalex-snapshot
Semi-supervised machine learning approach for DDoS detection
inferredcites →https://doi.org/10.1007/s11227-019-02945-zopenalex-snapshot
Distributed denial of service attacks and its defenses in IoT: a survey
inferredcites →https://doi.org/10.1007/s11227-020-03323-wopenalex-snapshot
The DDoS attacks detection through machine learning and statistical methods in SDN
inferredcites →https://doi.org/10.1007/s11227-021-04253-xopenalex-snapshot
A hybrid machine learning approach for detecting unprecedented DDoS attacks
inferredcites →https://doi.org/10.1007/s12083-017-0630-0openalex-snapshot
Survey on SDN based network intrusion detection system using machine learning approaches
inferredcites →https://doi.org/10.1007/s12652-020-02208-3openalex-snapshot
An anomaly based distributed detection system for DDoS attacks in Tier-2 ISP networks
inferredcites →https://doi.org/10.1007/s13042-021-01323-7openalex-snapshot
DDoS detection in 5G-enabled IoT networks using deep Kalman backpropagation neural network
inferredcites →https://doi.org/10.1016/b978-1-59749-197-6.x5000-7openalex-snapshot
The Official CHFI Study Guide (Exam 312-49)
inferredcites →https://doi.org/10.1016/j.comcom.2020.01.016openalex-snapshot
Deep learning and big data technologies for IoT security
inferredcites →https://doi.org/10.1016/j.comnet.2019.01.007openalex-snapshot
Introducing the SlowDrop Attack
inferredcites →https://doi.org/10.1016/j.comnet.2020.107223openalex-snapshot
Slow denial-of-service attacks on software defined networks
inferredcites →https://doi.org/10.1016/j.comnet.2020.107792openalex-snapshot
Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms
inferredcites →https://doi.org/10.1016/j.cose.2011.12.012openalex-snapshot
Toward developing a systematic approach to generate benchmark datasets for intrusion detection
inferredcites →https://doi.org/10.1016/j.cose.2019.101645openalex-snapshot
A dynamic MLP-based DDoS attack detection method using feature selection and feedback
inferredcites →https://doi.org/10.1016/j.cose.2023.103096openalex-snapshot
A comprehensive study of DDoS attacks over IoT network and their countermeasures
inferredcites →https://doi.org/10.1016/j.dcan.2022.09.010openalex-snapshot
Accurate threat hunting in industrial internet of things edge devices
inferredcites →https://doi.org/10.1016/j.jisa.2020.102474openalex-snapshot
On the educated selection of unsupervised algorithms via attacks and anomaly classes
inferredcites →https://doi.org/10.1016/j.patrec.2014.07.019openalex-snapshot
An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection
inferredcites →https://doi.org/10.1080/07366981.2018.1453101openalex-snapshot
DoS-DDoS: TAXONOMIES OF ATTACKS, COUNTERMEASURES, AND WELL-KNOWN DEFENSE MECHANISMS IN CLOUD ENVIRONMENT
inferredcites →https://doi.org/10.1093/comjnl/bxp078openalex-snapshot
Protection Against Denial of Service Attacks: A Survey
inferredcites →https://doi.org/10.1108/k.2010.06739hae.001openalex-snapshot
Introduction to Machine Learning
inferredcites →https://doi.org/10.1109/access.2020.2974293openalex-snapshot
RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things
inferredcites →https://doi.org/10.1109/access.2020.2976609openalex-snapshot
Low-Rate DoS Attacks, Detection, Defense, and Challenges: A Survey
inferredcites →https://doi.org/10.1109/access.2021.3062909openalex-snapshot
Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments
inferredcites →https://doi.org/10.1109/access.2022.3152577openalex-snapshot
A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks
inferredcites →https://doi.org/10.1109/access.2022.3165809openalex-snapshot
Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning
inferredcites →https://doi.org/10.1109/aicai.2019.8701238openalex-snapshot
Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques
inferredcites →https://doi.org/10.1109/cais.2019.8769454openalex-snapshot
Machine Learning Techniques to Detect DDoS Attacks on VANET System: A Survey
inferredcites →https://doi.org/10.1109/ccst.2019.8888419openalex-snapshot
Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
inferredcites →https://doi.org/10.1109/ccwc.2019.8666588openalex-snapshot
Deep Learning Models for Cyber Security in IoT Networks
inferredcites →https://doi.org/10.1109/cicybs.2014.7013367openalex-snapshot
Supervised learning to detect DDoS attacks
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1109/cloudtech.2017.8284731openalex-snapshot
DDoS attack detection using machine learning techniques in cloud computing environments
inferredcites →https://doi.org/10.1109/compcomm.2017.8322748openalex-snapshot
DDoS detection and prevention based on artificial intelligence techniques
inferredcites →https://doi.org/10.1109/comst.2015.2457491openalex-snapshot
Botnet in DDoS Attacks: Trends and Challenges
inferredcites →https://doi.org/10.1109/ecti-ncon.2019.8692243openalex-snapshot
A Machine Learning Approach for Detecting Distributed Denial of Service Attacks
inferredcites →https://doi.org/10.1109/gcce53005.2021.9622091openalex-snapshot
A Big Data and Deep Learning based Approach for DDoS Detection in Cloud Computing Environment
inferredcites →https://doi.org/10.1109/icc.2018.8422327openalex-snapshot
Machine-Learning-Based Online Distributed Denial-of-Service Attack Detection Using Spark Streaming
inferredcites →https://doi.org/10.1109/icca49400.2020.9022832openalex-snapshot
IoT Botnet Detection Mechanism Based on UDP Protocol
inferredcites →https://doi.org/10.1109/icccn.2018.8487445openalex-snapshot
Can Machine Learning Techniques Be Effectively Used in Real Networks against DDoS Attacks?
