The rapid development of virtual reality (VR) technologies is bringing unprecedented immersive experiences and unusual digital content. Nevertheless, these advancements introduce new security challenges, especially in safeguarding the visual content displayed by VR devices like VR glasses and head-mounted displays. Most existing approaches for visual output security rely exclusively on numerical data, such as object attributes and overlook the need of visual information necessary for thorough VR protection. Moreover, these approaches typically assume a fixed size input, failing to address the dynamic nature of VR where the number of virtual items is constantly changing. This article presents a multimodal deep reinforcement learning (MMDRL) approach to secure the visual outputs in VR applications. We formalize a Markov decision process (MDP) framework for the MMDRL agent that integrates both numerical and image data into the state space to effectively mitigate visual threats. Furthermore, our MMDRL agent is engineered to handle data of varying sizes, which makes it more suitable for VR environments. Results from our experiments demonstrate the agent’s ability to successfully counteract visual attacks, significantly outperforming previous approaches. The ablation study confirms the important role of image data in improving the agent’s performance, highlighting the efficacy of our multimodal approach. In addition, we provide a video demonstration to showcase these results. Finally, we open-source our VR testbed and source code for further testing and benchmarking.

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers' attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient's health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices'. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system's integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy (99.31%) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.

In many applications, software protection can not be sufficient to provide high security needed by some critical applications. A noteworthy example are the bitcoin wallets. Designed the most secure piece of software, their security can be compromised by a simple piece of malware infecting the device storing keys used for signing transactions. Secure hardware devices such as Trusted Platform Module (TPM) offers the ability to create a piece of code that can run unmolested by the rest of software applications hosted in the same machine. This has turned out to be a valuable approach for preventing several malware threats. Unfortunately, their restricted functionalities make them inconsistent with the use of multi and threshold signature mechanisms which are in the heart of real world cryptocurrency wallets implementation. This paper proposes an efficient multi-signature scheme that fits the requirement of the TPM. Based on discrete logarithm and pairings, our scheme does not require any interaction between signers and provide the same benefits as the well established BLS signature scheme. Furthermore, we proposed a formal model of our design and proved it security in a semi-honest model. Finally, we implemented a prototype of our design and studied its performance. From our experimental analysis, the proposed design is highly efficient and can serve as a groundwork for using TPM in future cryptocurrency wallets.

In this paper we present an electronic voting system based on homomorphic encryption to ensure privacy, confidentiality in the voting. Our proposal offers all the advantages of the multiplicatively homomorphic encryption cryptosystems. The proposed voting scheme is suitable for multi-candidate elections as well as for elections in which contains neutral votes.

Cloud computing has now become a major trend, it is a new data hosting technology that is Very popular in recent years thanks to the amortization of costs it induces to companies. In this paper we present the major security issues in cloud computing and we also propose a simple, secure, and privacy-preserving architecture for inter-Cloud data sharing based on an encryption/decryption algorithm which aims to protect the data stored in the cloud from the unauthorized access.

Research efforts have produced many algorithms for fast pairing computation and several efficient hardware implementations. However, side channel attacks (SCAs) are a serious threat on hardware implementations. Previous works in this area have provided some costly countermeasures against side channel attacks. In this paper, we propose a new countermeasure based on the isomorphism classes in order to secure pairing computation on elliptic curves defined over