Aug 2026· International Journal of Wireless and Microwave Technologies· 0 citations
TL;DR
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.
Abstract
With the rapid adoption of smart home solutions and related technologies, edge computing has emerged as a key enabler by offering low-latency data processing, increased efficiency and improved scalability. However, this integration in IoT systems introduces complex security challenges in smart home edge environments, increasingly susceptible to cyber threats such as denial-of-service (DoS), malware injection, passive surveillance, and unauthorized access. This paper investigates intelligent intrusion detection and attack classification strategies specifically designed for smart home edge systems. Using the comprehensive ML-EdgeIIoT dataset, this study designs and evaluates 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 while minimizing false positives and false negatives. The framework incorporates data cleaning, correlation- and feature importance-based feature selection, hyperparameter optimization using gridsearchCV, model training, and ensemble learning. A set of machine learning models comprising Artificial Neural Network, Balanced Random Forest, K-Nearest Neighbours, Random Forest, and Logistic Regression was implemented and comparatively evaluated. Two ensemble techniques were subsequently developed using the three best-performing classifiers: (1) a stacking ensemble with Logistic Regression as the meta-learner and (2) a Top-3 majority voting ensemble. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC. Robustness and generalization of the individual machine learning models were assessed through stratified 10-fold cross-validation for the three best-performing classifiers. The Top-3 voting ensemble subsequently achieved the highest performance on the independent test set, with accuracy of 99.24%, average precision of 98.75%, recall of 99.00%, and an F1-score of 99.00% for all attack classes, while reducing misclassification compared with individual classifiers. The findings of this study significantly enhance the understanding of smart home edge computing security, which will pave the way for more robust and intelligent threat detection frameworks.
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
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
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.
Nurin Abyana Balqis Jailani, Haniza Nahar, N. M. Salleh et al.· International journal of res...· 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
As interconnected devices increasingly transmit personal and sensitive data, security attacks are becoming more sophisticated and prevalent, highlighting the critical need for effective security solutions in Internet of Things (IoT) environments. An automated Network Intrusion Detection (NID) system plays a vital role in notifying system administrators of security breaches, acting as an efficient tool for protecting IoT networks from various threats. This study utilizes the UNSW-NB 15 dataset to enhance intrusion detection accuracy by addressing performance challenges and class imbalances within the data. We employ a combination of feature selection techniques, including Filter Method, Wrapper Method, and an Embedded approach using Lasso and Random Forest with Recursive Feature Elimination (RFE), alongside Pearson Correlation Coefficient (PCC). To tackle class imbalance, we apply the Synthetic Minority Over-sampling Technique (SOMTE). Various algorithms are implemented, including Random Forest, Decision Tree, AdaBoost, Bernoulli Naive Bayes, K-Nearest Neighbors, and Logistic Regression. Notably, the Stacking Classifier, which combines Boosted Decision Trees, Bagging with Random Forest, and LightGBM, demonstrates high performance in accurately detecting intrusions, significantly improving detection rates and reducing false alarms.
Rangu Shashidhar, M. Raju· International Journal of Eng...· 1 citation
The explosion of Internet of Things (IoT) deployment over the past decade has served as a foundational pillar for global digital transformation. However, the rapid expanding attack surface of IoT architectures often suffers from compromised security paradigms, rendering smart environments highly vulnerable to malicious exploitations. While traditional Intrusion Detection Systems (IDS) mitigate network threats, conventional datasets lack the granular, protocol-specific traffic anomalies characteristic of IoT environments. This research addresses this gap by developing an automated machine learning framework designed to differentiate reconnaissance and anomalous activities from baseline behaviors within smart home IoT infrastructures. Utilizing the Hacking and Countermeasure Research Lab (HCRL) dataset, we evaluate and contrast the efficacy of Naïve Bayes (NB) and Support Vector Machine (SVM) algorithms across varying data-split ratios. Experimental results indicate that while Naïve Bayes offers competitive computational recall in localized environments, the SVM classifier demonstrates superior robustness, achieving an accuracy threshold approaching 99.99% in isolating low-frequency reconnaissance attacks.
