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P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture

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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/ett.3271openalex-snapshot

Efficient caching through stateful SDN in named data networking

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DroPPPP: A P4 Approach to Mitigating DoS Attacks in SDN

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Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

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Smart home security: challenges, issues and solutions at different IoT layers

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A review on P4-Programmable data planes: Architecture, research efforts, and future directions

inferredcites →https://doi.org/10.1016/j.comnet.2020.107597openalex-snapshot

A survey on stateful data plane in software defined networks

inferredcites →https://doi.org/10.1016/j.compeleceng.2020.106738openalex-snapshot

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

inferredcites →https://doi.org/10.1016/j.compeleceng.2022.107716openalex-snapshot

Denial of service attack detection and mitigation for internet of things using looking-back-enabled machine learning techniques

inferredcites →https://doi.org/10.1016/j.compeleceng.2022.107757openalex-snapshot

Ascertain the efficient machine learning approach to detect different ARP attacks

inferredcites →https://doi.org/10.1016/j.compeleceng.2022.108034openalex-snapshot

DDoS attack detection in Internet of Things using recurrent neural network

inferredcites →https://doi.org/10.1016/j.cose.2021.102494openalex-snapshot

The internet of things security: A survey encompassing unexplored areas and new insights

inferredcites →https://doi.org/10.1016/j.cose.2022.102604openalex-snapshot

A hybrid method of entropy and SSAE-SVM based DDoS detection and mitigation mechanism in SDN

inferredcites →https://doi.org/10.1016/j.engappai.2022.105059openalex-snapshot

A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT

inferredcites →https://doi.org/10.1016/j.jnca.2021.103160openalex-snapshot

A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique

inferredcites →https://doi.org/10.1016/j.jnca.2022.103444openalex-snapshot

A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning

inferredcites →https://doi.org/10.1016/j.jnca.2022.103560openalex-snapshot

An optimized ensemble prediction model using AutoML based on soft voting classifier for network intrusion detection

inferredcites →https://doi.org/10.1016/j.procs.2019.11.004openalex-snapshot

New context-based node acceptance CBNA framework for MitM detection in SDN Architecture

inferredcites →https://doi.org/10.1016/j.scs.2021.102994openalex-snapshot

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

inferredcites →https://doi.org/10.1109/access.2018.2831284openalex-snapshot

A DDoS Attack Detection and Mitigation With Software-Defined Internet of Things Framework

inferredcites →https://doi.org/10.1109/access.2020.3021435openalex-snapshot

A New Framework for DDoS Attack Detection and Defense in SDN Environment

inferredcites →https://doi.org/10.1109/access.2021.3094024openalex-snapshot

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

inferredcites →https://doi.org/10.1109/access.2021.3120626openalex-snapshot

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Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning

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Dynamic Routing and Failure Recovery Approaches for Efficient Resource Utilization in OpenFlow-SDN: A Survey

inferredcites →https://doi.org/10.1109/access.2023.3260256openalex-snapshot

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

inferredcites →https://doi.org/10.1109/comst.2018.2866942openalex-snapshot

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

inferredcites →https://doi.org/10.1109/comst.2023.3265984openalex-snapshot

Empowering Network Security With Programmable Switches: A Comprehensive Survey

inferredcites →https://doi.org/10.1109/cscloud/edgecom.2019.00020openalex-snapshot

Low-Rate DoS Attack Detection Using PSD Based Entropy and Machine Learning

inferredcites →https://doi.org/10.1109/icce.2018.8326334openalex-snapshot

CMD: A convincing mechanism for MITM detection in SDN

inferredcites →https://doi.org/10.1109/jiot.2020.2973176openalex-snapshot

Learning-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture

inferredcites →https://doi.org/10.1109/jiot.2021.3077803openalex-snapshot

Federated-Learning-Based Anomaly Detection for IoT Security Attacks

inferredcites →https://doi.org/10.1109/jiot.2021.3102056openalex-snapshot

X-IIoTID: A Connectivity-Agnostic and Device-Agnostic Intrusion Data Set for Industrial Internet of Things

inferredcites →https://doi.org/10.1109/mnet.021.1900599openalex-snapshot

Enhancing 5G SDN/NFV Edge with P4 Data Plane Programmability

inferredcites →https://doi.org/10.1109/qrs-c.2019.00090openalex-snapshot

DDoS Intrusion Detection Through Machine Learning Ensemble

inferredcites →https://doi.org/10.1109/tifs.2017.2765506openalex-snapshot

Packet Injection Attack and Its Defense in Software-Defined Networks

inferredcites →https://doi.org/10.1109/tst.2016.7399280openalex-snapshot

Bayes-based ARP attack detection algorithm for cloud centers

inferredcites →https://doi.org/10.1109/wcnc.2019.8885926openalex-snapshot

Distributed SIP DDoS Defense with P4

inferredcites →https://doi.org/10.1109/wincom.2016.7777224openalex-snapshot

Deep learning approach for Network Intrusion Detection in Software Defined Networking

inferredcites →https://doi.org/10.14722/ndss.2015.23283openalex-snapshot

Poisoning Network Visibility in Software-Defined Networks: New Attacks and Countermeasures

inferredcites →https://doi.org/10.23919/acc50511.2021.9483322openalex-snapshot

Active Attack Detection and Control in Constrained Cyber-Physical Systems Under Prevented Actuation Attack

inferredcites →https://doi.org/10.32604/cmc.2022.024496openalex-snapshot

Anomaly Detection for Internet of Things Cyberattacks

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An Intrusion Detection System Based on Genetic Algorithm for Software-Defined Networks

