Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.

Detection of intrusions is a system that is competent in detecting cyber-attacks and network anomalies. A variety of strategies have been developed for IDS so far. However, there are factors that they lack in performance, creating scope for further research. The current trend shows that the Deep Learning (DL) technique has been proven better than traditional techniques for IDS. Throughout these studies, we presented a hybrid model that is a Deep Learning method called Bidirectional Recurrent Neural Network using Long Short-Term Memory and Gated Recurrent Unit. Through simulations on the public dataset CICIDS2017, we have shown the model’s effectiveness. It has been noted that the suggested model successfully predicted most of the network attacks with 99.13% classification accuracy. The proposed model outperformed the Naïve Bayes classifier in terms of prediction accuracy and False Positive rate. The suggested model managed to perform well with only 58% attributes of the dataset compared to other existing classifiers. Moreover, this study also demonstrates the performance of LSTM and GRU with RNN independently.

In this research work, various machine learning models such as linear regression (LR), KNN and MLP were created to predict the optimized synthesis of biodiesel from pre-treated and non-treated Linseed oil in base transesterification reaction mode. Three input parameters were included for modelling, reaction time, catalyst concentrated ion, and methanol/oil-molar ratio. In biodiesel transesterification reaction 180 samples run with non-Pre-treated Linseed Methyl Ester (NPLME), Water Pre-treated Linseed Methyl Ester (WPLME) and Enzymatic Pre-treated Linseed Methyl Ester (EPLME) oil as feed stocks and optimized parameters are find out for maximum biodiesel yield to be 8:1 molar ratio, 0.4% weight catalyst, 60 °C reaction temperature.To test the technique, R² and MAPE parameters were used. The average R² values for linear regression, KNN, and MLP are 0.7030, 0.8554 and 0.7864 respectively. Moreover, the average MAPE values for these models are 11.1886, 6.0873 and 8.0669 respectively. Hence, it is observed that the KNN model outperforms other models with higher accuracy and low MAPE score.

Wireless sensor networks (WSN) play in vital role in different research area technology, electronics, and telecommunication, but these wireless sensor networks have limited computational capability and memory. The basic functions of WSN are analysis, processing, storage, and mining of data. Due to the flexibility of WSN in solving problems of various fields, it has gained more popularity. WSNs have been successfully applied in a variety of applications. In these days, knowledge management systems are attracting research that emerge with wireless sensor networks to reduce drawback of sensors. This chapter presents the review literature of knowledge management and demonstrates creating, managing, sharing, and utilizing in organizations.