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The demand for cybersecurity is rising recently due to the rapid improvement of network technologies. As a primary defense mechanism, an intrusion detection system (IDS) was anticipated to adapt and secure computing infrastructures from the constantly evolving, sophisticated threat landscape. Recently, various deep learning methods have been put forth; however, these methods struggle to recognize all forms of assaults, especially infrequent attacks, because of network traffic imbalances and a shortage of aberrant traffic samples for model training. This work introduces deep learning (DL) based Attention based Nested U-Net (ANU-Net) for intrusion detection to address these issues and enhance detection performance. For this IDS model, the first data preprocessing is carried out in three stages: duplication elimination, label transformation, and data normalization. Then the features are extracted and selected based on the Improved Flower Pollination Algorithm (IFPA). The Improved Monarchy Butterfly Optimization Algorithm (IMBO), a new metaheuristic, is used to modify the hyper-parameters in ANU-Net, effectively increasing the learning rate for spatial-temporal information and resolving the imbalance problem. Through the use of parallel programming, the MapReduce architecture reduces computation complexity while significantly accelerating processing. Three publicly available data sets were used to evaluate and test the approach. The investigational outcomes suggest that the proposed technique can more efficiently boost the performances of IDS under the scenario of unbalanced data. The proposed method achieves above 98% accuracy and classifies various attacks significantly well compared to other classifiers.

The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.