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The technological growth and advances in the internet led to the generation of huge volume of data that networks must be capable of transmitting. Providing security to this data is a challenging task. The development in the internet attracts several vulnerable attacks. The researchers in the literature proposed several machine learning, Deep learning and ANN based approaches for efficient attack detection. However, these approaches are prone to high false alarm rates and exhibits poor performance for diversified incoming traffic, because these methodologies relay on the packet level or transaction level features. The performance is inversely proposal to the diversity ratio of packet level features. To handle this, we introduced a combination of high-performed evolutionary algorithms and neural networks for attack classification at flow level with low false alarm rates and high detection accuracy. A unique set of flow features are defined to handle the traffic at flow level and optimal feature selection using whale Optimization Algorithm (WOA). The gravitational search (GS), and particle swarm optimization (PSO) combinations are used in attack detection phase to train the ANN and results proposed model as GSPSO-ANN with WOA. The performance of the proposed model is evaluated with NSL-KDD and CSE-CIC-IDS2018 datasets. The results are compared with other ANN based conventional methods. The results inferred that the proposed GSPSO-ANN with WOA attained maximum detection accuracy with low false alarm rates and processing time and also maintained consistency in the performance for diversified traffic.

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In the recent advancements attention mechanism in deep learning had played a vital role in proving better results in tasks under computer vision. There exists multiple kinds of works under attention mechanism which includes under image classification, fine-grained visual recognition, image captioning, video captioning, object detection and recognition tasks. Global and local attention are the two attention based mechanisms which helps in interpreting the attentive partial. Considering this criteria, there exists channel and spatial attention where in channel attention considers the most attentive channel among the produced block of channels and spatial attention considers which region among the space needs to be focused on. We have proposed a streamlined attention block module which helps in enhancing the feature based learning with less number of additional layers i.e., a GAP layer followed by a linear layer with an incorporation of second order pooling(GSoP) after every layer in the utilized encoder. This mechanism has produced better range dependencies by the conducted experimentation. We have experimented our model on CIFAR-10, CIFAR-100 and FGVC-Aircrafts datasets considering finegrained visual recognition. We were successful in achieving state-of-the-result for FGVC-Aircrafts with an accuracy of 97%.