Aug 2026· international journal of engineering trends and technology· 0 citations
TL;DR
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.
Abstract
Critical vulnerabilities have been made public by the fast expansion of Internet of Things (IoT) devices, making these networks easy target for cyber-attacks. While security solutions based on Machine Learning (ML) have shown potential, they often encounter issues including slow detection times, scaling issues, and a lack of generalisability when it comes to diverse IoT devices. To work with these issues, this paper introduces an innovative ML-based security paradigm. The proposed framework improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism. The new paradigm maximizes detection accuracy and computational efficiency. This is achieved through real-time dynamic adjustment of feature selection against network conditions, rather than traditional hybrid approaches. Furthermore, a lightweight and scalable detection method fit for execution on low-resource IoT devices is offered. It is apt for several IoT environments. The proposed framework beats several current models by 15% in accuracy, 25% in the reduction of false positive rates, and 30% in detection times, according to experimental tests carried out on numerous IoT datasets.
The rapid expansion of the Internet of Things (IoT) has raised additional concerns about security, and there was a major risk of Distributed Denial-of-Service (DDoS) attacks because the IoT devices have limited computation, memory, and energy capabilities. Traditional intrusion detection methods, which are at times contrived to support a high capacity, are incompetent at these limitations, delaying detections, having too many false alarms, and also compromising the system performance. This study offers a resource-efficient, adaptive machine learning system that was suitable to be used in the operation of DDoS attacks in resource-confined IoT settings. The technique combines the hybrid feature selection algorithms based on mutual information gain and recursive feature elimination to construct a more compact and high-utility feature set together with the optimization of the lightweight classifiers, including stochastic gradient descent and shallow decision trees. The concept drift was solved by an online incremental learning mechanism that guarantees long-term trend detection over time against changing patterns of attacks. The evaluation of the benchmark datasets (CICDDoS2019, BoT-IoT, TON_IoT) using experimental evaluation on a heterogeneous testbed IoT and assessing both security metrics and resource efficiency was researched. The model suggested had a precision of 0.973, a recall of 0.959, an F1-score of 0.966, and an average decrease of malicious traffic by 93 percent at the expense of legitimacy throughput. Latency was decreased to 2.6 seconds when detecting high-intensity attacks, and the CPU and memory usage continued to be less than 35 percent and 70 percent of the device capacity, respectively. A better result in terms of accuracy, response time, false positive rates, and not using resource budgets was witnessed when compared to baseline models through comparative analysis. The results verify the framework's ability to provide low latency and correct DDoS mitigation directly on the IoT devices, which can be considered a feasible solution to achieve resilience improvement of critical IoT deployments in health care, industrial automation, and smart cities.
Selvi. T, Jayaganesh. J· International journal of com...· 0 citations
The rapid growth of Internet of Things (IoT) devices has significantly improved connectivity across smart environments. However, the resource-constrained nature of IoT devices and their limited built-in security mechanisms make them highly vulnerable to evolving cyberattacks. Traditional intrusion detection systems relying on signature-based or static rule sets are often ineffective against previously unseen attacks. This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks. The framework 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. The ensemble outputs are combined using a Logistic Regression meta-classifier to generate the final threat score. Experimental evaluation using the UNSW-NB15 dataset demonstrates that Extreme Gradient Boosting achieved the highest individual Receiver Operating Characteristic-Area Under the Curve score of 0.91, while the proposed framework further enhances detection robustness through ensemble learning and automated prevention. A Flask-based monitoring dashboard provides real-time visualization of detection results, blocked IP addresses, alerts, and system performance.
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
The rapid growth of the Internet of Things (IoT) has intensified cybersecurity risks while exposing the limitations of traditional security solutions in resource-constrained environments. Intrusion detection in IoT systems, therefore, requires reliable, real-time decision-making with minimal computational overhead. This paper presents a lightweight IoT security decision framework that combines entropy-guided feature selection with an adaptive ensemble-based intrusion detection strategy. The proposed approach employs an entropy–correlation (EnCor) feature selection pipeline to construct a compact and informative feature subset, reducing complexity while preserving discriminative security characteristics. Detection decisions are generated using a soft voting ensemble of complementary machine learning classifiers, supported by an adaptive fallback mechanism to improve reliability under diverse attack scenarios. The framework is specifically designed for edge- and gateway-level IoT deployment, avoiding the high latency and computational demands associated with deep learning and blockchain-based solutions. Experimental evaluation on the TON_IoT and CICIoT2023 datasets demonstrates high detection accuracy with low inference latency and reduced memory consumption. The results confirm that effective intrusion detection can be achieved without compromising practical deployment feasibility. Overall, the proposed framework establishes intrusion detection as an efficient and deployable security decision layer for real-world IoT environments.
Saif Wali Ali Alsudani, M. Feizi-Derakhshi· International Journal of Ele...· 0 citations
Among the industries that have been revolutionized by the new development of the Internet of Things (IoT) are healthcare, industry, and smart cities but at the same time, it has brought great security threats, notably Distributed Denial of Service (DDoS) attacks. Conventional deep learning intrusion detection systems offer good accuracy in detection, but can be costly in computation and do not fit well in the constrained resource environment of IoT. This review takes a critical look at the newer IoT-based DDoS detection methods with the attention to Federated Learning (FL), Explainable Artificial Intelligence (XAI), and lightweight machine learning (ML) methods. In a comparison of recent literature, it has been found that lightweight ML models including Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) reach a detection accuracy of around 94-96% with low computational and communication overhead, which makes them an appropriate choice in the deployment of edge-based IoT. Conversely, the FL-integrated deep learning methods, such as FL-XAI frameworks and FL-LSTM models, achieve better detection accuracy (99-99.8) and better privacy protection, but pose serious training complexities, communication, and resource constraints on the devices. As a middle ground to scalability, interpretability, and detection accuracy (97-98%), hybrid models like FL-Autoencoders and FL-CNNs exist. Notwithstanding such progress, the majority of investigations are based on simulated data and do not provide the validity of IoT implementation in the real world, which defines a significant research gap. In general, these results indicate that lightweight ML models are the most viable choice in real-time IoT setups, and federated and explainable frameworks are promising the scalability, privacy-aware, and explainable IoT security systems, as long as their computational efficiency and applicability in the real world are further enhanced.
Binay Sharma, Bipasha Regmi· Devkota Journal of Interdisc...· 0 citations
Traditional Internet of Things (IoT) security solutions often rely on heavy cloud-based or gateway-class infrastructure, which is frequently unsuitable for resource-limited hardware due to latency, power, and memory constraints. This paper proposed a resource-aware behavioral Intrusion Detection System (IDS) designed for highly constrained IoT devices. To address these challenges, the proposed system implements real-time application-layer monitoring on an ESP32 Microcontroller Unit (MCU) and utilizes an offline-trained logistic regression model for autonomous, on-device inference. The detection mechanism extracts behavioral features, such as request rates, failed authentication attempts, and invalid resource access within sliding time windows to estimate attack probabilities. Experimental evaluations under controlled scenarios involving flood, brute force, and scan attacks demonstrate that the system achieves high accuracy, precision, and recall. These findings indicate that effective cyber intrusion detection and local mitigation can be successfully executed directly on a single MCU while preserving stable runtime performance and minimal memory usage. Finally, this paper highlights the need for further optimizations to improve robustness and scalability.
Sofyan Bisher, Anas Fawaza, Tarek Mawed et al.· International Conference on...· 0 citations