Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC.

Internet of Things (IoT) is a system of wireless electronics that may visualize and control much of the world that surrounds us. This new concept brings with it a broad range of research challenges. Rather, the highly critical challenge of IoT networks is security and particularly authentication. This paper consists of an exhaustive study about authentication in the IoT environment. Accordingly, we present analysis result of a comparison study between eleven IoT authentication schemes. The main criteria of our comparison are cryptography techniques, security services provided, resistance against attacks and computational cost. At the end, this paper will highlight the weaknesses of current IoT authentication and propose future research directions.

Most of Wireless Sensor Networks researches focus on reducing the amount of energy consumed by nodes and network to increase the network lifetime. Thus, several papers have been presented and published to optimize energy consumption in each area of WSNs, such as routing, localization, coverage, security, etc. To test and evaluate their propositions, authors apply an energy dissipation model; this model must be more realistic and suitable to give good results. In this paper we present a general preview on different sources of energy consumption in wireless sensor networks, and provide a comparative study between two energy models used in WSNs that offer an effective and an adequate tool for researchers.