Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 25 references
Abstract
Denial-of-Service (DoS) attacks continue to threaten the availability and dependability of Internet of Things (IoT) networks. Because many IoT devices have limited processing power, memory, and built-in protection, they are frequently exploited to generate abnormal traffic that blocks legitimate communication. Signature-driven security tools are often unable to cope with changing attack behaviour. This paper presents a hybrid intrusion detection model that combines Artificial Neural Networks (ANNs) and Random Forest (RF) classification for DoS detection in IoT networks. The ANN component learns non-linear traffic representations, while the RF component performs the final ensemble-based classification. The model was implemented in MATLAB and evaluated using detection accuracy, false positive rate (FPR), and latency. The results show that the hybrid ANN-RF model reached 93% detection accuracy and 5% FPR, outperforming standalone ANN and RF models. The findings indicate that the proposed approach can support reliable and scalable real-time intrusion detection for IoT applications such as smart homes, healthcare systems, and industrial automation.
The rapid proliferation of Internet of Things (IoT) devices has fundamentally transformed global network infrastructure while simultaneously creating an expanding attack surface for advanced Distributed Denial of Service (DDoS) threats. IoT endpoints are inherently resource-constrained, making them vulnerable to exploitation as botnet nodes for large-scale DDoS campaigns. Conventional security mechanisms including statically configured firewalls and signature-based intrusion detection systems are insufficiently scalable and adaptive for heterogeneous IoT environments. This paper proposes a lightweight, hybrid Software-Defined Networking (SDN)-based framework for real-time DDoS detection and automated mitigation. The proposed system integrates Shannon entropy-based traffic anomaly detection at the data plane with a Random Forest (RF) classifier deployed at the Ryu SDN controller. Training and evaluation are performed on the CICDDoS2019 benchmark dataset, and end-to-end validation is conducted using Mininet network simulation. Experimental results demonstrate an average detection accuracy of 98.2%, a mean false positive rate (FPR) of 1.6%, a mean F1-score of 98.2%, and a mean mitigation time of 43 ms across four DDoS attack categories: UDP Flood, TCP SYN Flood, ICMP Flood, and HTTP Flood. The proposed approach achieves a favorable accuracy-overhead balance and outperforms state-of-the-art baselines in multiple evaluation dimensions.
Xodjayeva Mavluda Sabirovna, Sevinch Jovlieva, Bayjanov Furkat Bakhramovich et al.· 2026 International Conferenc...· 0 citations
The majority of assaults in heterogeneous networks are detected by intrusion detection systems (IDS). Cyberattack kinds that seriously harm networks are difficult for conventional IDSs to detect. The majority of existing solutions rely on deep learning models, which have a significant computational and energy overhead that limits their use in IoT environments with limited resources. A lightweight IDS based on ML is proposed in this research as a solution to this difficulty. Predicting the behavior of network traffic is achieved using ToN-IoT data and a tailored preprocessing pipeline. The voting-based ensemble classifier is built through the combination of models of RF and LightGBM to enhance the stability of the classification. The standard performance measures that are utilized to evaluate the proposed approach include accuracy, precision, recall, F1score, false alarm rates, and ROC analysis. The experimental findings indicate that RF achieve 99.81% accuracy, LGBM achieve 99.83%, and the ensemble model has a high accuracy of 99.99% with very low false alarms. Comparative evaluation with traditional ML and DL models demonstrates improved detection reliability with reduced computational overhead. These results prove that the suggested architecture is both computationally efficient and practically applicable to IoT settings with limited resources. However, direct hardware-level energy measurements are required to fully quantify the energy-saving characteristics of the proposed IDS.
Abhinay Kumar Reddy Seella, Rupesh Shirke, Vijay Kumar Kasuba et al.· International Conference on...· 0 citations
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.
P. P. Bairagi, Ashish Bagwari, Sailen Dutta Kalita et al.· international journal of eng...· 0 citations
This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks that integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while reducing false-positive predictions.
Ruthwik Palem, Likhith Reddy Peketi, Vanathi M et al.· Cureus Journal of Computer S...· 0 citations
An intelligent cyberattack detection system that applies machine learning and deep learning techniques to classify network traffic as either normal or malicious, and demonstrates the potential of machine learningbased intrusion detection systems in improving network security and supporting the protection of modern smart environments.
KADADHARAPU ANUPRIYA, Dr.S.SWATHI RAO· International Journal of Eng...· 0 citations
This study investigates the effectiveness of supervised machine learning techniques for detecting cyberattacks in IoT-based smart city networks using the TON_IoT dataset, finding that advanced ensemble learning combined with robust feature engineering provides a reliable and scalable solution for securing smart city IoT networks.
E. Okonta, Oluwaseun Bamgbose· ABC2: Journal of Architectur...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
