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In today's world, a Network Intrusion Detection System (NIDS) plays a vital role in order to secure the Wireless Sensor Network (WSN). However, the traditional NIDS model faced critical constraints with network traffic data due to growth in the complexity of modern attacks. These constraints have a direct impact on the overall performance of the WSN. In this paper, a new robust network intrusion classification framework based on the enhanced Visual Geometry Group (VGG-19) pre-trained model has been proposed to prolong the performance of WSN. Primarily, the pre-trained weights from the ImageNet dataset are utilized to train the parameters of the VGG-19. Afterward, a Hybrid Deep Neural Network based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) will be employed to extract the influential features from network traffic data to enlarge the intrusion detection accuracy. The proposed VGG-19 + Hybrid CNN-LSTM model exploits both binary classification and multi-classification to classify attacks as either normal or attacked. A network intrusion benchmark dataset is used to assess the performance of the suggested system. The results reveal that the proposed VGG-19 + Hybrid CNN-LSTM learning system surpasses other pre-trained models with a superior accuracy of 98.86% during the multi-classification test. [ABSTRACT FROM AUTHOR]

Latent fingerprint recognition plays an essential role for law enforcement agencies to detect criminals and security purposes. One of the key stages utilized in the latent fingerprint recognition model is to automatically learn consistent minutiae from fingerprint images. However, the existing state-of-the-art recognition approaches are not adequate since live-scan fingerprint images and enhancements are necessary for each step of the recognition process. Hence, an automated recognition system along with appropriate minutiae learning algorithm is required for matching the latent fingerprint exactly. In this paper, an efficient recognition system using dictionary learning and Local Context-Perception deep neural network (LCPnet) has been proposed to enhance the accuracy of latent fingerprint recognition. Primarily, the Total Variation decomposition model is utilized to remove the smooth background noise and dictionary learning contributes to the extraction of multiple patches. Afterward, the LCPnet is trained for 12 patch types to develop a salient minutiae descriptor where every descriptor is trained using LCPnet with a particular patch size at a location surrounding the minutiae. The proposed detection system has been tested through two latent public datasets. Here, three different types of templates (LCPnet minutiae, LCPnet texture, and LCPnet minutiae+texture) are analyzed for evaluating the proposed fingerprint detection system. The performance results manifest that the proposed system acquires a superior recognition accuracy of 99.44% and 99.58% under two different datasets. [ABSTRACT FROM AUTHOR]