There is a growing interest in deploying blockchain, a distributed and decentralized immutable ledger, on alternate form of hardware to improve the performance, security, energy usage and hardware cost of blockchain. However, so far, the success in utilizing alternate form of hardware has not gained popularity due to the limited benefits of using proposed alternate hardware. For example, running blockchain on a dedicated hardware setup that is separate from the primary infrastructure, such as Raspberry Pi, has limited the energy, hardware and efficiency as you need to manage an entirely separate and dedicated hardware with additional setup, wiring and more. A recent advancement in networking brought a new type of network interface card (NIC) called SmartNIC that allows users to run more advanced and customized programs on a NIC. The main set of benefits of SmartNICs include increased programmability, increased security and increased hardware cost and energy efficiency, as it provides additional computing power to a device that already must be present in a server, and freeing the CPUs to perform other important tasks. Given such benefit, this paper focuses on running customized blockchain application, based on Proof of Work mechanism, on the SmartNIC that utilizes all its available resources, which helps in removing the dependency on expensive, dedicated and specialized hardware and at the same time reduce the hardware needs for running blockchain application. The results are promising as the processing time of Proof of Work based blockchain application on a SmartNIC showed up to 72% improvement when the host is experiencing high load, all the while eliminating the need to add miscellaneous hardware dedicated for blockchain.
In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which currently hinder their widespread adoption. This paper introduces HybridChain that combines the advantages of sharded blockchain and DAG distributed ledger, and a consensus algorithm that leverages decentralized learning. Our approach involves validators exchanging perceptions as votes to assess potential conflicts between transactions and the witness set, representing input transactions in the UTXO model. These perceptions collectively contribute to an intermediate belief regarding the validity of transactions. By integrating their beliefs with those of other validators, localized decisions are made to determine validity. Ultimately, a final consensus is achieved through a majority vote, ensuring precise and efficient validation of transactions. Our proposed approach is compared to the existing DAG-based scheme IOTA and the sharded blockchain Omniledger through extensive simulations. The results show that IOTA has high throughput and low latency but sacrifices accuracy and is vulnerable to orphanage attacks especially with low transaction rates. Omniledger achieves stable accuracy by increasing shards but has increased latency. In contrast, the proposed HybridChain exhibits fast, accurate, and secure transaction processing, and excellent scalability.
Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing applications such as Artificial Intelligence (AI)/Machine Learning for Natural Language Processing, Scientific Simulations, and Cryptocurrency Mining have been estimated. In contrast to the bit-level switching, in which transistors achieved energy efficiency due to geometrical scaling, higher energy is expended both at the at the instructions and simulations levels of an application. Additionally, the analysis based on AI/ML Accelerators indicate that changes in architectures using an older semiconductor technology node have comparable energy efficiency with a different architecture using a newer technology. Further comparisons of the energy in computing systems with the thermodynamic and biological limits, indicate that there is a 27–36 orders of magnitude higher energy requirements for total simulation of an application. These energy estimates underscore the need for serious considerations of energy efficiency in computing by including energy as a design parameter, enabling growing needs of compute-intensive applications in a digital world.
Dharminder Chaudhary, M. S. P. Durgarao, Dheerendra Mishra, Saru Kumari
Abstract The Fiat–Shamir with Aborts paradigm of Lyubashevsky introduced efficient lattice based signatures. A lattice‐based signature is used to design post quantum secure blockchain architecture. But, a blockchain is always consisting of multiple nodes, and then we need an efficient aggregate signature to verify nodes in one‐step. We have designed a blockchain using module lattices. Blockchain security relies upon two assumptions, (1) module learning with errors and (2) module short integer solution. The proposed design ensures security against quantum attacks. The design is simulated in ns‐3 to capture latency and throughput, respectively.
Motivated by proof-of-stake (PoS) blockchains such as Ethereum, two key desiderata have recently been studied for Byzantine-fault tolerant (BFT) state-machine replication (SMR) consensus protocols: Finality means that the protocol retains consistency, as long as less than a certain fraction of validators are malicious, even in partially-synchronous environments that allow for temporary violations of assumed network delay bounds. Accountable safety means that in any case of inconsistency, a certain fraction of validators can be identified to have provably violated the protocol. Earlier works have developed impossibility results and protocol constructions for these properties separately. We show that accountable safety implies finality, thereby unifying earlier results.
