MOTION – A Framework for Mixed-Protocol Multi-Party Computation

The integration of blockchain and federated learning (FL) has emerged as a promising solution to address data privacy and security challenges in Intelligent Unmanned Ports (IUPs). However, existing blockchain federated learning (BFL) frameworks encounter significant limitations, including high latency, inefficient data processing, and limited scalability, particularly in scenarios with sparse and distributed data. This paper introduces a novel multi-chain federated learning (MFL) framework to overcome these challenges. The proposed MFL architecture interconnects multiple BFL chains to facilitate the secure and efficient aggregation of data across distributed devices. The framework enhances privacy and efficiency by transmitting aggregated model updates rather than raw data. A low-frequency consensus mechanism is employed to improve performance, leveraging game theory for representative selection to optimize model aggregation while reducing inter-chain communication overhead. The experimental results demonstrate that the MFL framework significantly outperforms traditional BFL in terms of accuracy, latency, and system efficiency, particularly under the conditions of high data sparsity and network latency. These findings highlight the potential of MFL to provide a scalable and secure solution for decentralized learning in IUP environments, with broader applicability to other distributed systems such as the Industrial Internet of Things (IIoT).

The IBM SP is one of the most powerful commercial MPPs, yet, in spite of its fast processors and high network bandwidth, the SP's communication latency is inferior to older machines such as the TMC CM-5 or Meiko CS-2. This paper investigates the use of Active Messages (AM) communication primitives as an alternative to the standard message passing in order to reduce communication overheads and to offer a good building block for higher layers of software. The first part of this paper describes an implementation of Active Messages (SP AM) which is layered directly on top of the SP's network adapter (TB2). With comparable bandwidth, SP AM's low overhead yields a round-trip latency that is 40% lower than IBM MPL's. The second part of the paper demonstrates the power of AM as a communication substrate by layering Split-C as well as MPI over it. Split-C benchmarks are used to compare the SP to other MPPs and show that low message overhead and high throughput compensate for SP's high network latency. The MPI implementation is based on the freely available MPICH version and achieves performance equivalent to IBM's MPI-F on the NAS benchmarks.

An end-to-end home network security framework

Cloud techology is growing fast and every new technology is developed and supported by cloud platform. There are various issues has been raised when task is executing on cloud, So we need a new and secure framework on which each and every task securily executed. For every task execution we need a efficient task sheduling algorithms, realible resorces and security features by which we will assigned and run our tasks. İn this paper we are proposed a framework which includes all these features and you can add or delete any security feature related with scheduling, resource allocation and security protocols.

ABSTRACTVANETs have been developed to improve the safety and efficiency of transportation systems (V2V communications) and to enable various mobile services for the traveling public (V2I communications). For VANET technologies to be widely available, security issues concerning several essential requirements should be addressed. The existing security architectures and mechanisms have been studied separately in V2V and V2I networks, which results in duplicated efforts, security modules, and more complex security architectures. In this paper, we propose a unified security architecture and its corresponding security protocols that achieve essential security requirements such as authentication, conditional privacy, non‐repudiation, and confidentiality. To the best of our knowledge, this paper is the first study that deals with the security protocol in V2V as well as the handover authentication in V2I communications. Our proposal is characterized by a low‐complexity security framework, owing to the design and unification of the security architectures and modules. Furthermore, the evaluation of the proposed protocols proves them to be more secure and efficient than existing schemes. Copyright © 2010 John Wiley & Sons, Ltd.

In the history of cryptography, many cryptographic protocols rely on random coin tosses to discuss their provable security. Although flipping coins is indispensable in this manner, the coins themselves have never been in the spotlight. Therefore, we would like to make physical coins go up to the stage of cryptography, as a deck of physical playing cards has been used to perform a secure multi-party computation. Such a card-based protocol is helpful both to perform a secure computation without any black-box computers and to understand the principles of secure protocols. In this paper, we propose a new framework of secure multi-party computation using physical coins, named a coin-based protocol. Whereas a face-down card can conceal the information about its face side, one side of a coin leaks the information of its other side. Hence, more careful design is required for a secure coin-based protocol than the card-based one. We introduce a computational model of the coin-based protocol and explicitly give protocols for NOT, AND, and copy computations. We also discuss how to implement the protocols in practice.

