Internet of Things (IoT)-enabled Smart Grid (SG) network is envisioned as the next-generation network for intelligent and efficient electric power transmission. In SG environment, the Smart Meters (SMs) mostly exchange services and data from Service Providers (SPs) via insecure public channel. This makes the entire SG ecosystem vulnerable to various security threats. Motivated from the aforementioned challenges, we incorporate Digital Twin (DT) technology, Software-Defined Networking (SDN), Deep Learning (DL) and blockchain into the design of a novel SG network. Specifically, a secure communication channel is first designed using an authentication method based on blockchain technology that has the ability to withstand a number of well-known assaults. Second, a new DL architecture that includes a self-attention mechanism, a Bidirectional-Gated Recurrent Unit (Bi-GRU) model, fully connected layers, and a softmax classifier is designed to enhance the attack detection process in SG environments. To deliver low latency and real-time services, the SDN is next employed as the network’s backbone to send requests from SMs to a global SDN controller. DT technology is finally integrated into the SDN control plane, which stores the operating states and behavior models of SMs and communicates with SMs. The efficiency of the proposed framework is demonstrated by the blockchain implementation used in the SG network to assess computing time for the various numbers of transactions per block. Finally, the numerical results based on the N-BaIoT dataset shows better intrusion detection.
The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and organisations. To detect and predict cyber threats effectively and accurately, an intelligent system must be developed. Cybercriminals can exploit Internet of Things devices and endpoints because they are not intelligent and have limited resources. A hybrid decision tree method (HIDT) is proposed in this article that integrates machine learning with blockchain concepts for anomaly detection. In all datasets, the proposed system (HIDT) predicts attacks in the shortest amount of time and has the highest attack detection accuracy (99.95% for the KD99 dataset and 99.72% for the UNBS-NB 15 dataset). To ensure validity, the binary classification test results are compared to those of earlier studies. The HIDT’s confusion matrix contrasts with previous models by having low FP/FN rates and high TP/TN rates. By detecting malicious nodes instantly, the proposed system reduces routing overhead and has a lower end-to-end delay. Malicious nodes are detected instantly in the network within a short period. Increasing the number of nodes leads to a higher throughput, with the highest throughput measured at 50 nodes. The proposed system performed well in terms of the packet delivery ratio, end-to-end delay, robustness, and scalability, demonstrating the effectiveness of the proposed system. Data can be protected from malicious threats with this system, which can be used by governments and businesses to improve security and resilience.
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.
Aiming to safeguard a decentralized setup such as smart cities, collaborative intrusion detection system (CIDS) has become a mainstream security mechanism to protect different types of computer networks, especially decentralized computing platforms such as Internet of Things (IoT). The main benefit of CIDS relies on the information sharing process among devices, nodes, software and hardware entities. However, traditional CIDS often requires a trusted third partner, e.g., a centralized computing server, to help build up a trusted communication channel among various entities. Such requirement is not practical in real-world implementation, making the integrity of shared information compromised easily. With the wide adoption, blockchain technology has given a solution to protect the distributed/collaborative detection system. In the current market, blockchain technology has been extensively researched across many detection scenarios, but there is a need to explore how such technology can overall contribute to CIDS and a general distributed detection system. In this work, we introduce a blockchain-assisted security management framework for CIDS, which summarizes and provides an integrated protection given by blockchain. In the case study , we evaluate our proposed framework in both a simulated and a real CIDS setup with challenge-based mechanism. The results demonstrate the promising benefits provided by blockchain in CIDS.
The Internet of Things (IoT) has become a game-changing technology, bridging the gap between the real and virtual worlds and allowing for smooth data transfer and communication between linked objects. The other two potential technologies are blockchain (BC) and artificial intelligence (AI), whose application areas are incredibly diverse and which may perform best when combined. Since some traditional machine learning (ML) techniques have limitations, this article proposed using distributed machine learning techniques such as federated learning and blockchain to build a more reliable and secure IoT network that will be better protected and less susceptible to outside intrusions. As an alternative to centralized cloud storage, we also recommended using decentralized data storage techniques like the InterPlanetary File System (IPFS) and Hyperledger Fabric (HLF). Additionally, as a proof of concept, we deployed our model using Ethereum Smart Contracts (SC). Using the well-known cybersecurity dataset known as Edge-IIoTset, we utilized both centralized and federated machine learning models to evaluate the efficiency of the suggested approach. The experimental results and successful deployment of Smart Contracts demonstrate that employing Blockchain and distributed storage systems is preferable for safeguarding IoT networks.
