Search

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security. [ABSTRACT FROM AUTHOR]

Content Delivery Networks (CDNs) have grown in popularity as a result of the ongoing development of the Internet and its applications. The workload on streaming media service systems can be significantly decreased with the help of the cooperative edge-cloud computing architecture. In the traditional works, a different types of content placement and routing algorithms are developed for improving the content delivery of cloud systems with reduced delay and cost. But, the majority of existing algorithms facing complexities in terms of increased resource usage, ineffective delivery, and high system designing complexity. Therefore, the proposed work aims to develop a new framework, named as, Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud CDNs. Here, the Chaotic Krill Herd Optimization (CKHO) method is used to optimize the resource usage for content placement. Then, a Hierarchical Probability Routing (HPR) model is employed to enable a dependable end-to-end data transmission with an optimized routing path. The performance of the proposed HORCP model is validated and compared by using several performance metrics. The obtained results are also compared with current state-of-the-art methodologies in order to show the superiority of the proposed HORCP model. By using the HORCP mechanism, the overall memory usage of the network is reduced to 80%, CPU usage is reduced to 20%, response is minimized to 2 s, and total congestion cost with respect to the network load level is reduced to 100. [ABSTRACT FROM AUTHOR]