Cloud computing (CC) is becoming an essential technology worldwide. This approach represents a revolution in data storage and collaborative services. Nevertheless, security issues have grown with the move to CC, including intrusion detection systems (IDSs). Intruders have developed advanced tools that trick the traditional IDS. This study attempts to contribute toward solving this problem and reducing its harmful effects by boosting IDS performance and efficiency in a cloud environment. We build two models based on deep neural networks (DNNs) for this study: the first model is built on a multi-layer perceptron (MLP) with backpropagation (BP), and the other is trained by MLP with particle swarm optimization (PSO). We use these models to deal with binary and multi-class classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. This study aims to improve the accuracy of detecting intrusion attacks for IDSs in a cloud environment and to enhance other performance metrics. In this study, we document all aspects of our experiments in depth. The results show that the best accuracy obtained for binary classification was 98.97% and that for multi-class classification was 98.41%. Furthermore, the results are compared with those from the related literature.

The network revolution is becoming bigger and more complex and data need to be secured against cybercrimes. Cybercrimes caused the loss of billions of dollars affecting the global economy, with the intention to maliciously affect critical and confidential information. With the presence of these crimes committed on a daily basis, the security of the cyberspace has become an urgent reaction to reduce and possibly avoid the effect of cybercrimes. Nowadays, the internet of things (IoT) revolution is becoming the focus of research, and both security and privacy are recognized as the main issues for IoT applications, mainly because of its implementation in critical areas, such as healthcare systems. In this paper, the cybersecurity state in IoT domain along with its security challenges are discussed, also we address some security requirements and techniques to overcome these challenges. Finally, blockchain technology is discussed as a recommended solution to support IoT security.