Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G$-invariant architecture for a given problem? Before we can consider the optimization problem itself, we must understand the search space, the architectures in it, and how they relate to one another. In this paper, we take a first step towards this goal; we prove a theorem that gives a classification of all $G$-invariant single-hidden-layer or ``shallow'' neural network ($G$-SNN) architectures with ReLU activation for any finite orthogonal group $G$, and we prove a second theorem that characterizes the inclusion maps or ``network morphisms'' between the architectures that can be leveraged during neural architecture search (NAS). The proof is based on a correspondence of every $G$-SNN to a signed permutation representation of $G$ acting on the hidden neurons; the classification is equivalently given in terms of the first cohomology classes of $G$, thus admitting a topological interpretation. The $G$-SNN architectures corresponding to nontrivial cohomology classes have, to our knowledge, never been explicitly identified in the literature previously. Using a code implementation, we enumerate the $G$-SNN architectures for some example groups $G$ and visualize their structure. Finally, we prove that architectures corresponding to inequivalent cohomology classes coincide in function space only when their weight matrices are zero, and we discuss the implications of this for NAS.
Blockchain technology is impacting several industries, including the creative industries and those operating in the Internet of Things (IoT). Lately, researchers' attention has been devoted to the application of blockchain in the recorded music industry. However, thus far, no research has investigated the use of such technology in the Internet of Musical Things (IoMusT). The IoMusT is a new area emerging in industry and academy as an extension of the IoT to the musical domain. The IoMusT itself, as the IoT, is a distributed network of musical things, which are objects augmented with information and communication technologies serving a musical purpose. The IoMusT vision requires, above all, IoT features such as decentralization, seamless authentication, transparency, data integrity and privacy, and self-maintenance, as well as the musical domain features such as efficient handling of copyrights and speed of royalties payment. Such features can be brought by blockchain. In this paper, we investigate the integration of blockchain technology with the IoMusT, and we name such synthesis “Blockchain-based IoMusT”. We present a vision for this new paradigm in terms of the novel opportunities that are enabled, and we propose a set of application scenarios enabled by technological integration. Finally, we outline the open research directions in this promising area.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
After the success of the Bitcoin blockchain, came several cryptocurrencies and blockchain solutions in the last decade. Nonetheless, Blockchain-based systems still suffer from low transaction rates and high transaction processing latencies, which hinder blockchains' scalability. An entire class of solutions, called Layer-1 scalability solutions, have attempted to incrementally improve such limitations by adding/modifying fundamental blockchain attributes. Recently, a completely different class of works, called Layer-2 protocols, have emerged to tackle the blockchain scalability issues using unconventional approaches. Layer-2 protocols improve transaction processing rates, periods, and fees by minimizing the use of underlying slow and costly blockchains. In fact, the main chain acts just as an instrument for trust establishment and dispute resolution among Layer-2 participants, where only a few transactions are dispatched to the main chain. Thus, Layer-2 blockchain protocols have the potential to transform the domain. However, rapid and discrete developments have resulted in diverse branches of Layer-2 protocols. In this work, we systematically create a broad taxonomy of such protocols and implementations. We discuss each Layer-2 protocol class in detail and also elucidate their respective approaches, salient features, requirements, etc. Moreover, we outline the issues related to these protocols along with a comparative discussion. Our thorough study will help further systematize the knowledge dispersed in the domain and help the readers to better understand the field of Layer-2 protocols.