inferredcites →https://doi.org/10.1109/iccspa.2015.7081286openalex-snapshot
Analysis of UDP DDoS flood cyber attack and defense mechanisms on Web Server with Linux Ubuntu 13
inferredcites →https://doi.org/10.1109/icssit.2018.8748836openalex-snapshot
Detection of DDoS Attack on SDN Control plane using Hybrid Machine Learning Techniques
inferredcites →https://doi.org/10.1109/ismsit.2018.8567252openalex-snapshot
Anomaly Based Distributed Denial of Service Attack Detection and Prevention with Machine Learning
inferredcites →https://doi.org/10.1109/jiot.2020.3026023openalex-snapshot
A Quantitative Study of DDoS and E-DDoS Attacks on WiFi Smart Home Devices
inferredcites →https://doi.org/10.1109/jiot.2021.3090909openalex-snapshot
Boosting-Based DDoS Detection in Internet of Things Systems
inferredcites →https://doi.org/10.1109/jiot.2022.3196942openalex-snapshot
An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks
inferredcites →https://doi.org/10.1109/jiot.2023.3245153openalex-snapshot
Feature Engineering and Machine Learning Framework for DDoS Attack Detection in the Standardized Internet of Things
inferredcites →https://doi.org/10.1109/lsens.2018.2879990openalex-snapshot
Deep and Machine Learning Approaches for Anomaly-Based Intrusion Detection of Imbalanced Network Traffic
inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-snapshot
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
inferredcites →https://doi.org/10.1109/milcom.2018.8599738openalex-snapshot
Machine Learning Based DDos Detection Through NetFlow Analysis
inferredcites →https://doi.org/10.1109/ntict.2017.7976107openalex-snapshot
A modified DBSCAN clustering algorithm for proactive detection of DDoS attacks
inferredcites →https://doi.org/10.1109/scopes.2016.7955549openalex-snapshot
DDoS attacks, new DDoS taxonomy and mitigation solutions — A survey
inferredcites →https://doi.org/10.1109/services.2019.00051openalex-snapshot
DDoS Attacks Detection and Mitigation in SDN Using Machine Learning
inferredcites →https://doi.org/10.1109/spin.2019.8711780openalex-snapshot
On Security Threats of Botnets to Cyber Systems
inferredcites →https://doi.org/10.1109/spw.2018.00013openalex-snapshot
Machine Learning DDoS Detection for Consumer Internet of Things Devices
inferredcites →https://doi.org/10.1109/surv.2013.031413.00127openalex-snapshot
A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks
inferredcites →https://doi.org/10.1109/tdsc.2021.3074086openalex-snapshot
On Capturing DDoS Traffic Footprints on the Internet
inferredcites →https://doi.org/10.1109/tii.2023.3240586openalex-snapshot
ADAM: An Adaptive DDoS Attack Mitigation Scheme in Software-Defined Cyber-Physical System
inferredcites →https://doi.org/10.1109/tits.2022.3177772openalex-snapshot
Securing IoT Based Maritime Transportation System Through Entropy-Based Dual-Stack Machine Learning Framework
inferredcites →https://doi.org/10.1109/upcon.2017.8251130openalex-snapshot
Detection techniques of DDoS attacks: A survey
inferredcites →https://doi.org/10.1109/wimob.2018.8589104openalex-snapshot
A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network
inferredcites →https://doi.org/10.1145/3148055.3148077openalex-snapshot
An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics
inferredcites →https://doi.org/10.1145/997150.997156openalex-snapshot
A taxonomy of DDoS attack and DDoS defense mechanisms
inferredcites →https://doi.org/10.1155/2017/3691629openalex-snapshot
Low-Rate DDoS Attack Detection Using Expectation of Packet Size
inferredcites →https://doi.org/10.1186/s40537-018-0145-4openalex-snapshot
Intrusion detection model using machine learning algorithm on Big Data environment
inferredcites →https://doi.org/10.13052/jsn2445-9739.2017.009openalex-snapshot
Towards a Reliable Intrusion Detection Benchmark Dataset
inferredcites →https://doi.org/10.14569/ijacsa.2016.070159openalex-snapshot
Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
inferredcites →https://doi.org/10.14722/ndss.2014.23233openalex-snapshot
Amplification Hell: Revisiting Network Protocols for DDoS Abuse
inferredcites →https://doi.org/10.23721/105/1354086openalex-snapshot
DNS AMPL DDOS DEC2015 (07/22/2015 to 07/22/2015)
inferredcites →https://doi.org/10.23919/indiacom49435.2020.9083716openalex-snapshot
Detection of DDoS Attacks using Machine Learning Algorithms
inferredcites →https://doi.org/10.3390/app13031431openalex-snapshot
