An optimized LSTM-based deep learning model for anomaly network intrusion detection.

The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations.

RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System.

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches.

A Deep Learning Method to Forecast COVID-19 Outbreak.

A new pandemic attack happened over the world in the last month of the year 2019 which disrupt the lifestyle of everyone around the globe. All the related research communities are trying to identify the behaviour of pandemic so that they can know when it ends but every time it makes them surprise by giving new values of different parameters. In this paper, support vector regression (SVR) and deep neural network method have been used to develop the prediction models.