Memory in biological beings is as complex as the rational complexity of that concrete being requires. Clearly, memory helps to conform knowledge bases to serve the needs of the specific natural being. To analogize from Robotics concepts, it seems that the degrees of freedom in the biological being’s memory are higher or lower depending upon the rationality of each living being. Robots and artificial systems appear to require analogous structures. That is, to build a reactive system, the requirement of memory is not highly demanding with respect to the degrees of freedom. However, the required degrees of freedom seems to grow as the ability of the artificial system to deliberate increases. Consequently, to design artificial systems that would implement cognitive abilities, it is required to rethink memory structures. When designing a Cognitive Artificial System, memory systems should be thought of as highly accessible discrete units. In addition, these systems would require designs in the form of distributed architectures with non-linear features, such as those of human thought. In addition, they should allow for complex mixed types of data (text, images, time or so). Blockchain has attracted great interest for a few years now, especially since the appearance of Bitcoin. A blockchain is a distributed ledger that combines an append-only data structure designed to be resistant to modifications, with a consensus protocol [ 1 , 2 ]. This innovation can be thought of as a sequence of containers, the blocks, that store two things: the information of a “system” and the “service” that such system provides [ 2 ], and it provides an interesting starting point to rethink memory systems in robots.
Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many computational nodes installed in the blockchain competing to approve the records by just solving a meaningless puzzle. Here, we pose proof-of-work as a reinforcement-learning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment's state, whereas a new block is added and verified. Specifically, we design the block verification and consensus mechanism as a deep reinforcement-learning iteration process. As a result, our method utilizes the determination of state transition and the randomness of action selection of a Markov decision process, as well as the computational complexity of a deep neural network, collectively to make the blocks not easy to recompute and to preserve the order of transactions, while the blockchain nodes are exploited to train the same deep neural network with different data samples (state-action pairs) in parallel, allowing the model to experience multiple episodes across computing nodes but at one time. Our method is used to design the next generation of public blockchain networks, which has the potential not only to spare computational resources for industrial applications but also to encourage data sharing and AI model design for common problems.
As a result of the increasingly pervasive deployment of the Internet of Things(IoT), the cybersecurity of IoT has already attracted more and more research efforts. Identity management is believed to be the fundamental keystone to build security mechanisms. The traditional centralized identity management scheme suffers from a single point of failure and identity forgery. A secure IoT system framework was proposed leveraging blockchain as the basic infrastructure with Physical Unclonable Functions (PUFs) identifying the sensors uniquely. We brought up a scheme to improve the identity authentication protocol. Experiments showed that our approach was more effective and secure against attacks.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
This paper provides an overview of blockchain technology’s security and privacy features, as well as an overview of IoT-based cache memory and single-bit six transistor static random-access memory cell sense amplifier architecture. Each chip’s memory is used for recorded as blocks, which are encrypted and used as a blockchain for other memory devices. The architectures comprise of the circuit of write driver, six transistor static random access memory cells, and sense amplifiers such as current differential sense amplifier, charge transfer differential sense amplifier, and voltage latch sense amplifier. Furthermore, different parameters such as the number of transistors, sensing delay, and power consumption have been analyzed for varying resistance values (i.e., R=$42.3\Omega $and R=42.3K$\Omega$). Apart from that, power reduction techniques such as dual sleep, forced stack, sleep transistor, and sleep stack are used to optimize power consumption. These power reduction techniques are applied over different blocks of architecture, such as six transistors static random access memory cell and sense amplifier to optimize power consumption of the architecture. The conclusion arises that a single-bit six transistor static random access memory cell with power reduction dual sleep technique voltage latch sense amplifier with power reduction dual sleep technique in architecture consumes$11.65~\mu \text{W}$of power and has 33 transistors which are lowest from other architectures.
Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (<i>IoT</i>). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (<i>Multizone-DAG-Blockchain</i>) framework is proposed for the fog-based IoT environment. In this context, fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes, thus preserving the energy consumption of the IoT devices. Both IoT and fog nodes are initially authenticated using a non-cloneable physical function- based validation mechanism (<i>DPUF-VM</i>) in which multiple authentication certificates are verified in the blockchain. Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q- Curve algorithm. In the cloud, sensitive data is stored as ciphertext. Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes. The fog node first performs a redundancy analysis using the Jaccard Similarity (JS) measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network (<i>N2IN</i>) algorithm. A lightweight proof-of-authentication (<i>PoAh</i>) algorithm is presented and executed by the optimal consensus node selected by the bi- objective spiral optimization (<i>BoSo</i>) algorithm for transaction validation. The proposed work is modeled in Network Simulator 3.26 (ns-3.26), and the performance is evaluated in terms of energy consumption, storage cost, response time, and throughput.