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Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment

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inferredcites →https://doi.org/10.3390/math11092145openalex-snapshot

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New Detection Mechanism for Distributed Denial of Service Attacks in Software Defined Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-04725-0_19openalex-snapshot

Analysis of Mitigation Methods for ARP Attacks in Local Area Networks: A Systematic Review

inferred← citeshttps://doi.org/10.1007/s00521-026-12040-zopenalex-snapshot

ARP spoofing detection using coral reefs optimization algorithm and MLP network

inferred← citeshttps://doi.org/10.1007/s10115-024-02219-yopenalex-snapshot

ARP spoofing detection using machine learning classifiers: an experimental study

inferred← citeshttps://doi.org/10.1007/s12083-025-02151-0openalex-snapshot

AI-driven intrusion detection and mitigation framework for software-defined IoT networks

inferred← citeshttps://doi.org/10.1007/s44227-024-00049-6openalex-snapshot

Beyond the Basics: An In-Depth Analysis and Multidimensional Survey of Programmable Switch in Software-Defined Networking

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109484openalex-snapshot

MP-GUARD: A novel multi-pronged intrusion detection and mitigation framework for scalable SD-IoT networks using cooperative monitoring, ensemble learning, and new P4-extracted feature set

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110543openalex-snapshot

Deception and cloud integration: A multi-layered approach for DDoS detection, mitigation, and attack surface minimization in SD-IoT networks

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110894openalex-snapshot

CATOR: A confidence-adaptive dual-plane in-switch orchestrated resilience framework for multi-vector DDoS defense in software-defined IoT

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2025.110929openalex-snapshot

DiMCA: A novel P4-powered framework using machine learning for adaptive defense against combined DDoS and ARP spoofing attacks in SD-IoT networks

inferred← citeshttps://doi.org/10.1016/j.cose.2025.104349openalex-snapshot

CO-STOP: A robust P4-powered adaptive framework for comprehensive detection and mitigation of coordinated and multi-faceted attacks in SD-IoT networks

inferred← citeshttps://doi.org/10.1016/j.iot.2024.101231openalex-snapshot

MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

inferred← citeshttps://doi.org/10.1016/j.iot.2024.101432openalex-snapshot

LBTMA: An integrated P4-enabled framework for optimized traffic management in SD-IoT networks

inferred← citeshttps://doi.org/10.1038/s41598-025-28830-9openalex-snapshot

SFARP: a multi-layered real-time security framework for hybrid ARP and DDoS attack defense in SD-IoT networks

inferred← citeshttps://doi.org/10.1038/s41598-025-31216-6openalex-snapshot

MC-LBTO: secure and resilient state-aware multi-controller framework with adaptive load balancing for SD-IoT performance optimization

inferred← citeshttps://doi.org/10.1038/s41598-026-62233-8openalex-snapshot

APATCP: programmable multi-controller framework for real-time detection and mitigation of complex TCP flooding attacks in SD-IoT networks

inferred← citeshttps://doi.org/10.1109/access.2023.3347332openalex-snapshot

P4 Cybersecurity Solutions: Taxonomy and Open Challenges

inferred← citeshttps://doi.org/10.1109/fllm67465.2025.11391009openalex-snapshot

A Real-Time Framework for Prompt Injection Attacks Detection on Cloud-Hosted Large Language Models

inferred← citeshttps://doi.org/10.1109/gtss68743.2026.11679857openalex-snapshot

A Contemporary Review of DDoS Detection in IoT Systems

inferred← citeshttps://doi.org/10.1109/iccpct61902.2024.10672984openalex-snapshot

A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis

inferred← citeshttps://doi.org/10.1109/iceem66692.2025.11225082openalex-snapshot

SENATE: SDN-Empowered Network Anomaly Tracker using Entropy for IoT DDoS Detection

inferred← citeshttps://doi.org/10.3389/fcomp.2024.1477501openalex-snapshot

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

inferred← citeshttps://doi.org/10.3390/data10110173openalex-snapshot

Method for Detecting Low-Intensity DDoS Attacks Based on a Combined Neural Network and Its Application in Law Enforcement Activities

inferred← citeshttps://doi.org/10.3390/fi18080385openalex-snapshot

Explainable AI for Securing Perception-Layer Sensor Data in IoT Environmental Danger Detection Systems

inferred← citeshttps://doi.org/10.4018/ijcac.334214openalex-snapshot

Using Supervised Learning to Detect Command and Control Attacks in IoT

inferred← citeshttps://doi.org/10.48084/etasr.10852openalex-snapshot

An IDS-based Adaptive Neural Fuzzy Inference System (ANFIS) for IoBT Security Utilizing Particle Swarm Optimization

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