As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVMs), and Deep Convolutional Neural Networks (DCNNs). Identifying optimal anomaly detection models for IoT anomaly detection is challenging due to diverse IoT applications and dynamic IoT networking environments. It is of vital importance to evaluate ML-powered anomaly detection models using multiple datasets collected from different environments. We utilized three reputable datasets to benchmark the aforementioned machine learning methods, namely, IoT-23, NSL-KDD, and TON_IoT. Our results show that XGBoost outperformed both the SVM and DCNN, achieving accuracies of up to 99.98%. Moreover, XGBoost proved to be the most computationally efficient method; the model performed 717.75 times faster than the SVM and significantly faster than the DCNN in terms of training times. The research results have been further confirmed by using our real-world IoT data collected from an IoT testbed consisting of physical devices that we recently built.

The Internet of Things (IoT) has revolutionized industries by enabling seamless data exchange between billions of connected devices. However, the rapid proliferation of IoT devices has introduced significant security challenges, as many of these devices lack robust protection against cyber threats such as data breaches and denial-of-service attacks. Addressing these vulnerabilities is critical to maintaining the integrity and trust of IoT ecosystems. Traditional cybersecurity solutions often fail in dynamic, heterogeneous IoT environments due to device diversity, limited computational resources, and inconsistent communication protocols, which hinder the deployment of uniform and scalable security mechanisms. Moreover, there is a notable lack of realistic, high-quality datasets for training and evaluating machine learning (ML) models for IoT security, limiting their effectiveness in detecting complex and evolving threats. This paper presents the development and implementation of a novel physical smart office/home testbed designed to evaluate ML algorithms for detecting and mitigating IoT security vulnerabilities. The testbed replicates a real-world office environment, integrating a variety of IoT devices, such as different types of sensors, cameras, smart plugs, and workstations, within a network generating authentic traffic patterns. By simulating diverse attack scenarios including unauthorized access and network intrusions, the testbed provides a controlled platform to train, test, and validate ML-based anomaly detection systems. Experimental results show that the XGBoost model achieved a balanced accuracy of up to 99.977% on testbed-generated data, comparable to 99.985% on the benchmark IoT-23 dataset. Notably, the SVM model achieved up to 96.71% accuracy using our testbed data, outperforming its results on IoT-23, which peaked at 94.572%. The findings demonstrate the testbed’s effectiveness in enabling realistic security evaluations and ability to generate real-world datasets, highlighting its potential as a valuable tool for advancing IoT security research. This work contributes to the development of more resilient and adaptive security frameworks, offering valuable insights for safeguarding critical IoT infrastructures against evolving threats.

Image segmentation plays a key role in image analysis processes. The operations performed on a segmented image tend to affect it differently than if they were performed on the original image; therefore, segmenting an image can show radically different results from the original image and successfully doing so can yield features and other important information about the image. Proper image analysis is of high importance to the medical community as accurately classifying different conditions and diseases can be facilitated with excellent patient imaging. Multifractal analysis can be leveraged for performing texture classification and image segmentation. In this paper, we propose fusion-based algorithms utilizing multifractal analysis for medical image segmentation. We use two specific multifractal masks: square and quincunx. Our techniques show new insights by using methods such as histogram decomposition in conjunction with new techniques, such as fusion. By fusing different slope images, we can extract more features thus making our proposed algorithms more robust and accurate than traditional multifractal analysis techniques. These methods are further capable of reliably segmenting medical images by implementing multifractal analysis techniques in coordination with methods such as gaussian blurring and morphological operations. The resulting image can then be easily analyzed by medical professionals for diagnosing medical conditions. The outcomes show that the proposed algorithms extract dominant features that are more encompassing and powerful than classical techniques.

Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonomously perceive, reason, plan, and act—open a path to self-defending IoT ecosystems, but the integrating literature remains fragmented. Within the IEEE Xplore, ACM Digital Library, and MDPI literature, this survey is, to the best of our knowledge, among the first systematic reviews of agentic AI and LLM-driven approaches for autonomous IoT cybersecurity. Following a PRISMA 2020 protocol, we analyze 153 peer-reviewed studies published between 2020 and 2026 in IEEE Xplore, the ACM Digital Library, and MDPI journals. We organize the corpus along a four-pillar taxonomy: agent architecture (single- vs. multi-agent), reasoning strategy (chain-of-thought, ReAct, plan-and-solve, tool use), action scope (detection, response, threat hunting, vulnerability discovery, deception), and deployment topology (edge, fog, cloud). We synthesize four flagship application domains, consolidate datasets and benchmarks, and analyze open challenges including hallucination, prompt-injection robustness, explainability, privacy, latency, and governance. A 2026 research roadmap identifies federated agentic learning, verifiable autonomous reasoning, trustworthy multi-agent collaboration, and resource-hardened edge agents as high-priority directions. A companion reproducibility kit—prompt templates, reference single- and multi-agent loops, and an Edge-IIoTset-style evaluation harness, released as illustrative scaffolding rather than a validated framework—is released publicly and archived on Zenodo (DOI 10.5281/zenodo.20726552).

Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonomously perceive, reason, plan, and act—open a path to self-defending IoT ecosystems, but the integrating literature remains fragmented. Within the IEEE Xplore, ACM Digital Library, and MDPI literature, this survey is, to the best of our knowledge, among the first systematic reviews of agentic AI and LLM-driven approaches for autonomous IoT cybersecurity. Following a PRISMA 2020 protocol, we analyze 153 peer-reviewed studies published between 2020 and 2026 in IEEE Xplore, the ACM Digital Library, and MDPI journals. We organize the corpus along a four-pillar taxonomy: agent architecture (single- vs. multi-agent), reasoning strategy (chain-of-thought, ReAct, plan-and-solve, tool use), action scope (detection, response, threat hunting, vulnerability discovery, deception), and deployment topology (edge, fog, cloud). We synthesize four flagship application domains, consolidate datasets and benchmarks, and analyze open challenges including hallucination, prompt-injection robustness, explainability, privacy, latency, and governance. A 2026 research roadmap identifies federated agentic learning, verifiable autonomous reasoning, trustworthy multi-agent collaboration, and resource-hardened edge agents as high-priority directions. A companion reproducibility kit — prompt templates, reference single- and multi-agent loops, and an Edge-IIoTset-style evaluation harness — is released at https://github.com/vnageshwaran-de/agentic-iot-security and archived on Zenodo (DOI 10.5281/zenodo.20446651).