Nada Abdu Alsharif, Shailendra Mishra, Mohammed Alshehri
The rise of IoT devices has brought forth an urgent need for enhanced security and privacy measures, as IoT devices are vulnerable to cyber-attacks that compromise the security and privacy of users. Traditional security measures do not provide adequate protection for such devices. This study aimed to investigate the use of machine learning and blockchain to improve the security and privacy of IoT devices, creating an intrusion detection system powered by machine learning algorithms and using blockchain to encrypt interactions between IoT devices. The performance of the whole system and different machine learning algorithms was evaluated on an IoT network using simulated attack data, achieving a detection accuracy of 99.9% when using Random Forrest, demonstrating its effectiveness in detecting attacks on IoT networks. Furthermore, this study showed that blockchain technology could improve security and privacy by providing a tamper-proof decentralized communication system.
Beibei Han, Yingmei Wei, Qingyong Wang, Francesco Maria De Collibus · 5 authors
Abstract In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform the anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated.
Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts.
Smart contract is the building block of blockchain systems that enables automated peer-to-peer transactions and decentralized services. Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. Formal verification and symbolic analysis have been employed to combat these destructive scams by analyzing the codes and function calls, yet each scam's vulnerability should be discreetly predefined. In this work, we introduce ADEFGuard, a new anomaly detection framework based on the behavior of smart contracts, as new features. We design a learning and monitoring module to determine fraudulent smart contract behaviors. Our framework is advantageous over basic algorithms in three aspects. First, ADEFGuard provides a unified solution to different genres of scams, relieving the need for code analysis skills. Second, ADEFGuard's inference is orders of magnitude faster than code analysis. Third, experimental results show that ADEFGuard achieves high accuracy (85%), precision (75%) and recall (90%) for malicious contracts and is potentially useful in detecting new malicious behaviors of smart contracts.
In recent years, with the continuous promotion of blockchain technology, the application of smart contracts has shown an explosive growth trend, and smart contract vulnerabilities seriously threaten the ecological security of blockchain. Aiming at the inefficiency of existing smart contract Symbolic Execution vulnerability detection technology, this paper proposes an effective smart contract vulnerability detection method at the source code level. Firstly, we define the critical path. As attackers typically aim to steal or freeze funds, we define the path related to fund transfer as the critical path, and its related instructions are the critical instructions. Then, we constructed a smart contract control flowchart based on Ethereum bytecode and used a constraint solver to solve path constraints and corresponding vulnerability constraints. Detect common smart contract vulnerabilities such as reentrancy, access control, arithmetic vulnerabilities, unchecked low calls, and denial of service. The experimental results show that the proposed scheme has good detection performance, and vulnerability detection was performed on 55 smart contracts containing vulnerabilities in the dataset. Compared with the pre optimized scheme, the precision rate of this scheme has been improved by 7.52%, and the total execution time has been reduced by 34.92%.
Because of its ability to completely revamp current blockchain security methods, this connection is crucial. An effective safeguard against complex assaults, Nonlinear Dynamics (ND) adds a living, breathing component to consensus methods and cryptographic primitives. There is an urgent need for creative, nonlinear methods to strengthen blockchain security in light of present challenges including increasing attack vectors and risks posed by quantum computing. The suggested Dynamic Chaos-based Blockchain Security (DC-BS) system in this paper makes use of the chaotic dynamics present in ND to strengthen various aspects of blockchain security. Adaptive threat detection systems, dynamic consensus methods, and chaos-based encryption are all newly introduced in DC-BS. Validation of DC-BS's efficacy in preventing various attack scenarios through simulation studies demonstrates its advantages in reducing vulnerabilities and responding to new attack types. Various decentralized systems can benefit from DC-BS, including as supply chain management, the Internet of Things (IoT), conventional blockchain networks, and decentralized finance (DeFi). To strengthen the security of various decentralized applications, DC-BS works to increase trust, transparency, and resilience. The effectiveness of DCBS is confirmed by thorough simulation analyses that cover a wide range of attack scenarios, including double-spending assaults, Sybil attacks, and eclipse attacks. Based on the results of the simulations, DCBS is much more effective than conventional blockchain security procedures at reducing these risks. Showcased as well is the technique's capacity to react to changing attack techniques, highlighting its capacity to provide strong security even in dynamic settings.