Abdullah Ayub Khan, Asif Ali Laghari, Aftab Ahmed Shaikh, Mazhar Ali Dootio · 6 authors
A brain-computer interface (BCI) affords real-time communication, significantly improving the quality of lifecycle, brain-to-internet (B2I) connectivity, and communication between the brain and external digital devices. This assistive technology innovates information and communication development paradigms, such as directly connecting the brain and multimedia devices to the cyber world. The system converts brain information to understandable signals for multimedia devices without physical interference and replaces human-based languages with the external environment control protocols. This advancement challenges and limits security severely. For this reason, the rate of attacks, malware, ransomware, and other types of vulnerabilities is increasing drastically. Another reason is the need to improve traditional procedures to investigate cyberenvironment security aspects. Also, these malicious attackers' prime objective is to harm personal information, enable content security and privacy protocols and physical systems integrity, and create high risk between system and consumers. However, security's capital importance stems from the growing number of wearables (on-body) and in-body wireless devices. These limitations affect personal and healthcare wireless networks during the communication (such as on-chain and off-chain) between human and wearable sensors (sense and transmit) and actuators. This paper presents a novel, secure Blockchain Security Module (BSM) for BCI with Multimedia Life Cycle Framework (MLCF) (BSM-BCIMLCF) that safely connects wearables while investigating the present-day BCI life cycle (BCILC) protection. It homogenizes a Blockchain-based distributed permission network approach to overcome existing challenges. The Blockchain enables assistant cybersecurity for BCI distributed applications to identify brain operations in real-time.
The advent of neural network (NN) based deep learning, especially the recent development of the automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, in order to generate a new consensus block, Proof of Work (PoW) based blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes but to solving a difficult cryptographic hash puzzle problem.In this study, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for block consensus toward optimization of neural networks. In our design, the training and testing data are released to the entire blockchain network and the consensus nodes train NN models on the data, which serves as the proof of learning. As a core component of PoLe, we design a secure mapping layer (SML) to prevent consensus nodes from cheating, which can be straightforwardly implemented as a linear NN layer. When the consensus on the blockchain network is achieved, a new block is appended to the blockchain. We experimentally compare the PoLe protocol with Proof of Work (PoW) and show that PoLe can achieve a more stable block generation rate, which leads to more efficient transaction processing. Experimental evaluation also shows the PoLe can achieve a stable block generation rate without significantly sacrificing training performance.
Hai Nam Nguyen, Hai Anh Tran, Scott Fowler, Sami Souihi
Abstract Software‐Defined Networking (SDN) brought a groundbreaking idea to facilitate network system management by decoupling and abstracting the Control plane and Data plane of traditional networks. The centralised control offers network administrators many benefits such as a global view of the network, programmability, dynamic updating of forwarding rules, and software‐based traffic analysis. The SDN architecture has been applied a lot in practice, and especially in Internet of Things (IoT) platforms. With the superiority of SDN, IoT devices can be managed and configured much more easily when combined. However, SDN also raises many challenges in terms of scalability, reliability, and security. Blockchain is another promising solution for secure information storage and transmission technology that operates without a centralised authority. Applying Blockchain technology into SDN can address some of the current issues of SDN by providing decentralised methods to authenticate exchanged network information. This study provides a comprehensive survey on Blockchain technologies applied to SDN in both security and non‐security fields. First, related studies and an overview of SDN and the background of Blockchain technology are presented. Then, the authors review how Blockchain technologies are applied in SDN from two perspectives: non‐security and security‐aware approaches. Finally, challenges and broader perspectives are discussed.