[1]. Artificial Intelligence (AI) is revolutionizing cybersecurity by improving threat identification, mitigation, and protection strategies. As cyber threats become more complex and sophisticated, AI-driven solutions play a key role in strengthening the security architecture and ensuring proactive protection, AIdriven solutions offer proactive defense strategies, real-time network monitoring, and automated security protocols. This research employs the The Gray Correlation Analysis (GRA) method is used to evaluate the performance of AI in five important domains: communication protocols (C1), node security (C2), network monitoring (C3), cryptography (C4), and security policy (C5). study highlights how AI optimizes security frameworks, mitigates cyber risks, and strengthens overall defense mechanisms. The results indicate that AI significantly improves cybersecurity resilience by addressing vulnerabilities across multiple layers of security. Research Significance: Cyber threats are evolving, requiring intelligent and adaptive security measures AI enhances conventional cybersecurity approaches by automating threat detection and mitigation. Assessing the impact of AI on the security architecture helps improve security mechanisms and response effectiveness. impact on key security components provides insights into its effectiveness. Methodology: Grey Relational Analysis (GRA) Grey Relational Analysis (GRA) is used to assess the relationship between multiple security factors in AI-driven cybersecurity. GRA helps in ranking and determining the most effective AI applications in cybersecurity by analyzing Communication Protocols (C1), Node Security (C2), Network Monitoring (C3), Cryptography (C4), and Security Policy (C5). Alternative Approaches: Communication Protocol (C1): AI-based secure communication frameworks and anomaly detection in data exchange. Node Security (C2): AI-driven authentication, endpoint protection, and intrusion detection at the node level. Network Monitoring (C3): AI-powered network traffic analysis, anomaly detection, and automated threat response. Cryptography (C4): AI-assisted encryption, quantumresistant algorithms, and secure key management. Security Policy (C5): AI-enhanced policy enforcement, adaptive security frameworks, and compliance monitoring. Evaluation Parameters: Threat Intelligence AI analyzes vast datasets to predict, dentify, analyze, and neutralize cyber threats by recognizing attack patterns and vulnerabilities before exploitation. Intrusion Detection and Prevention AI enhances Intrusion Detection Systems (IDS) and Through Intrusion Prevention Methods (IPM) identifying malicious activities and blocking attacks proactively. Malware Detection and Analysis AI-powered cybersecurity solutions detect, classify, and neutralize malware using machine learning algorithms and behavioral analysis. User and Entity Behavior Analytics (UEBA) AI monitors Monitor user behavior to identify abuse, prevent unauthorized access, and detect insider threats enhancing cybersecurity posture. Automated Incident Response AI accelerates cybersecurity responses by automating threat mitigation, reducing human intervention, and minimizing damage from cyberattacks. Results: The study findings indicate that AI significantly improves cybersecurity across all parameters. AI-driven network monitoring (C3) and cryptography (C4) exhibit the highest impact in mitigating threats. Node security (C2) and communication protocols (C1) demonstrate enhanced efficiency in securing endpoints and data exchange. Security policies (C5) benefit from AI-driven automation, ensuring compliance and real-time adaptation to threats. The GRA analysis highlights AI plays a key role in improving cybersecurity resilience and reducing the likelihood of cyber threats attacks.

Advancements of the Internet of Things (IoT) provide several opportunities for automating industrial applications such as smart cities, intelligent logistics, smart grids, smart manufacturers, etc. IoT interconnects a colossal number of heterogeneous devices with the Internet and performs intelligent collaboration with other devices anywhere, anytime, and leads to a technologically optimistic future. Though the IoT has made considerable progress in the industry, there are still a few challenges, including network heterogeneity, network scale, the sheer amount of data generated, and privacy and security, which are considered major concerns in the design of IoT frameworks and architectures. Privacy and security are major concerns in the IoT, where various heterogeneous devices are interconnected in order to communicate with each other. The IoT is facing challenges, which include lack of user awareness, absence of robust and efficient security protocols, and lack of active device monitoring. The IoT is more susceptible to attacks; therefore, to achieve the level of security it requires, various security techniques are needed. In this chapter, various security measures and IoT system backgrounds have been explored. This chapter presents a general survey and analysis of the following things: (i) various security and privacy issues, and (ii) suitable security protocols and models at various IoT layers. This chapter further discussed the potential suitable IoT solutions based on different communication protocols, security mechanisms, cryptography, and intrusion detection systems. A critical analysis is performed and identified the potential research gaps for future work.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The oil and gas industry remains critical to the global economy, as it contributes to the provision of energy and raw materials. Nonetheless, this sector continued to face clear challenges in operational effectiveness, risk and security. Regular tracking methods are limited to latency issues; they are not secure, and data may face integrity issues. To this end, this paper lays out an efficient fog-enabled private blockchain-based identity authentication approach for oil and gas field monitoring. By integrating IoT devices to blockchain, , decentralized control systems are created that enhance security, transparency, and efficient execution of transactions. In this scheme, by making full use of the decentralized structure of blockchain technology and applying the computational power of fog nodes, a secure and efficient identity authentication framework is designed. Fog nodes are an intermediary between IoT devices and blockchain technology, providing lower latency in communication, and therefore more efficient. The main contributions of this paper include: developing a decentralized authentication system based on private blockchains and fog nodes to overcome the drawbacks of centralized models. Create a network model using a private blockchain that dramatically improves feasibility by incorporating strict admission and authorization procedures. Hence, this leads to simultaneous registrations with minimal network time consensus Authentication that incorporating fuzzy extractor to connect the privacy-centric approach and to improve the security analysis and performance evaluation proving that the proposed solution provides better. According to the previous security analysis, it is clear that the scheme conflicts with different types of threats including DoS, MITM attacks, replay, Sybil, and message substitution attacks. The performance evaluation also shows low computational and communication costs, high compatibility, and real-time operation, which indicates that the proposed scheme is effective and can be implemented as a real-time oil and gas field monitoring system.