Tao Huang, Renchao Xie, Yuzheng Ren, F. Richard Yu · 10 authors
In recent years, the Industrial Internet and Industry 4.0 came into being. With the development of modern industrial intelligent manufacturing technology, digital twins, Web3 and many other digital entity applications are also proposed. These applications apply architectures such as distributed learning, resource sharing, and arithmetic trading, which make high demands on identity authentication, asset authentication, resource addressing, and service location. Therefore, an efficient, secure, and trustworthy Industrial Internet identity resolution system is needed. However, most of the traditional identity resolution systems follow DNS architecture or tree structure, which has the risk of a single point of failure and DDoS attack. And they cannot guarantee the security and privacy of digital identity, personal assets, and device information. So we consider a decentralized approach for identity management, identity authentication, and asset verification. In this paper, we propose a distributed trusted active identity resolution system based on the inter-planetary file system (IPFS) and non-fungible token (NFT), which can provide distributed identity resolution services. And we have designed the system architecture, identity service process, load balancing strategy and smart contract service. In addition, we use Jmeter to verify the performance of the system, and the results show that the system has good high concurrent performance and robustness.
To address the problems that current studies for enhancing network accountability based on IPv6 addresses do not support cross-Autonomous Systems (AS) or restrict threatener behaviors, a distributed IPv6 Address Traceback and Threatener Restriction Mechanism (ATTRM6) based on smart contract is proposed. Tracing servers of each AS form a blockchain and invoke smart contract functions to store address information of different ASes on the blockchain. When IPv6 address traceback is needed across ASes, the traceback server of a specific AS reads addresses information stored on the blockchain to identify the threatener. Considering the restriction scheme for threatener associated with IPv6 addresses, and proposing a Punishment-Forgiveness Policy (PFP) to dynamically adjust the reputation of threatener, and store restricted threatener and their reputation on the blockchain, thus providing data support to each AS to take restriction measures. Compared with information sharing based on a centralized database, the ATTRM6 mechanism can accomplish more reliable sharing. Experimental results show that the ATTRM6 mechanism has low overhead and can effectively perform IPv6 address traceback and threatener restriction.
Viji Rajendran, A. Jasrotia, Ghulam Murtaza, Rohit Sharma
Any democracy must have an open voting<br> process that satisfies the needs of the populace to give the<br> appropriate individual the power. Additionally, the<br> traditional voting systems currently in use have<br> significant flaws and lack security and transparency. It<br> has long been difficult to create a safe electronic voting<br> system that provides the transparency and flexibility<br> provided by electronic systems, while maintaining the<br> fairness and privacy of present voting schemes. In this<br> project, we assess a blockchain-based implementation of<br> distributed electronic voting systems. It addresses some<br> of the well-known blockchain frameworks with the aim<br> of building a blockchain-based electronic voting system<br> and presents a novel electronic voting system based on<br> blockchain that tackles some of the shortcomings in<br> existing systems. In particular, we evaluate the potential<br> of distributed ledger technologies through the<br> description of a case study; namely, the process of an<br> election, and the implementation of a blockchain-based<br> application, which improves the security and decreases<br> the cost of hosting a nationwide election.
Abstract The increasing prominence of cryptocurrencies has brought to the forefront the critical issue of security vulnerabilities, particularly the majority 51% attack. This study addresses the need for benchmarks to distinguish between vulnerable and non-vulnerable cryptocurrencies. A comprehensive literature review reveals a lack of research with the desired statistical rigor in this domain, necessitating the development of a robust model. Drawing upon mathematical modeling, this research fills the gap by proposing a susceptibility test model which incorporates essential parameters identified from literature. The model is validated with additional data to ensure its accuracy and reliability. Furthermore, k-means clustering analysis is employed to determine benchmarking thresholds, allowing for a refined categorization of cryptocurrencies based on their susceptibility levels. The findings of this study reveal five distinct clusters, each representing a unique security profile. Resilience is associated with susceptibility test values less than the critical threshold \(0.532\). In contrast, cryptocurrencies with susceptibility test values greater than 1.557 exhibit alarming vulnerability. In between, three cryptocurrency susceptibility levels are identified, ranging from moderate resilience to high vulnerability. The outcomes of this study serve as a foundation for better-informed investment decisions as well as future research endeavors, informing the development of industry best practices and policy recommendations aimed at strengthening the robustness of cryptocurrencies against malicious activities.
