The rapid expansion of Internet of Things (iot) networks has significantly increased security vulnerabilities, exposing critical infrastructures to sophisticated cyberattacks. Traditional intrusiondetection Systems, ...
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The rapid expansion of Internet of Things (iot) networks has significantly increased security vulnerabilities, exposing critical infrastructures to sophisticated cyberattacks. Traditional intrusiondetection Systems, based mainly on signature matching and predefined rules, present limitations in identifying emerging threats and distributed attacks due to their inability to analyze complex interactions within iot networks. To address this problem, this study proposes a graph-based intrusiondetection model using Graph Neural Networks (GNNs), leveraging a dynamic representation of iot network traffic. In this model, devices are represented as nodes and communications as weighted edges, integrating features such as communication frequency, transmitted data volume, and protocol type. The proposed method was evaluated using a customized dataset from a simulated iot network to reflect real-world attack scenarios, including Denial of Service, Spoofing, and Man-in-the-Middle. Experimental results demonstrate that our GNN-based model significantly outperforms traditional machine learning methods, achieving an F1-Score of 0.95 and an AUC-ROC of 0.98, compared to values between 0.84 and 0.91 for Support Vector Machines and Random Forests. Furthermore, the system reduces the false positive rate by 40% compared to signature-based IDS, improving its applicability in operational environments. The model also proved scalable, maintaining an inference time of 2.5 ms per sample on graphs of up to 10,000 nodes, making it viable for real-time deployment. These findings confirm that graph-based anomaly detection is a promising approach for securing large-scale iot infrastructures, providing increased precision, adaptability, and robustness against emerging cyber threats.
The connectivity and integration of commodities have transformed many industries through the Internet of Things, enhancing both efficiency and functionality. However, this interconnection has also introduced critical ...
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The connectivity and integration of commodities have transformed many industries through the Internet of Things, enhancing both efficiency and functionality. However, this interconnection has also introduced critical security challenges, necessitating a robust intrusiondetection System. This paper presents a novel, enhanced method for intrusiondetection Systems in Internet of Things environments, utilizing advanced machine learning techniques and optimization algorithms. The proposed method integrates the Marine Predator Optimizer and Grey Wolf Optimizer for efficient feature selection, improving the detection and classification of anomalies. Two datasets, NFC-SECICIDS2018v2 and Botiot2018, were used to evaluate the performance and effectiveness of the proposed intrusiondetection System. The results demonstrate that the proposed method achieves an accuracy of 97.59% on the NFC-SECICIDS2018v2 dataset and 99.97% on the Botiot2018 dataset. Additionally, the method shows a perfect sensitivity of 100% on the Botiot2018 dataset and a sensitivity of 99.58% on the NFC-SECICIDS2018v2 dataset, significantly reducing the false alarm rate. The findings underscore the effectiveness of combining the Marine Predator Optimizer and Grey Wolf Optimizer to enhance the performance of intrusiondetection Systems, offering a promising solution for securing the Internet of Things networks against evolving cyber threats.
Since its inception, the Internet of Things (iot) has witnessed mushroom growth as a breakthrough technology. In a nutshell, iot is the integration of devices and data such that processes are automated and centralized...
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Since its inception, the Internet of Things (iot) has witnessed mushroom growth as a breakthrough technology. In a nutshell, iot is the integration of devices and data such that processes are automated and centralized to a certain extent. iot is revolutionizing the way business is done and is transforming society as a whole. As this technology advances further, the need to exploit detection and weakness awareness increases to prevent unauthorized access to critical resources and business functions, thereby rendering the system unavailable. Denial of Service (DoS) and Distributed DoS attacks are all too common. In this paper, we propose a Protocol Based Deep intrusiondetection (PB-DID) architecture, in which we created a data-set of packets from iot traffic by comparing features from the UNSWNB15 and Bot-iot data-sets based on flow and Transmission Control Protocol (TCP). We classify non-anomalous, DoS, and DDoS traffic uniquely by taking care of the problems like imbalanced and over-fitting. We have achieved a classification accuracy of 96.3% by using deep learning (DL) technique.
In this paper, we present recent approaches proposed to secure the Internet of Things (iot) devices against malicious cyber attacks and malware. As iot devices have limited computing, storage, processing and communica...
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ISBN:
(纸本)9798350332865
In this paper, we present recent approaches proposed to secure the Internet of Things (iot) devices against malicious cyber attacks and malware. As iot devices have limited computing, storage, processing and communication capabilities, protection those devices by themselves is very challenging. Fog computing has been proposed to support resource constrained iot devices to reduce delay caused by cloud computing. We also presented adopted ML models and datasets vs. targeted cyber attacks in a tabular form.
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