Patikiri Arachchige Don Shehan Nilmantha Wijesekara, Subodha Gunawardena
Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.
Abstract SDN revolutionises network management by providing a centralised controller that enables flexible and effortless configuration of networks. However, this flexibility also leads to a vulnerability that enables the adversary to trick the security system into allowing the installation of unauthorised flow rules in the switches. Blockchain provides us with a way to protect against malicious tampering with flow rules by storing them in the distributed ledger. In this work, we propose FTISCON, a mechanism to preserve the integrity of the OpenFlow flow table that utilizes blockchain technology. We employ the Ethereum Private Blockchain to implement the proof-of-concept and conduct a comparative analysis of the proposed scheme and existing related schemes, evaluating their performance in terms of delay, computation time, transaction cost, and detection rate. The proposed work is found to perform better in each of these. The study results suggest that the proposed approach offers a practical and efficient remedy to prevent flow modification attacks within SDN networks.
Siben Tian, Fenhua Bai, Tao Shen, Chi Zhang · 5 authors
To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm’s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.
Blockchain and Artificial Intelligence (AI) are two rapidly emerging technologies. The intersection between them typically focuses on areas where the blockchain can improve trust and transparency in the decisions made by AI models. Yet few practical use cases exist that demonstrate where AI can be used to benefit blockchain. In this paper, we identify Non-Fungible Tokens (NFTs) as a concrete example of how techniques developed in AI can enhance the functionality of blockchain systems and enable new use cases. Specifically, we explore how singular value decomposition (SVD), a fundamental tool in the field of machine learning, can be used in several ways to generate composite NFTs. We show how using the SVD to generate NFTs can allow their more efficient storage on public blockchains as low-fidelity thumbnails that can be provably linked to the original full image. Building on this design, we outline how composite NFTs can be constructed by combining different components of an NFT image derived using the SVD. These components can either be made public or kept private to allow for complex functionality in novel NFT protocols.
Ning Lin, Shaocong Wang, Yi Li, Bo Wang · 22 authors
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.
Decentralized distributed systems can provide certain advantages over their centralized counterparts. These include improved fault tolerance and attack resistance due to the elimination of a single point of failure, better censorship resistance and openness because of the absence of a central authority, as well as higher autonomy of the nodes due to self-governance of resources. Distributed ledger and blockchain technologies have played a key role in the increasing adoption of decentralized systems. One of the notable contributions of blockchains to decentralized systems is that they are able to ensure properties such as integrity, immutability, and verifiability even in trust-deficient environments where nodes lack trust in each other. However, ensuring the privacy of users still remains a challenge in decentralized systems where the application requires users to contribute confidential or identifiable information. Privacy is particularly challenging to achieve in trust-deficient environments due to the nodes not being able to rely on fellow nodes for privacy guarantees. My work addresses privacy preservation in trust-deficient decentralized systems. In this habilitation thesis, we include some selected contributions from our work in three broad areas: privacy-preserving decentralized reputation systems, privacy-preserving message routing in decentralized networks, and privacy preservation in decentralized financial networks.The reputation of a user in a distributed system may be computed as an aggregate of the feedback provided by fellow users. Privacy-preserving reputation systems enable users to provide feedback in a private and thus uninhibited manner. We present several contributions in this area, which include a decentralized privacy-preserving reputation protocol based on the computation of mean that is secure under the malicious adversarial model. The protocol offers significant improvement in performance as compared to earlier protocols in the literature. Additionally, we define an attack called Reputation-based Re-identification (RR attack), which can link successive contributions provided by a participant in participatory sensing applications and subsequently re-identify them. We then propose PrivaSense, a privacy-preserving reputation system that defends against this attack. Experiments on a real dataset demonstrate that PrivaSense is successful in decreasing the number of contributions linked to their providers. Furthermore, we propose a voting protocol (as a generalized instance of a privacy-preserving reputation protocol) that ensures transparency, confidentiality, and integrity in a decentralized trust-deficient setup. The persistence and immutability of the protocol's communication allow verifiability of the outcome by the voters themselves. Our contribution on secure voting is further extended by our work on collusion-resistant worker set selection. We propose protocols that select a subset of workers (who process data during the protocol execution) from the set of participants such that the risk of collusion between workers is minimized.Mobile Delay Tolerant Networks (MDTNs) are composed of mobile devices that communicate in a decentralized manner without the help of fixed infrastructure. A prediction-based routing protocol for MDTNs functions by