Abdullah Lakhan, Orawit Thinnukool, Tor Morten Groenli, Pattaraporn Khuwuthyakorn
These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security and cyberattacks in practice. Blockchain technology is a promising technology that can process valid and secure remote healthcare data among different clinics. However, ransomware attacks are still complex holes in blockchain technology and prevent many healthcare data transactions during the process on the network. The study presents the new ransomware blockchain efficient framework (RBEF) for digital networks, which can identify transaction ransomware attacks. The objective is to minimize transaction delays and processing costs during ransomware attack detection and processing. The RBEF is designed based on Kotlin, Android, Java, and socket programming on the remote process call. RBEF integrated the cuckoo sandbox static and dynamic analysis application programming interface (API) to handle compile-time and runtime ransomware attacks in digital healthcare networks. Therefore, code-, data-, and service-level ransomware attacks are to be detected in blockchain technology (RBEF). The simulation results show that the RBEF minimizes transaction delays between 4 and 10 min and processing costs by 10% for healthcare data compared to existing public and ransomware efficient blockchain technologies healthcare systems.
As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.
Smart contract vulnerabilities have led to significant financial losses, with their increasing complexity rendering outright prevention of hacks increasingly challenging. This trend highlights the crucial need for advanced forensic analysis and real-time intrusion detection, where dynamic analysis plays a key role in dissecting smart contract executions. Therefore, there is a pressing need for a unified and generic representation of smart contract executions, complemented by an efficient methodology that enables the modeling and identification of a broad spectrum of emerging attacks We introduce C lue , a dynamic analysis framework specifically designed for the Ethereum virtual machine. Central to C lue is its ability to capture critical runtime information during contract executions, employing a novel graph-based representation, the Execution Property Graph. A key feature of C lue is its innovative graph traversal technique, which is adept at detecting complex attacks, including (read-only) reentrancy and price manipulation. Evaluation results reveal C lue ’s superior performance with high true positive rates and low false positive rates, outperforming state-of-the-art tools. Furthermore, C lue ’s efficiency positions it as a valuable tool for both forensic analysis and real-time intrusion detection.
Liwa H. Al-Farhani, Yahya Alqahtani, Hamdan Alshehri, John Martin · 6 authors
The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT.
The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.
Saba Abdulbaqi Salman, Sufyan Al-Janabi, Ali Makki Sagheer
Electronic voting has become popular in democratic countries, and thus the cyber security of this system is demanded. In this paper, some attacks were made on a proposed electronic election model based on blockchain technology, where the impact of each attack (Sybil, DDoS, Eclipse, Selfish mining, 51% attack) was calculated, and the time in which it achieved 51% of the attack was calculated. In this study, we investigate of Blockchain technology’s attack surface, focusing on general blockchains. The following factors show how these attacks have an impact on the proposed model: 1) The cryptographic architecture of the Blockchain. 2) The distributed architecture of systems using Blockchain. 3) The Blockchain application context. For each of these factors, we identify several attacks, including selfish mining, 51% attack, sybil attacks, eclipse attacks, distributed denial-of-service (DDos) attacks, consensus delay (due to selfish behavior or distributed denial-of-service attacks), blockchain forks, orphan blocks, block swallowing, wallet theft, smart contract attacks, and privacy attacks.
Abstract With the high-speed development of decentralized applications, account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value. Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks, which have resulted in a huge loss of crypto assets in recent years. Therefore, it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation. To this end, this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples. Furthermore, this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption. Moreover, three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods. The results demonstrate the high efficiency and generality of the proposed approach. Finally, the lower time consumption and robustness of our method were validated through additional experiments. In conclusion, the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.
In the industrial Internet of things (IIoT), various applications generate a large number of interactions and are vulnerable to various attacks, which are difficult to be monitored in a sophisticated way by traditional network architectures. Therefore, deploying software-defined network (SDN) in IIoT is essential to defend against various attacks. However, SDN has a draw-back: there is a security problem of distributed denial-of-service (DDoS) attacks at the control layer. This paper proposes an effective solution: DDoS detection within the domain using tri-entropy in information theory. The detected attacks are then uploaded to a smart contract in the blockchain, so that the attacks can be quickly cut off even if the same attack occurs in different domains. Experimental validation was conducted under different attack strengths and multiple identical attacks, and the results show that the method has better detection ability under different attack strengths and can quickly block the same attacks.