The "Distributed Denial of Service (DDoS)" threats have become a tool for the hackers, cyber swindlers, and cyber terrorists. Despite the high amount of conventional mitigation mechanisms that are present no...
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The "Distributed Denial of Service (DDoS)" threats have become a tool for the hackers, cyber swindlers, and cyber terrorists. Despite the high amount of conventional mitigation mechanisms that are present nowadays, the DDoS threats continue to enhance in severity, volume, and frequency. The DDoS attack has highly affected the availability of the networks for the previous years and still, there is no efficient defense technique against it. Moreover, the new and complex DDoS attacks are increasing on a daily basis but the traditional DDoS attack detection techniques cannot react to these threats. On the other hand, the hackers are employing very innovative strategies to initiate the threats. But, the traditional methods can become effective and reliable when combined with the deep learning-aided approaches. To solve these certain issues, a framework detection mechanism for DDoS attacks utilizes an attention-aided deep learning methodology. The primary thing is the acquisition of data from standard data online sources. Further, from the garnered data, the significant features are drawn out from the "Deep Weighted Restricted Boltzmann Machine (RBM)" using a "Deep Belief Network (DBN)", in which the parameters are tuned by employing the recommended enhanced gannet optimization algorithm (EGOA). This feature extraction operation increases the network performance rate and also diminishes the dimensionality issues. Lastly, the acquired features are transferred to the model of "Attention and Cascaded Recurrent Neural Network (RNN) with Residual Long Short Term Memory (LSTM) (ACRNN-RLSTM)" blocks for the DDoS threat detection purpose. This designed network precisely identifies the complex and new attacks, thus it increases the trustworthiness of the network. In the end, the performance of the approach is contrasted with other traditional algorithms. Hence, the simulation outcomes are obtained that prove the system's efficiency. Also, the outcomes displayed that the designed s
This method of identifying plant leaf disease generally involves a large team of experts with extensive knowledge of plant diseases, and it can be expensive, time-consuming, and subjective. Hence, a novel plant leaf d...
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This method of identifying plant leaf disease generally involves a large team of experts with extensive knowledge of plant diseases, and it can be expensive, time-consuming, and subjective. Hence, a novel plant leaf disease classification framework is proposed to classify the plant diseases and then take preventive measures based on the classified outcomes. The plant leaf images are collected from traditional databases. The classification of leaf diseases is done with the support of the developed Multi-scale Feature Fusion-based Adaptive Deep Network (MFF-ADNet). In this developed MFF-ADNet, two processes are carried out such as feature extraction and classification. The collected images are given to the feature extraction phase, where the Visual Geometry Group (16) (VGG16), Variational Autoencoder (VAE), and Visual Transformer (ViT) network are used for extracting the features. The extracted features are fused and the resultant Multi-scale fused features are provided to the input of the classification process. Here, the Adaptive Convolutional Neural Network with Attention Mechanism (CNNAM) is utilized for classifying the plant leaf diseases and the parameters are optimized using the enhanced gannet optimization algorithm (EGOA) approach. From the results, the median value is obtained for a proposed method that is more than 7.18% of MAO-MFF-ADNet, 4.11% of TSO-MFF-ADNet, 8.03% of CO-MFF-ADNet and 4.07% of GOA-MFF-ADNet. Therefore, the experimental outcome of the developed plant leaf classification model is validated over various approaches to ensure the goodness of the developed scheme.
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