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Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.
In the work at hand, we combine a Private Information Retrieval (PIR) protocol with Somewhat Homomorphic Encryption (SHE) and use Searchable Encryption (SE) with the objective to provide security and confidentiality features for a third party cloud security audit. During the auditing process, a third party auditor will act on behalf of a cloud service user to validate the security requirements performed by a cloud service provider. Our concrete contribution consists of developing a PIR protocol which is proceeding directly on a log database of encrypted data and allowing to retrieve a sum or a product of multiple encrypted elements. Subsequently, we concretely apply our new form of PIR protocol to a cloud audit use case where searchable encryption is employed to allow additional confidentiality requirements to the privacy of the user. Exemplarily we are considering and evaluating an audit of client accesses to a controlled resource provided by a cloud service provider.
Cloud computing is the emerging technology providing IT as a utility through internet. The benefits of cloud computing are but not limited to service based, scalable, elastic, shared pool of resources, metered by use. Due to mentioned benefits the concept of cloud computing fits very well with the concept of m-learning which differs from other forms of e-learning, covers a wide range of possibilities opened up by the convergence of new mobile technologies, wireless communication structure and distance learning development. The concept of cloud computing like any other concept has not only benefits but also introduces myriad of security issues, such as transparency between cloud user and provider, lack of standards, security concerns related to identity, Service Level Agreements (SLA) inadequacy etc. Providing secure, transparent, and reliable services in cloud computing environment is an important issue. This paper introduces a secured three layered architecture with an advance Intrusion Detection System (advIDS), which overcomes different vulnerabilities on cloud deployed applications. This proposed architecture can reduce the impact of different attacks by providing timely alerts, rejecting the unauthorized access over services, and recording the new threat profiles for future verification. The goal of this research is to provide more control over data and applications to the cloud user, which are now mainly controlled by Cloud Service Provider (CSP).