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As IoT-connected devices, sometimes referred to as the Internet of Things (IoT), continue to proliferate, existing centralized identity management systems struggle in the large scale due to issues with scalability, privacy and security. For these reasons, centralized identity management systems will not meet the requirements of large-scale IoT deployments. In this paper, we suggest a decentralized identity management system to authenticate and authorize IoT devices based on a hybrid blockchain and Zero-Knowledge Proof (ZKP) protocol. The proposed system utilizes decentralized identifiers (DIDs), verifiable credentials (VCs) and a hierarchical web-of-trust structure as part of the identity management process. The identity and credentials can be created and validated in a decentralized manner and locally, using smart contracts and lightweight consensus models such as Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT). The performance evaluation demonstrated the performance in respect of authentication latency businesses managed to get the latency to 250 ms, throughput reaching to 200 messages per second and energy efficiency improved to 300mW/device. Based on the baseline comparisons including PoW, OAuth and Hash-MAC based systems included, the proposed method is scalably better, provides greater security against DDoS and MITM attacks and used less memory. The proposed method yields a robust, fully decentralized identification system for managing IoT identities without requiring a centralized authority, allowing scalable and secure interactions across distributed networks.

Blockchain has made an impact on today’s technology by revolutionizing the financial industry through utilization of cryptocurrencies using decentralized control. This has been followed by extending Blockchain to span several other industries and applications for its capabilities in verification. With the current trend of pursuing the decentralized Internet, many methods have been proposed to achieve decentralization considering different aspects of the current Internet model ranging from infrastructure and protocols to services and applications. This paper investigates Blockchain’s capacities to provide a robust and secure decentralized model for Internet. The paper conducts a critical review on recent Blockchain-based methods capable for the decentralization of the future Internet. We identify and investigate two research aspects of Blockchain that provides high impact in realizing the decentralized Internet with respect to current Internet and Blockchain challenges while keeping various design in considerations. The first aspect is the consensus algorithms that are vital components for decentralization of the Blockchain. We identify three key consensus algorithms including PoP, Paxos, and PoAH that are more adequate for reaching consensus for such tremendous scale Blockchain-enabled architecture for Internet. The second aspect that we investigated is the compliance of Blockchain with various emerging Internet technologies and the impact of Blockchain on those technologies. Such emerging Internet technologies in combinations with Blockchain would help to overcome Blockchain’s established flaws in a way to be more optimized, efficient and applicable for Internet decentralization.

We present a new class of decentralized first-order methods for nonsmooth and stochastic optimization problems defined over multiagent networks. Considering that communication is a major bottleneck in decentralized optimization, our main goal in this paper is to develop algorithmic frameworks which can significantly reduce the number of inter-node communications. Our major contribution is to present a new class of decentralized primal–dual type algorithms, namely the decentralized communication sliding (DCS) methods, which can skip the inter-node communications while agents solve the primal subproblems iteratively through linearizations of their local objective functions. By employing DCS, agents can find an ϵ-solution both in terms of functional optimality gap and feasibility residual in O(1/ϵ) (resp., O(1/ϵ)) communication rounds for general convex functions (resp., strongly convex functions), while maintaining the O(1/ϵ2) (resp., O(1/ϵ)) bound on the total number of intra-node subgradient evaluations. We also present a stochastic counterpart for these algorithms, denoted by SDCS, for solving stochastic optimization problems whose objective function cannot be evaluated exactly. In comparison with existing results for decentralized nonsmooth and stochastic optimization, we can reduce the total number of inter-node communication rounds by orders of magnitude while still maintaining the optimal complexity bounds on intra-node stochastic subgradient evaluations. The bounds on the (stochastic) subgradient evaluations are actually comparable to those required for centralized nonsmooth and stochastic optimization under certain conditions on the target accuracy.