forwarding a message from one intermediate node to another if the latter has higher probability of encountering the destination node. However, this process compromises the privacy of the nodes by revealing their mobility patterns. We propose the Privacy-Preserving Probabilistic Prediction-based Routing (4PR) protocol that forwards messages by comparing information about communities of nodes instead of individual nodes in order to protect their privacy. The protocol computes a probability function in a decentralized privacy-preserving manner. Simulations on a community-based mobility model demonstrate that our protocol is able to preserve privacy while offering performance comparable to protocols that do not protect privacy.Many Decentralized Finance (DeFi) Peer-to-Peer (P2P) lending platforms offer users to obtain a loan by committing a collateral or by calculating a credit score. However, the requirements of collateral and credit history are quite burdensome for certain groups of users. We propose to use a social trustworthiness score drawn from users' social interactions as an alternative risk mitigator for lending instead of collateral. Privacy considerations are taken into account in order to protect the borrower's privacy despite the use of social interaction data. Another application that we address is Supplier Impersonation Fraud (SIF) detection in the Business-to-Business (B2B) context. This type of fraud occurs when a company supplying goods and services to another company is impersonated by a fraudster in order to trigger a payment to an illegitimate bank account. We introduce GraphSIF, a SIF detection system that aims to infer knowledge from the relational properties created by the transactions of a company. GraphSIF analyzes data that has been heavily anonymized in order to preserve the privacy of the participating companies. The classification of a targeted transaction is performed by first clustering the graphs, and then comparing the similarity between the targeted transaction's graph with the other graphs of its cluster. The model shows good efficiency in terms of computational time needed to create the behavior sequence and to classify the transactions.
For a comprehensive understanding of blockchain’s rising popularity and its potential to replace the current web2, it is crucial to grasp how this technology functions and the skills required by professionals sought after by companies. This paper explores the interplay between three key blockchain applications: Non-Fungible Tokens (NFTs), stablecoins, and decentralized exchanges (DEXs). It highlights their functionalities and demonstrates their combined potential in creating a secure, transparent, and efficient marketplace for digital assets. Additionally, the paper delves into blockchain architecture, popular networks, and cryptocurrencies, showcasing the importance of blockchain in fostering trust, transparency, and security.
Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Elias Kougianos, Vasanth Iyer · 5 authors
This work presents a sustainable cybersecurity solution using Physical Unclonable Functions (PUF), Trusted Platform Module (TPM), and Tangle Distributed Ledger Technology (DLT) for sustainable device and data security. Security-by-Design (SbD) or Hardware- Assisted Security (HAS) solutions have gained much prominence due to the requirement of tamper-proof storage for hardwareassisted cryptography solutions. Designing complex security mechanisms can impact their efficiency as IoT applications are more decentralized. In the proposed architecture, we presented a novel TPM-enabled PUF-based security mechanism with effective integration of PUF with TPM. The proposed mechanism is based on the process of sealing the PUF key in the TPM, which cannot be accessed outside the TPM and can only be unsealed by the TPM itself. A specified NV-index is assigned to each IoT node for sealing the PUF key to TPM using the Media Access Control (MAC) address. Access to the TPM's Non-Volatile Random Access Memory (NVRAM) is defined by the TPM's Enhanced Authorization policies as specified by the Trust Computing Group (TCG). The proposed architecture uses Tangle for sustainable data security and storage in decentralized IoT systems through a Masked Authentication Messaging (MAM) scheme for efficient and secure access control to Tangle. We validated the proposed approach through experimental analysis and implementation, which substantiates the potential of the presented PUFchain 4.0 for decentralized IoT-driven security solutions.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Advanced Memory and Neural Computing
Advanced Steganography and Watermarking Techniques
This paper introduces the Proof of Opinion (PoO) consensus algorithm as an alternative to the traditional Proof of Work (PoW) and Proof of Stake (PoS) algorithms. PoO is designed to address the issues of energy efficiency, decentralization, privacy protection, scalability, and incentivization that are associated with PoW and PoS. PoO relies on the collective opinion of network nodes to reach consensus, which reduces the computational and energy requirements compared to PoW and PoS. PoO can be customized to provide a greater degree of privacy protection, such as by enabling private transactions and hiding transaction details from other nodes. PoO is designed to be more scalable than PoW and PoS, as it relies on the collective opinion of nodes, which reduces the computational requirements and enables greater scalability. PoO’s reputation-based approach incentivizes nodes to act honestly and perform their duties diligently, which promotes network security, stability, and better consensus decision-making. Our simulation results show that PoO can achieve comparable performance to PoW and PoS while consuming significantly less energy. PoO also provides greater decentralization, privacy protection, and scalability compared to PoW and PoS.
