Open Access

ARTICLE

Renovated Random Attribute-Based Fennec Fox Optimized Deep Learning Framework in Low-Rate DoS Attack Detection in IoT

Prasanalakshmi Balaji^1,2, Sangita Babu³, Maode Ma⁴, Zhaoxi Fang², Syarifah Bahiyah Rahayu^5,6,*, Mariyam Aysha Bivi¹, Mahaveerakannan Renganathan⁷

1 Department of Computer Science, College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
2 Institute of Artificial Intelligence, Shaoxing University, Shaoxing, 312000, China
3 Department of Computer Science, King Khalid University, Rijal Alma, 61421, Saudi Arabia
4 KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, 122104, Qatar
5 Faculty of Defence Science and Technology, National Defence University of Malaysia (UPNM), Kuala Lumpur, 57000, Malaysia
6 Cyber Security & Digital Industrial Revolution Centre, National Defense University of Malaysia (UPNM), Kuala Lumpur, 57000, Malaysia
7 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602117, India

* Corresponding Author: Syarifah Bahiyah Rahayu. Email:

(This article belongs to the Special Issue: Fortifying the Foundations: IoT Intrusion Detection Systems in Cloud-Edge-End Architecture)

Computers, Materials & Continua 2025, 84(3), 5831-5858. https://doi.org/10.32604/cmc.2025.065260

Received 08 March 2025; Accepted 24 June 2025; Issue published 30 July 2025

Abstract

The rapid progression of the Internet of Things (IoT) technology enables its application across various sectors. However, IoT devices typically acquire inadequate computing power and user interfaces, making them susceptible to security threats. One significant risk to cloud networks is Distributed Denial-of-Service (DoS) attacks, where attackers aim to overcome a target system with excessive data and requests. Among these, low-rate DoS (LR-DoS) attacks present a particular challenge to detection. By sending bursts of attacks at irregular intervals, LR-DoS significantly degrades the targeted system’s Quality of Service (QoS). The low-rate nature of these attacks confuses their detection, as they frequently trigger congestion control mechanisms, leading to significant instability in IoT systems. Therefore, to detect the LR-DoS attack, an innovative deep-learning model has been developed for this research work. The standard dataset is utilized to collect the required data. Further, the deep feature extraction process is executed using the Residual Autoencoder with Sparse Attention (ResAE-SA), which helps derive the significant feature required for detection. Ultimately, the Adaptive Dense Recurrent Neural Network (ADRNN) is implemented to detect LR-DoS effectively. To enhance the detection process, the parameters present in the ADRNN are optimized using the Renovated Random Attribute-based Fennec Fox Optimization (RRA-FFA). The proposed optimization reduces the False Discovery Rate and False Positive Rate, maximizing the Matthews Correlation Coefficient from 23, 70.8, 76.2, 84.28 in Dataset 1 and 70.28, 73.8, 74.1, 82.6 in Dataset 2 on EPC-ADRNN, DPO-ADRNN, GTO-ADRNN, FFA-ADRNN respectively to 95.8 on Dataset 1 and 91.7 on Dataset 2 in proposed model. At batch size 4, the accuracy of the designed RRA-FFA-ADRNN model progressed by 9.2% to GTO-ADRNN, 11.6% to EFC-ADRNN, 10.9% to DPO-ADRNN, and 4% to FFA-ADRNN for Dataset 1. The accuracy of the proposed RRA-FFA-ADRNN is boosted by 12.9%, 9.09%, 11.6%, and 10.9% over FFCNN, SVM, RNN, and DRNN, using Dataset 2, showing a better improvement in accuracy with that of the proposed RRA-FFA-ADRNN model with 95.7% using Dataset 1 and 94.1% with Dataset 2, which is better than the existing baseline models.

---

Keywords

---