Recent advancements in decentralized finance (DeFi) have resulted in a rapid increase in the use of Automated Market Makers (AMMs) for creating decentralized exchanges (DEXs). In this paper, we organize these developments by treating an AMM as a neoclassical black-box characterized by the conversion of inputs (tokens) to outputs (prices). The conversion is governed by the technology of the AMM summarized by an ‘exchange function’. Various types of AMMs are examined, including: Constant Product Market Makers; Constant Mean Market Makers; Constant Sum Market Makers; Hybrid Function Market Makers; and, Dynamic Automated Market Makers. The paper also looks at the impact of introducing concentrated liquidity in an AMM. Overall, the framework presented here provides an intuitive geometric representation of how an AMM operates, and a clear delineation of the similarities and differences across the various types of AMMs.

The Internet of Things (IoT) has extended the internet connectivity to reach not just computers and humans, but most of our environment things. The IoT has the potential to connect billions of objects simultaneously which has the impact of improving information sharing needs that result in improving our life. Although the IoT benefits are unlimited, there are many challenges facing adopting the IoT in the real world due to its centralized server/client model. For instance, scalability and security issues that arise due to the excessive numbers of IoT objects in the network. The server/client model requires all devices to be connected and authenticated through the server, which creates a single point of failure. Therefore, moving the IoT system into the decentralized path may be the right decision. One of the popular decentralization systems is blockchain. The Blockchain is a powerful technology that decentralizes computation and management processes which can solve many of IoT issues, especially security. This paper provides an overview of the integration of the blockchain with the IoT with highlighting the integration benefits and challenges. The future research directions of blockchain with IoT are also discussed. We conclude that the combination of blockchain and IoT can provide a powerful approach which can significantly pave the way for new business models and distributed applications.

Internet of Things (IoT) networks’ wide range and heterogeneity make them prone to cyberattacks. Most IoT devices have limited resource capabilities (e.g., memory capacity, processing power, and energy consumption) to function as conventional intrusion detection systems (IDSs). Researchers have applied many approaches to lightweight IDSs, including energy-based IDSs, machine learning/deep learning (ML/DL)-based IDSs, and federated learning (FL)-based IDSs. FL has become a promising solution for IDSs in IoT networks because it reduces the overhead in the learning process by engaging IoT devices during the training process. Three FL architectures are used to tackle the IDSs in IoT networks, including centralized (client–server), decentralized (device-to-device), and semi-decentralized. However, none of them has solved the heterogeneity of IoT devices while considering lightweight-ness and performance at the same time. Therefore, we propose a semi-decentralized FL-based model for a lightweight IDS to fit the IoT device capabilities. The proposed model is based on clustering the IoT devices—FL clients—and assigning a cluster head to each cluster that acts on behalf of FL clients. Consequently, the number of IoT devices that communicate with the server is reduced, helping to reduce the communication overhead. Moreover, clustering helps in improving the aggregation process as each cluster sends the average model’s weights to the server for aggregation in one FL round. The distributed denial-of-service (DDoS) attack is the main concern in our IDS model, since it easily occurs in IoT devices with limited resource capabilities. The proposed model is configured with three deep learning techniques—LSTM, BiLSTM, and WGAN—using the CICIoT2023 dataset. The experimental results show that the BiLSTM achieves better performance and is suitable for resource-constrained IoT devices based on model size. We test the pre-trained semi-decentralized FL-based model on three datasets—BoT-IoT, WUSTL-IIoT-2021, and Edge-IIoTset—and the results show that our model has the highest performance in most classes, particularly for DDoS attacks.