Aug 19, 2021·2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Shiv Sondhi, Sherif Saad, Kevin Shi, Mohammad Abdullah Al Mamun · 5 authors
A critical component of any blockchain or distributed ledger technology (DLT) platform is the consensus algorithm. Blockchain consensus algorithms are the primary vehicle for the nodes within a blockchain network to reach an agreement. In recent years, many blockchain consensus algorithms have been proposed mainly for private and permissioned blockchain networks. However, the performance of these algorithms and their reliability in hostile environments or the presence of byzantine and other network failures are not well understood. In addition, the testing and validation of blockchain applications come with many technical challenges. In this paper, we apply chaos engineering and testing to understand the performance of consensus algorithms in the presence of different loads, byzantine failure and other communication failure scenarios. We apply chaos engineering to evaluate the performance of three different consensus algorithms (PBFT, Clique, Raft) and their respective blockchain platforms. We measure the blockchain network's throughput, latency, and success rate while executing chaos and load tests. We develop lightweight blockchain applications to execute our test in a semi-production environment. Our results show that using chaos engineering helps understand how different consensus algorithms perform in a hostile or unreliable environment and the limitations of blockchain platforms. Our work demonstrates the benefits of using chaos engineering in testing complex distributed systems such as blockchain networks.
Tooba Faisal, Mischa Döhler, Simone Mangiante, Diego López
Infrastructure sharing is a widely discussed and implemented approach and is successfully adopted in telecommunications networks today. In practice, it is implemented through prior negotiated Service Level Agreements (SLAs) between the parties involved. However, it is recognised that these agreements are difficult to negotiate, monitor and enforce. For future 6G networks, resource and infrastructure sharing is expected to play an even greater role. It will be a crucial technique for reducing overall infrastructure costs and increasing operational efficiencies for operators. More efficient SLA mechanisms are thus crucial to the success of future networks. In this work, we present "BEAT", an automated, transparent and accountable end-to-end architecture for network sharing based on blockchain and smart contracts. This work focuses on a particular type of blockchain, Permissioned Distributed Ledger (PDL), due to its permissioned nature allowing for industry-compliant SLAs with stringent governance. Our architecture can be implemented with minimal hardware changes and with minimal overheads.
Recently, the trending 5G technology encourages extensive applications of on-device machine learning , which collects user data for model training. This requires cost-effective techniques to preserve the privacy and the security of model training within the resource-constrained environment. Traditional learning methods rely on the trust among the system for privacy and security. However, with the increase of the learning scale, maintaining every edge device’s trustworthiness could be expensive. To cost-effectively establish trust in a trustless environment, this paper proposes democratic learning (DemL), which makes the first step to explore hardware/software co-design for blockchain-secured decentralized on-device learning. By utilizing blockchain’s decentralization and tamper-proofing, our design secures AI learning in a trustless environment. To tackle the extra overhead introduced by blockchain , we propose PoMC (an algorithm and architecture co-design) as a novel blockchain consensus mechanism , which first exploits cross-domain reuse (AI learning and blockchain consensus) in AI learning architecture. Evaluation results show our DemL can protect AI learning from privacy leakage and model pollution, and demonstrated that privacy and security come with trivial hardware overhead and power consumption (2%). We believe that our work will open the door of synergizing blockchain and on-device learning for security and privacy.