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction.

Digital fingerprinting is an emerging technology to protect the digital data from illegal redistribution by dishonest customer, where each distributed copy is labeled with unique identification information. Industry design map is one of the most important digital assets for both of the designer and company. However, industry design maps are usually used for unintended purposes by those dishonest customers. Piracy of industry design map becomes increasingly rampant in recent years, since the dishonest customer can easily duplicate, modify and redistribute the received copy. How to effectively protect the costly industry design map from unauthorized using has become increasingly critical, especially considering the ease of manipulating digital industry design map and the open Internet environment. This paper proposed a secure and practical fingerprinting protocol for protecting the copyrighted industry design map. In the proposed scheme, the designer can trace the traitors from a pirated copy by means of the embedded unique fingerprint information, while the customer is immune of being framed due to the asymmetric property.

Edge computing is a promising paradigm to expand the capability of Internet of Things (IoT) devices by computation offloading. To establish a distributed ledger to provide a secure and trusted environment for the resource allocation between edge servers and IoT devices, the emerging blockchain technology has attracted a lot of attention recently. However, in practice, edge resource allocation in IoT devices often involves multi-layer structures, which poses a challenge due to information incompleteness among different layers. Moreover, how to design a suitable and efficient blockchain framework for hierarchical resource allocation markets is a critical issue. In this paper, we apply blockchain to propose a secure and efficient hierarchical resource allocation framework for edge computing. First, we study the edge computing resource allocation problem in the hierarchical market of IoT devices, in which the IoT devices beyond the coverage of Access Points can participate in the resource allocation through middlemen. To solve the problem, a smart contract-based hierarchical auction mechanism is developed. The edge computing resources allocated in the top market can be continually reallocated to the sub-markets based on the mechanism, which then leads an efficient solution that maximizes the social welfare of the whole participants. Moreover, the mechanism is implemented as a smart contract in the blockchain, which enforces the rule of the hierarchical auction in a non-deniable and automated manner. Finally, the extensive simulations demonstrate the correctness and performance of the proposed mechanism.

We present a two-state practical quantum bit commitment protocol, the security of which is based on the current technological limitations, namely the nonexistence of either stable long-term quantum memories or nondemolition measurements. For an optical realization of the protocol, we model the errors, which occur due to the noise and equipment (source, fibers, and detectors) imperfections, accumulated during emission, transmission, and measurement of photons. The optical part is modeled as a combination of a depolarizing channel (white noise), unitary evolution (e.g., systematic rotation of the polarization axis of photons), and two other basis-dependent channels, namely the phase- and bit-flip channels. We analyze quantitatively the effects of noise using two common information-theoretic measures of probability distribution distinguishability: the fidelity and the relative entropy. In particular, we discuss the optimal cheating strategy and show that it is always advantageous for a cheating agent to add some amount of white noise---the particular effect not being present in standard quantum security protocols. We also analyze the protocol's security when the use of (im)perfect nondemolition measurements and noisy or bounded quantum memories is allowed. Finally, we discuss errors occurring due to a finite detector efficiency, dark counts, and imperfect single-photon sources, and we show that the effects are the same as those of standard quantum cryptography.

In this paper we present SNUAGE, a platform-as-a-service security framework for building secure and scalable multi-layered services based on the cloud computing model. SNUAGE ensures the authenticity, integrity, and confidentiality of data communication over the network links by creating a set of security associations between the data-bound components on the presentation layer and their respective data sources on the data persistence layer. SNUAGE encapsulates the security procedures, policies, and mechanisms in these security associations at the service development stage to form a collection of isolated and protected security domains. The secure communication among the entities in one security domain is governed and controlled by a standalone security processor and policy attached to this domain. This results into: (1) a safer data delivery mechanism that prevents security vulnerabilities in one domain from spreading to the other domains and controls the inter-domain information flow to protect the privacy of network data, (2) a reusable security framework that can be employed in existing platform-as-a-service environments and across diverse cloud computing service models, and (3) an increase in productivity and delivery of reliable and secure cloud computing services supported by a transparent programming model that relieves application developers from the intricate details of security programming. Last but not least, SNUAGE contributes to a major enhancement in the energy consumption and performance of supported cloud services by providing a suitable execution container in its protected security domains for a wide suite of energy- and performance-efficient cryptographic constructs such as those adopted by policy-driven and content-based security protocols. An energy analysis of the system shows, via real energy measurements, major savings in energy consumption on the consumer devices as well as on the cloud servers. Moreover, a sample implementation of the presented security framework is developed using Java and deployed and tested in a real cloud computing infrastructure using the Google App Engine service platform. Performance benchmarks show that the proposed framework provides a significant throughput enhancement compared to traditional network security protocols such as the Secure Sockets Layer and the Transport Layer Security protocols.