Anomaly detection in Internet of Things (IOT) network traffic involves identifying abnormal patterns or behaviors, enabling early detection of potential security threats or system malfunctions in the IOT ecosystem. Io...
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Anomaly detection in Internet of Things (IOT) network traffic involves identifying abnormal patterns or behaviors, enabling early detection of potential security threats or system malfunctions in the IOT ecosystem. IoT encompasses a variety of networks, addressing not only security challenges within sensor networks, the internet, and mobile communication networks but also specifically focusing on issues related to privacy protection, information management, network authentication, and access control. In this manuscript, Anomaly detection in IoT network Traffic using a bidirectional 3d quasi-recurrent neural network with Coati Optimization Algorithm (AdIOT-B3dQRNN-COA) is proposed. Initially, the input data are collected from the dS2OS dataset. Then, the collecteddata is fed into pre-processing utilizing an Implicit Unscented Particle Filter (IUPF). The IUPF is used to remove the invaliddata. Subsequently, the preprocesseddata are sent into the Archimedes optimization algorithm (AOA) to select features. Seven characteristics from the dS2OS dataset are chosen using AOA. The selected features are then fed into a bidirectional 3d quasi-recurrent neural network (Bi-3dQRNN) to classify anomaly detection in an Internet of Things network into the following categories: data probing, malicious control, malicious operation, scan, spying, incorrect configuration, dOS attack, and normal. To guarantee accurate classification of anomaly detection in IoT networks, Bi-3dQRNN generally does not express any adaptation of optimization algorithms for figuring out the best parameters. Hence, the Coati Optimization Algorithm (COA) to optimize Bi-3dQRNN accurately classifies anomaly detection in the IOT network. The proposed AdIOT-B3dQRNN-COA approach is implemented in MATLAB. The performance of the proposed method was examined utilizing performance metrics like Accuracy, Computational Time, F-measure, Precision, Recall, and ROC. The proposed AdIOT-B3dQRNN-COA approach contains 32.15%,
Hyperspectral imaging is unable to acquire images with high resolution in both spatial and spectral dimensions yet, due to physical hardware limitations. It can only produce low spatial resolution images in most cases...
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Hyperspectral imaging is unable to acquire images with high resolution in both spatial and spectral dimensions yet, due to physical hardware limitations. It can only produce low spatial resolution images in most cases and thus hyperspectral image (HSI) spatial super-resolution is important. Recently, deep learning-based methods for HSI spatial super-resolution have been actively exploited. However, existing methods do not focus on structural spatial-spectral correlation and global correlation along spectra, which cannot fully exploit useful information for super-resolution. Also, some of the methods are straightforward extension of RGB super-resolution methods, which have fixed number of spectral channels and cannot be generally applied to hyperspectral images whose number of channels varies. Furthermore, unlike RGB images, existing HSI datasets are small and limit the performance of learning-based methods. In this article, we design a bidirectional 3d quasi-recurrent neural network for HSI super-resolution with arbitrary number of bands. Specifically, we introduce a core unit that contains a 3d convolutional module and a bidirectionalquasi-recurrent pooling module to effectively extract structural spatial-spectral correlation and global correlation along spectra, respectively. By combining domain knowledge of HSI with a novel pretraining strategy, our method can be well generalized to remote sensing HSI datasets with limited number of training data. Extensive evaluations and comparisons on HSI super-resolution demonstrate improvements over state-of-the-art methods, in terms of both restoration accuracy and visual quality.
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