The blistering growth of the Internet of Things (IoT) networks has posed considerable issues of security because of the growing number of connected devices and the susceptibility of them to cyberattacks. Conventional anomaly detectors usually cannot reflect complicated communication association and dynamic behavioral trends that exist in the IoT context. This paper suggests a Probabilistic Graph Learning Based Anomaly Detection (PGL-AD) model on the CICIoT2023 data. The suggested method models IoT devices as the nodes of a probabilistic graph, with the communication relationships included as weighted edges with the probabilities of interactions. Learning through graph representation is used to learn probabilistic embeddings that incorporate structural and behavioral network traffic attributes. Probabilistic inference is carried out to calculate anomaly scores to determine abnormal communication patterns. Experimental performance shows that the proposed framework has reached $99.08 \%, 98.86 \%, 98.91 \%$, and $98.86\%$ detection accuracy, precision, recall, and F1-score respectively, and is superior to the traditional machine learning and deep learning models. The probabilistic graph learning algorithm is a good algorithm with the capability to learn network dependencies and uncertainty, to be able to detect anomalies accurately and at scale. The proposed framework is a dependable and effective measure of increasing the security of IoT and ensuring that connected devices are not affected by developing cyber threats.
T. H. Vidhya, K. Nithya, K. Alqawasmi et al.· International Conference on...· 0 citations
The high rate of Internet of Things (IoT) networks development has posed a serious problem of effective resource allocation because devices are heterogeneous, the traffic conditions are dynamic, and the energy and latency requirements are severe. Traditional resource allocation methods and classical reinforcement learning methods are not always the best methods to perform in a highly dynamic environment because they lack the adaptability and reduce convergence. The proposed paper introduces a Quantum Reinforcement Learning (QRL)-motivated adaptive resource allocation model which uses quantum-inspired state representation, as well as quantum-classical policy optimization, to optimize resource allocation. The proposed model is a dynamic distribution of bandwidth, transmission power, and computational resources the real-time state of network. Experimental assessment shows better performance than the traditional and classical reinforcement learning techniques. The proposed QRL framework attains the average accuracy of resource allocation 97.84%, lessens the communication latency by 43%, escalates the throughput by 64%, and lessens the energy usage by 37%. The system also has better convergence and stability exception when applied to different network load. Combination of quantum feature encoding improves efficacy in decision and learning. The findings affirm that the suggested framework offers a powerful and scalable system of smart resource management in the next-generation IoT systems.
A.Mohan Kumar, M. Al-Shalout, M. Elakiya et al.· International Conference on...· 0 citations
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