With the arrival of the 5G era, the Internet of Things (IoT) has entered a new stage, and the amount of IoT data is growing rapidly. The traditional blockchain cannot handle massive amounts of data, which presents scalability challenges for blockchain technology. Existing blockchain improvement technologies such as off-chain payments, protocol improvements, and sharding techniques have performance bottlenecks and limitations in the data, which is rapidly growing. The blockchain is fundamentally a decentralized distributed ledger, and the traditional chain structure is inadequate for addressing concerns such as forks, double-spending attacks, and other factors in the current IoT landscape. In this paper, we propose a high-throughput distributed ledger based on Directed Acyclic Graph (DAG) named TEEDAG. We design a consensus algorithm based on self-referencing parallel chains combined with Trusted Execution Environment (TEE) to ensure the security of the consensus process. The experiment proves that TEEDAG demonstrates a significantly higher throughput compared to traditional blockchain solutions and offers improved security and efficiency compared to existing DAG-based distributed ledger solutions.
Maytham S. Jabor, Aqeel Salman Azez, J.C. Campelo, Alberto Bonastre
Nowadays, Wireless Sensor Networks (WSNs) are widely used for collecting, communicating, and sharing information in various applications. Due to its limited resources in terms of computation, power, battery lifetime, and memory storage for sensor nodes, it is difficult to add confidentiality and integrity security features. It is worth noting that blockchain (BC) technology is one of the most promising technologies, because it provides security, avoids centralization, and a trusted third party. However, to apply BCs in WSNs is not an easy task because BC is typically resource-hungry for energy, computation, and memory. In this paper, the additional complication of adding BC in WSNs is compensated by an energy minimization strategy, which basically depends on minimizing the processing load of generating the blockchain hash value, and encrypting and compressing the data that travel from the cluster-heads to the base station to reduce the overall traffic, leading to reduced energy per node. A specific (dedicated) circuit is designed to implement the compression technique, generate the blockchain hash values and data encryption. The compression algorithm is based on chaotic theory. A comparison of the power consumed by a WSN using a blockchain implementation with and without the dedicated circuit, illustrates that the hardware design contributes considerably to reduce the consumption of power. When simulating both approaches, the energy consumed when replacing functions by hardware decreases up to 63%.
Physical terminals provide network services to upper-layer applications, but their limited memory and processing power make it challenging to perform security updates and patches, leaving them vulnerable to known security threats. Attackers can exploit these weaknesses to control the terminals and attack the network. To restrict unauthorized access to the network and its resources, appropriate access control mechanisms are necessary. In this paper, we propose a fine-grained access control method based on smart contracts (FACSC) for terminals in software-defined networking (SDN). FACSC utilizes the attribute-based access control (ABAC) model to achieve fine-grained control over terminal access networks. To ensure the security and reliability of access control policies and terminal-related attribute information, we utilize smart contract technology to implement the ABAC model. Furthermore, we leverage the programming protocol-independent packet processor (P4) to filter and forward packets in the data plane based on the packet option field, enabling rapid terminal access. Experimental results show that our proposed method achieves fine-grained secure authentication of terminals in SDN networks with a low authentication processing overhead.
