Abdulatif Alabdulatif

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.

Malware has become increasingly prevalent in recent years, endangering people, businesses, and digital assets worldwide. Despite the numerous techniques and methodologies proposed for detecting and neutralizing malicious agents, modern automated malware creation methods continue to produce malware that can evade modern detection techniques. This has increased the need for advanced and accurate malware classification and detection techniques. This paper offers a unique method for classifying malware, using images that use dual attention and convolutional neural networks. Our proposed model has demonstrated exceptional performance in malware classification, achieving the remarkable accuracy of 98.14% on the Malimg benchmark dataset. To further validate its effectiveness, we also evaluated the model’s performance on the big 2015 dataset, where it achieved an even higher accuracy rate of 98.95%, surpassing previous state-of-the-art solutions. Several metrics, including the precision, recall, specificity, and F1 score were used to evaluate accuracy, showing how well our model performed. Additionally, we used class-balancing strategies to increase the accuracy of our model. The results obtained from our experiments indicate that our suggested model is of great interest, and can be applied as a trustworthy method for image-based malware detection, even when compared to more complex solutions. Overall, our research highlights the potential of deep learning frameworks to enhance cyber security measures, and mitigate the risks associated with malware attacks.