2026· International journal of research and innovation in social science· Vol 10, pp. 2275-2287· 0 citations
Abstract
Smart homes depend on interconnected sensors, cameras, routers, mobile applications, and cloud services. This connectivity improves automation and convenience, but it also expands the attack surface for Distributed Denial of Service (DDoS), Denial of Service (DoS), Mirai botnet, brute-force, spoofing, reconnaissance, and man-in-the-middle attacks. Traditional signature-based security is often insufficient because IoT devices are resource-constrained, heterogeneous, and frequently deployed with weak authentication or delayed firmware updates. This study evaluates supervised machine-learning classifiers for detecting cyberattacks in smart-home IoT network traffic using the CICIoT2023 dataset. Four algorithms, namely Random Forest, Decision Tree, k-Nearest Neighbour, and Support Vector Machine, were compared under 50:50, 70:30, and 80:20 train-test split settings. The models were evaluated using accuracy, precision, recall, and F1-score, with emphasis on DDoS, Mirai, and brute-force attack classes that are particularly relevant to smart-home environments. The findings show that tree-based classifiers are highly effective for IoT attack detection. Random Forest achieved the strongest overall accuracy and precision, while Decision Tree showed the most stable recall and F1-score for brute-force detection. The results indicate that Random Forest is suitable as a general-purpose smart-home IDS classifier, whereas Decision Tree or a hybrid ensemble strategy should be considered when missed brute-force attacks carry high operational risk. The paper contributes a clearer empirical comparison of lightweight supervised learning models and provides implementation guidance for smart-home intrusion detection systems.
A machine learning-based intrusion detection framework for multiclass classification of eight categories of IoT network attacks, namely Backdoor, MITM, DDoS, Ransomware, Password Attack, SQL Injection, Prob-attacks, and Normal traffic is designed and evaluated while minimizing false positives and false negatives.
Abhay Kumar Ray, Rupak Sharma, Sunil Kumar Pandey· International Journal of Wir...· 0 citations
A machine learning-driven intrusion detection system that aims to detect attacks on IoT devices and suggests that machine learning methods can be successfully used to differentiate between legitimate and malicious network behavior, which can be used as a viable solution to enhance the security and surveillance of IoT-based systems.
P. Praveen, K. Sridhar, B. Rao et al.· International journal of com...· 0 citations
The swift deployment of IoT-based smart home appliances has increased the attack surface for the smart environment and exposed it to attacks like botnet command-and-control communications, brute force attacks, denial-of-service attacks, and web-based attacks. Even though the Intrusion Detection Systems (IDSs) that use Machine Learning (ML) algorithms achieve a very high detection rate, most existing solutions focus on predictive performance but lack the ability to link detected attacks to the corresponding vulnerabilities in the Internet of Things (IoT). In this paper, an interpretable vulnerability-aware ML-based approach is presented to solve this problem through the integration of vulnerability classes of IoT, attack classes, network flow attributes, and ML features into one interpretation model. The proposed method uses leakage-aware pre-processing, addressing class imbalance, and compares Random Forest, XGBoost, and soft voting ensemble ML techniques using the CSE-CIC-IDS2018 dataset. Experimental outcomes indicate that XGBoost outperforms the other approaches in terms of performance, with an accuracy of 98.22%, precision of 99.69%, F1-score of 95.36%, ROC-AUC of 99.07%, and only 760 false alarms, which is approximately 19× lower number of false positives compared to Random Forest while keeping a similar level of detection efficiency. In addition to numeric assessment of the approach performance, the suggested model provides the vulnerability-oriented interpretation module that establishes mapping between prominent network flow attributes and possible IoT vulnerability states and attacks. Therefore, the integration of an interpretable vulnerability reasoning component into a high-performing tree-based machine learning algorithm proves to be effective for smart home IoT intrusion detection.
Huda Aldawghan, Mounir Frikha· International Journal of Adv...· 0 citations
Wireless Sensor Networks (WSNs) have become indispensable components of modern cyber-physical systems, supporting healthcare, industrial automation, agriculture, environmental monitoring, and military surveillance. However, their limited computational resources, open wireless communication channels, and unattended long-term deployments expose them to diverse cyberattacks, whilst conventional security mechanisms remain inadequate for such constrained environments. This study aims to develop and evaluate an efficient Machine Learning-based framework for real-time anomaly detection and multi-class attack classification in WSNs. The objective is to enhance network security, maintain detection accuracy, and enable practical deployment on low-resource sensor hardware. Seven supervised and unsupervised learning models, including Random Forest, Support Vector Machine, XGBoost, LSTM, Isolation Forest, Autoencoder, and k-Nearest Neighbour, were assessed using three benchmark datasets: NSL-KDD, UNSW-NB15, and WSN-DS. A tailored feature engineering process selected the most informative network, temporal, and protocol attributes, followed by Bayesianoptimized ensemble learning. The proposed soft-voting ensemble outperformed individual models and achieved highly accurate intrusion detection with strong classification consistency and minimal false alarm rates. Cross-dataset evaluation confirmed robust generalization, while hardware profiling demonstrated low memory usage and fast inference, making the framework feasible for real-time embedded WSN deployment. This research provides a scalable and practical intelligent security solution for WSN environments. Its originality lies in combining high detection performance, lightweight deployment capability, and adversarial robustness analysis, offering significant value for securing future smart infrastructure and resource-constrained IoT systems.
Qasim M. Zainel, Adnan Yousif Dawod, M. Abdulqader et al.· Journal of Cyber Security an...· 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
This study presents a Quantum Machine Learning (QML)-based Intrusion Detection framework that uses Quantum Support Vector Machines (QSVM) to improve detection accuracy, adaptability, and computational efficiency in conceptual IoT Cloud-Enabled Smart City environments.
Sukanya. Pondavakam, S. Singh, Himanshu Gupta· Discover Internet of Things· 0 citations
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