Low Rate DDoS Detection Using Weighted Federated Learning in SDN Control Plane in IoT Network
inferredcites →https://doi.org/10.3390/e17042367openalex-snapshot
An Entropy-Based Network Anomaly Detection Method
inferredcites →https://doi.org/10.3390/electronics9030444openalex-snapshot
Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark
inferredcites →https://doi.org/10.3390/fi12090147openalex-snapshot
Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey
inferredcites →https://doi.org/10.4018/978-1-5225-9715-5.ch070openalex-snapshot
Machine Learning and Cyber Security
inferredcites →https://doi.org/10.5220/0006639801080116openalex-snapshot
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
inferredcites →https://doi.org/10.5815/ijcnis.2013.08.01openalex-snapshot
Analysis of the SYN Flood DoS Attack
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-85923-6_27openalex-snapshot
SVM Enhanced Detection of Volume-Based Attacks in IoT Networks
inferred← citeshttps://doi.org/10.1007/978-3-032-08649-5_11openalex-snapshot
Applications of Positive Unlabeled Learning in Detection of DDoS Attacks
inferred← citeshttps://doi.org/10.1007/978-3-032-12930-7_22openalex-snapshot
Random Forest and Naive Bayes for DDoS Detection in the Cloud
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Detection of DDoS Attack Using Machine Learning Algorithms
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HTTP DDoS Attack Detection Technology Based on PF-RING and Gaussian Naive Bayes in Containerized Environment
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Review of Cyber-Physical System-Based Security Datasets for Learning-Based Intrusion Detection Systems
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Fortifying EV charging stations: AI-powered detection and mitigation of DDoS attacks using personalized Federated learning
inferred← citeshttps://doi.org/10.1007/s10207-024-00891-3openalex-snapshot
Collaborative intrusion detection using weighted ensemble averaging deep neural network for coordinated attack detection in heterogeneous network
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Intrusion Detection with Federated Learning and Conditional Generative Adversarial Network in Satellite-Terrestrial Integrated Networks
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Machine learning- and deep learning-based anomaly detection in firewalls: a survey
inferred← citeshttps://doi.org/10.1007/s11227-025-07236-4openalex-snapshot
DDos prevention in IoT networks by analyzing source-side inter-bot traffic using deep learning techniques
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Next-gen distributed denial-of-service detection and mitigation in software-defined networking using hybrid machine learning approach
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DDoSBERT: Fine-tuning variant text classification bidirectional encoder representations from transformers for DDoS detection
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Transformers model for DDoS attack detection: A survey
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Oversee effective DDoS detection based on dual frameworks
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Enhancing IoT Security via Federated Learning: A Comprehensive Approach to Intrusion Detection
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LDAP DDoS attack detection using SVM with different kernel functions
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HIDS-RPL: A Hybrid Deep Learning-Based Intrusion Detection System for RPL in Internet of Medical Things Network
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Adaptive DDoS Attack Detection: Entropy-Based Model With Dynamic Threshold and Suspicious IP Reevaluation
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Lightweight DDoS Attack Detection Using Bayesian Space-Time Correlation
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Method for Detecting Denial-of-Service Attacks Based on Statistical Analysis of the Temporal Dynamics of Network Traffic
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Defid: Detecting and Filtering Distributed Denial of Service Attack in Edge Computing Environment
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Machine Learning Approach for Detection of DDOS Attack in Cloud Environment
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