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There is a steep rise in the trend of the utility of Internet technology day by day. This tremendous increase ushers in a massive amount of data generated and handled. For apparent reasons, undivided attention is due for ensuring network security. An intrusion detection system plays a vital role in the field of the stated security. The proposed XGBoost–DNN model utilizes XGBoost technique for feature selection followed by deep neural network (DNN) for classification of network intrusion. The XGBoost–DNN model has three steps: normalization, feature selection, and classification. Adam optimizer is used for learning rate optimization during DNN training, and softmax classifier is applied for classification of network intrusions. The experiments were duly conducted on the benchmark NSL-KDD dataset and implemented using Tensor flow and python. The proposed model is validated using cross-validation and compared with existing shallow machine learning algorithms like logistic regression, SVM, and naive Bayes. The classification evaluation metrics such as accuracy, precision, recall, and F1-score are calculated and compared with the existing shallow methods. The proposed method outperformed over the existing shallow methods used for the dataset.

The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.

Aircraft undergoes various low-velocity impacts such as bird strike, runway debris, foreign tools drop, etc. on its structure. Kevlar is widely used in several components and structure of the aircraft as Kevlar is an organic fiber from an aromatic polyamide family. Carbon fiber has advantages like high stiffness and properties like that of kevlar. The present study focuses on the analysis of low-velocity impact properties of kevlar 149-carbon fiber reinforced polymer matrix composite material. In this analysis, kevlar 149-carbon fiber, pure kevlar 149 by 10 layers of laminate and pure carbon fiber by 10 layers of laminate are impacted by ball material with a mass of 8g at velocity of 9m/s. The ball impact on the laminate is simulated using LS-DYNA. The max. principal stress, absorbed energy, velocity and displacement of K149C, CK149, KK149CC, CCKK149, KK149 and CC are obtained and compared with all the six composite laminates. The composite laminate, K149C, proves to be the best laminate for aircrafts as it possesses a higher max. principal stress and lower displacement.