Decentralized Finance (DeFi) represents the new generation of blockchain financial services by developing an open-access financial model without banking or lending institution intermediaries. However, DeFi's open feature threatens its security, making it vulnerable and a target for different attack types. In this systematic review, we present the security of DeFi by selecting fifteen studies from 2020 to 2024 to determine and display the security solutions' effectiveness in identifying the attacks, focusing on various DeFi components such as smart contracts, DEX, AMM, governance, AMM-based DEX, and smart contracts with (DEX, Oracle); detecting different kinds of attacks (e.g., price manipulation, Oracle manipulation, flash loan) using detection tools (e.g., DeFort, CRPWarner, FORAY); we find out that 40% of the selected studies focus on Oracle manipulation attack, 33.33% for price manipulation and flash loan attacks separately, followed by 13.33% for (MEV, rug pull, front-running, Token Leakage, and deep logical bugs), 6.67% for (EEV, reentrancy, sandwich, access control, and state derailment defects). We compare the studies based on the attack type that they detected using four state-of-the-art types of research, such as DeFiScope, FlashSyn, SecPLF, and DeFiGuard; this indicates the concentration of the trend studies is on accuracy and combining AI in DeFi security, or aggregating the existing tools with it, giving an overview of DeFi components' security, underlining the gaps in the attack types that future research can address to build more robust, trustworthy, and secure DeFi systems.

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 24.1.0.2603908) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server. However, this approach introduces unique communication security challenges that have yet to be thoroughly addressed in the literature. These challenges primarily originate from the decentralized nature of the aggregation process, the varied roles and responsibilities of the participants, and the absence of a central authority to oversee and mitigate threats. Addressing these challenges, this paper first delineates a comprehensive threat model focused on DFL communications. In response to these identified risks, this work introduces a security module to counter communication-based attacks for DFL platforms. The module combines security techniques such as symmetric and asymmetric encryption with Moving Target Defense (MTD) techniques, including random neighbor selection and IP/port switching. The security module is implemented in a DFL platform, Fedstellar, allowing the deployment and monitoring of the federation. A DFL scenario with physical and virtual deployments have been executed, encompassing three security configurations: (i) a baseline without security, (ii) an encrypted configuration, and (iii) a configuration integrating both encryption and MTD techniques. The effectiveness of the security module is validated through experiments with the MNIST dataset and eclipse attacks.The results showed an average F1 score of 95%, with the most secure configuration resulting in CPU usage peaking at 68% (± 9%) in virtual deployments and network traffic reaching 480.8 MB (± 18 MB), effectively mitigating risks associated with eavesdropping or eclipse attacks.

As the Industrial Internet of Things (IIoT) is one of the emerging trends and paradigm shifts to revolutionize the traditional industries with the fourth wave of evolution or transform it into Industry 4.0. This all is merely possible with the sensor-enabled technologies, e.g., wireless sensor networks (WSNs) in various landscapes, where security provisioning is one of the significant challenges for miniaturized power hungry networks. Due to the increasing demand for the commercial Internet of things (IoT) devices, smart devices are also extensively adopted in industrial applications. If these devices are compromising the date/information, then there will be a considerable loss and critical issues, unlike information compromising level by the commercial IoT devices. So emerging industrial processes and smart IoT based methods in medical industries with state-of-the-art blockchain security techniques have motivated the role of secure industrial IoT. Also, frequent changes in android technology have increased the security of the blockchain-based IIoT system management. It is very vital to develop a novel blockchain-enabled cyber-security framework and algorithm for industrial IoT by adopting random initial and master key generation mechanisms over long-range low-power wireless networks for fast encrypted data processing and transmission. So, this paper has three remarkable contributions. First, a blockchain-driven secure, efficient, reliable, and sustainable algorithm is proposed. It can be said that the proposed solution manages keys randomly by introducing the chain of blocks with less power drain, a small number of cores, will slightly more communication and computation bits. Second, an analytic hierarchy process (AHP) based intelligent decision-making approach for the secure, concurrent, interoperable, sustainable, and reliable blockchain-driven IIoT system. AHP based solution helps the industry experts to select the more relevant and critical parameters such as (reliability in-line with a packet loss ratio), (convergence in mapping with delay), and (interoperability in association with throughput) for improving the yield of the product in the industry. Third, sustainable technology-oriented services are supporting to propose the novel cloud-enabled framework for the IIoT platform for regular monitoring of the products in the industry. Moreover, experimental results reveal that proposed approach is a potential candidate for the blockchain-driven IIoT system in terms of reliability, convergence, and interoperability with a strong foundation to predict the techniques and tools for the regulation of the adaptive system from Industry 4.0 aspect.