Evidence map›Paper›PMID 42552332›Full record

ArticleScientific reports2026

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

Ashraf Alyanbaawi, Aya Hassan, Marwa M Khashaba, Osama Elkomy, Ehab Rushdy, Ameer El-Sayed

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ashraf AlyanbaawiCollege of Computer Science and Engineering, Taibah University, Yanbu, 966144, Saudi Arabia.
Aya HassanDepartment of Information Technology, Faculty of Computers and Informatics, Damanhour University, Nubaria, 22773, Egypt.
Marwa M KhashabaDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44511, Egypt.
Osama ElkomyDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44511, Egypt.
Ehab RushdyDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44511, Egypt.
Ameer El-SayedDepartment of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44511, Egypt. amir.hasebat@gmail.com.ORCID http://orcid.org/0000-0003-3305-5844

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exponential growth of the Internet of Things (IoT) has magnified vulnerability to Distributed Denial-of-Service (DDoS) threats, particularly advanced TCP flooding attacks that exploit protocol weaknesses to disrupt services and overwhelm constrained devices. Existing Software-Defined Networking (SDN) defenses often suffer from scalability bottlenecks, high controller overhead, and limited adaptability to evolving traffic dynamics. To overcome these challenges, this paper proposes APATCP, a P4-enabled, multi-controller SD-IoT framework for real-time adaptive detection and mitigation of TCP flooding. APATCP integrates four coordinated modules: Adaptive Collaborative Intrusion Defense (ACID) for decentralized intelligence sharing; Dynamic Traffic Anomaly Mitigation (DTAM) for adaptive thresholding and flow isolation; Dynamic Threat-Adaptive Classifier (DTAC), which leverages 24 P4-extracted features and an adaptive weighted ensemble classifier for complex multi-type attack classification; and FlowGuard-AP, a context-aware mitigation engine with dynamic response strategies. Evaluation across three IoT-centric datasets-CICIoT2024, Edge-IIoTset, and ToN_IoT-demonstrates superior performance, with APATCP achieving 99.3-99.98% accuracy, 98.2-99.5% recall, F1-scores up to 99.2%, and false positive rates as low as 0.4%. In binary classification, it consistently outperformed baselines such as LightGBM (97.5-97.8%) and XGBoost (96.7-97.1%). In multi-class evaluations spanning eight adversarial scenarios-including burst floods, hybrid multi-vector campaigns, and stealthy microbursts-the framework maintained > 98% accuracy while remaining operationally stable. Live emulations confirmed resilience under > 100k packets/s attack rates, keeping CPU load below 30%, packet loss under 2%, and end-to-end latency below 100 ms, while restoring throughput to over 95% of baseline within seconds. These results establish APATCP as a scalable, adaptive, and resource-efficient framework that delivers high detection accuracy, low overhead, and robust mitigation, ensuring reliable defense for next-generation SD-IoT infrastructures against increasingly sophisticated TCP flooding threats.

Indexed as

Feature ExtractionMulti-Vector AttacksReal-Time MitigationScalabilitySD-IoTTCP Flood Detection

Identifiers

PMID42552332
PMCPMC13438613

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.