Distributed ledger technologies (DLT) usage is currently limited to a single platform as they do not have design-based interoperability capabilities. In general, it's challenging for a DLT to communicate with another one. Although several DLT solutions have been proposed and applied in specific application areas, building a general-purpose interoperability solution for any DLT remains a challenge. In previous work, we proposed a tailor-made interoperability solution between Hyperledger Fabric and Corda. This paper extends that work to enable interoperability between Hyperledger Fabric and Ethereum. The main contribution of this paper is to provide a new approach to enable interoperability between these two DLT. The approach follows a new request-response interaction model and future payments to enable the payments of services. A prototype was developed and evaluated through a case scenario, performance tests and cost analysis. Performance tests showed bottlenecks under heavy load scenarios due to Ethereum's design. Costs analysis showed that the approach is suitable for purchasing high-priced services. These promising results constitute a step forward in developing a general-purpose solution for DLT interoperability.
The robustness of a blockchain against the adversary is often characterized by the maximum fraction (fmax) of adversarial power that it can tolerate. While most existing blockchains can only tolerate ${f_{\max }} < \frac{1}{2}$ or lower, there are some blockchain systems that are able to tolerate a malicious majority, namely ${f_{\max }} \geq \frac{1}{2}$. A key price paid by such blockchains, however, is their large confirmation latency. This work aims to significantly reduce the confirmation latency in such blockchains, under the common case where the actual fraction f of adversarial power is relatively small. To this end, we propose a novel blockchain called Flint. Flint tolerates ${f_{\max }} \geq \frac{1}{2}$ and can give optimistic execution (i.e., fast confirmation) whenever f is relatively small. Our experiments show that the fast confirmation in Flint only takes a few minutes, as compared to several hours of confirmation latency in prior works.
The metaverse, as an emerging technical term, conceptually aims to construct a virtual digital space that runs parallel to the physical world. Due to human behaviors and interactions being represented in the virtual world, security in the metaverse is a challenging issue in which the traditional centralized service model is one of the threat sources. To conquer the obstacle caused by centralized computing, blockchain-based solutions are potential problem-solving methods. However, it is difficult for a single blockchain to support large-scale data and business services in the metaverse, due to the scalability restrictions. Moreover, multi-chain settings also encounter the interoperability issues. In this work, we propose a Relay chain and Asynchronous consensus-based Consortium blockchain cross-Chain model, which realizes message transmission and cross-chain transactions in multiple chains by adopting the relay chain and cross-chain gateways. All nodes of the application chains and the relay chain execute cross-chain transactions in sequence and reach a consensus on transactions at any transmission delay. Our experiment evaluations demonstrate that our approach performs well in atomicity, security, and functionality (cross-chain transactions), such that the performance of blockchain scalability in the metaverse can be improved, compared with the traditional relay chain schemes.
To mine a blockchain on IP Networks, one must do several tasks related to chain management, rule optimization, verification, and hash generation design. Various consensus model subsets may benefit from the various blockchain mining techniques proposed by researchers. Most of these techniques, however, are rather complicated, which slows down the mining process for large-scale blockchains. Overly simplistic models that include unnecessary redundancies are inefficient and have little practical use. To solve these issues and boost blockchain mining efficiency in large-scale deployments, the authors of this paper propose creating a novel hybrid bioinspired approach. The proposed IP Network model is adaptable to almost all consensus procedures and may be easily combined with dynamic consensus models with few alterations. After collecting performance and context-specific data from the underlying blockchains, the technique uses Genetic Algorithm (GA) that distributes these range sets among miner nodes that support trust, allowing for high-performance mining while maintaining a high degree of trust under actual application situations. The model was tested against Proof-of-Stake (PoS), Proof-of- Work (PoW), Proof-of- Trust (PoT), and Practical Byzantine Fault Tolerance (PBFT) based consensus algorithms to ensure its effectiveness in real-world scenarios. Mining latency, energy consumption, and computational complexity were used as metrics against which this performance was measured. This analysis revealed that the proposed model has the potential to decrease mining latency by 4.5%, energy usage by 3.9%, and compute complexity by 4.1% across a variety of consensus mechanisms, making it suitable for a number of real-time applications.
Ali Nikhalat-Jahromi, Ali Mohammad Saghiri, Mohammad Reza Meybodi
Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.