Javier Arcenegui, Rosario Arjona, Roberto Román, Iluminada Baturone
Non-fungible tokens (NFTs) are widely used in blockchain to represent unique and non-interchangeable assets. Current NFTs allow representing assets by a unique identifier, as a possession of an owner. The novelty introduced in this paper is the proposal of smart NFTs to represent IoT devices, which are physical smart assets. Hence, they are also identified as the utility of a user, they have a blockchain account (BCA) address to participate actively in the blockchain transactions, they can establish secure communication channels with owners and users, and they operate dynamically with several modes associated with their token states. A smart NFT is physically bound to its IoT device thanks to the use of a physical unclonable function (PUF) that allows recovering its private key and, then, its BCA address. The link between tokens and devices is difficult to break and can be traced during their lifetime, because devices execute a secure boot and carry out mutual authentication processes with new owners and users that could add new software. Hence, devices prove their trusted hardware and software. A whole demonstration of the proposal developed with ESP32-based IoT devices and Ethereum blockchain is presented, using the SRAM of the ESP32 microcontroller as the PUF.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Haris Javaid, Ji Yang, Nathania Santoso, Mohit Upadhyay · 7 authors
In this paper, we demonstrate how Hyperledger Fabric, one of the most popular permissioned blockchains, can benefit from network-attached acceleration. The scalability and peak performance of Fabric is primarily limited by the bottlenecks present in its block validation/commit phase. We propose Blockchain Machine, a hardware accelerator coupled with a hardware-friendly communication protocol, to act as the validator peer. It can be adapted to applications and their smart contracts, and is targeted for a server with network-attached FPGA acceleration card. The Blockchain Machine retrieves blocks and their transactions in hardware directly from the network interface, which are then validated through a configurable and efficient block-level and transaction-level pipeline. The validation results are then transferred to the host CPU where non-bottleneck operations are executed. From our implementation integrated with Fabric v1.4 LTS, we observed up to 12x speedup in block validation when compared to software-only validator peer, with commit throughput of up to 68,900 tps. Our work provides an acceleration platform that will foster further research on hardware acceleration of permissioned blockchains.
Yao Du, Shuxiao Miao, Zitian Tong, Victoria L. Lemieux · 5 authors
Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence.
Abstract If blockchain networks are to become the building blocks of the infrastructure for the future digital economy, then several challenges related to the resiliency and survivability of blockchain networks need to be addressed. The survivability of a blockchain network is influenced by the diversity of its nodes. Trustworthy device-level attestations permits nodes in a blockchain network to provide truthful evidence regarding their current configuration, operational state, keying material and other system attributes. In the current work we review the recent developments towards a standard attestation architecture and evidence conveyance protocols. We explore the applicability and benefits of a standard attestation architecture to blockchain networks. Finally, we discuss a number of open challenges related to node attestations that has arisen due to changing model of blockchain network deployments, such as the use of virtualization and containerization technologies for nodes in cloud infrastructures.
With the advent of 5G verticals and the Internet of Things paradigm, Edge Computing has emerged as the most dominant service delivery architecture, placing augmented computing resources in the proximity of end users. The resource orchestration of edge clouds relies on the concept of network slicing, which provides logically isolated computing and network resources. However, though there is significant progress on the automation of the resource orchestration within a single cloud or edge cloud datacenter, the orchestration of multi-domain infrastructure or multi-administrative domain is still an open challenge. Towards exploiting the network service marketplace at its full capacity, while being aligned with ETSI Network Function Virtualization architecture, this article proposes a novel Blockchain-based service orchestrator that leverages the automation capabilities of smart contracts to establish cross-service communication between network slices of different tenants. In particular, we introduce a multi-tier architecture of a Blockchain-based network marketplace, and design the lifecycle of the cross-service orchestration. For the evaluation of the proposed approach, we set up cross-service communication in an edge cloud and we demonstrate that the orchestration overhead is less than other cross-service solutions.
In the blockchain, the transaction hashes are implemented through public-key cryptography and hash functions. Hence, there is a possibility for the two users to choose the same private key knowingly or unknowingly. Even the intruders can follow the particular user's bitcoin transaction, and they can masquerade as that user by generating the private and public key pairs of him. If it happens, the user may lose his transaction. Generally, bitcoin technology uses random numbers from 1 to 2256. It is a wide range, but for a greater number of users, there should be one another solution. There is a possibility of digital prototyping which leads to the loss of more accounts. This chapter provides the device-specific fingerprint technology known as physical unclonable function (PUF) to be employed for authentication in a blockchain-based bitcoin environment. The random unique response from PUF ensures correct transaction. In this chapter, a new tetrahedral oscillator PUF has been introduced intrinsically. All the blockchain operations are carried out and verified with PUF response.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security