The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform.

Recent advances in cloud technology facilitates data owners having limited resources to outsource their data and computations to remote servers in Cloud. To protect against unauthorized information access, sensitive data are encrypted before outsourcing. However, traditional cryptosystems need decrypting ciphertext for outsourced computations that may violate data security as well may introduce higher computational complexity. Homomorphic encryption is a solution that allows performing computations directly on ciphertext. On the otherhand, it is evident that the computations on data may vary from users to users depending on the requirements. So, it is not always feasible to allow all computations to different users on the whole ciphertext stored in cloud. In this paper, we proposed a framework for integration of role based access control (RBAC) mechanism with homomorphic cryptosystem for secure and controlled access of data in cloud. Our proposed framework is developed based on trust and role hierarchy with multi-granular operational access rights to heterogeneous stakeholders or users.

Nowadays, Malware has become a serious threat to the digitization of the world due to the emergence of various new and complex malware every day. Due to this, the traditional signature-based methods for detection of malware effectively becomes an obsolete method. The efficiency of the machine learning model in context to the detection of malware files has been proved by different researches and studies. In this paper, a framework has been developed to detect and classify different files (e.g exe, pdf, php, etc.) as benign and malicious using two level classifier namely, Macro (for detection of malware) and Micro (for classification of malware files as a Trojan, Spyware, Adware, etc.). Cuckoo Sandbox is used for generating static and dynamic analysis report by executing files in the virtual environment. In addition, a novel model is developed for extracting features based on static, behavioral and network analysis using analysis report generated by the Cuckoo Sandbox. Weka Framework is used to develop machine learning models by using training datasets.