Using cloud computing, businesses can adopt IT without incurring a significant upfront cost. The Internet has numerous benefits, but model security remains a concern, which affects cloud embracing negatively. The security challenge gets too difficult underneath data center, while additional dimensions such as model design, multitenancy, elasticity, and the layers dependency stack have been added to the problem scope. We present a thorough examination of the cloud security challenge in this article. We looked at the issue from the standpoints of network infrastructure: cloud-provided features, cloud consumers, and cloud service delivery methods. Based on this research, we developed an in-depth characterization of the data protection challenge and recommended security solutions that should address the critical aspects of the issue. Cloud computing is an exploding field of research that relies on distributing computing power instead of using dedicated computers or smart devices. Most of the growth in this sector is attributed to the indispensability of electronic and digital gadgets and the shift from a traditional IT subscription model to a unique cloud model. Cloud computing offered a significant danger and difficulty for information system projects, but it also provided them with several possibilities to improve their data processing. It has also been noted that cloud users and consumers do not yet have the necessary forensic skills to detect illegal activity in the cloud. Although the cloud offers potential technological and economic advantages, consumers have been reluctant to adopt it primarily due to security concerns and the difficulty of conducting an appropriate investigation into the cloud. Some study has been done in this area, and strategies for conducting forensic investigations have been proposed. In this research paper, we begin by analyzing the intrusion detection progress made by other academics, and then we analyze and evaluate our conclusions in order to assess the potential difficulties that cloud forensics face based on these findings.

Traditional beamforming techniques rely on optimization or analytically derived weight calculations. Beamforming techniques are often inadequate in dynamic propagation environments characterized by user mobility, rapid channel variations, and heterogeneous service demands. The evolution of 6G wireless communication systems demands intelligent and highly adaptive beamforming mechanisms to satisfy stringent requirements for spectral efficiency, latency, reliability, and massive connectivity. To overcome these limitations, this study presents an artificial intelligence and machine learning-based adaptive beamforming framework tailored for future 6G communication networks. The proposed approach integrates ray-tracing-based channel modeling with supervised learning techniques to enable data-driven-optimized beamforming. Artificial neural network (ANN) and Support Vector Regression (SVR) models directly predict optimal complex-valued beamforming weights from relevant channel features such as angular parameters, user position, transmission power, and path loss. A massive multiple input multiple output (MIMO) configuration using a 32-element uniform linear array and 64 system parameters was considered to evaluate the effectiveness of the learning-based framework under realistic simulation conditions. The results indicate that AI and ML beamforming enable fast and precise beam adaptation while eliminating the need for computationally real-time analytical calculations. The proposed work offers a scalable and efficient solution for intelligent beam management in dynamic 6G environments and supports real-time decision making. Furthermore, this study establishes a strong foundation for future extensions incorporating edge intelligence, federated learning, reinforcement learning, and autonomous beam control in next-generation wireless networks.

Mobile ad hoc networks (MANET) have been seen as a related advancement to Group Key Management (GKM) applications. Remembering the true objective to guarantee amass applications and disallow uncertified clients from getting to the correspondence data that cannot be anchored by a remote MANET, including IP multicast, the singular gathered data content must remain encoded by a typical shared gathering key. Key administration is required to anchor the assurance of gathering the key and to safeguard those gathering data. GKM framework is associated with the remote system condition partners with three issues: execution, security, and system versatility. This article focuses on the Unmanned Aerial Vehicle (UAV)-mobile backbone node (MBN) remote system performance. The UAV-MBN Network condition is a military system that includes a proposal to group an important administrative structure. A half-and-half gathering key administration technique, which works on each target of UAV-MBN, is included in an arrangement to start two basic remote gathering key administration difficulties: (1) operational performance and (2) multiple-enrollment development. By working with minimal small-scale key administration, this strategy can diminish the execution cost associated with the key administration along with the increment operational execution of remote GKM. Scaled-down key organization is carried out in the context of these movement units. The key administration approach also restricted the operational procedure and decreased the operations cost in terms of key generation, figuring, and associated correspondence. Overall, the HGKM strategy that has been introduced enhances the operational process and functions effectively in reasonable remote areas.

The objective of this research is to focus on handling QoS for Wireless Sensor Network (WSN). Packet dropping and modifying is common attack in wireless sensor networks. These attacks interrupt the communication network and are difficult to identify in multi hop sensor networks. This paper presents an effective scheme to identify the packet droppers and modifiers by using ranking algorithms on the DAG generated by the nodes in the network. Simulation results are presented.