With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models.
Nowadays, Internet of Things platforms are being deployed in a wide range of application domains. Some of these include use cases with security requirements, where the data generated by an IoT node is the basis for making safety-critical or liability-critical decisions at system level. The challenge is to develop a solution for data exchange while proving and verifying the authenticity of the data from end-to-end. In line with this objective, this paper proposes a novel solution with the proper protocols to provide Trust in Data, making use of two Roots of Trust that are the IOTA Distributed Ledger Technology and the Trusted Platform Module. The paper presents the design of the proposed solution and discusses the key design aspects and relevant trade-offs. The paper concludes with a Proof-of-Concept implementation and an experimental evaluation to confirm its feasibility and to assess the achievable performance.
The Internet of Vehicles (IoV) plays a significant role in shaping smart cities by integrating vehicles, infrastructure, and information and communication technologies (ICT). IoV allows vehicles to connect and exchange information with each other and other smart devices contributing in shaping smart cities. IoV enables real-time data exchange between vehicles and traffic management systems. By collecting and analysing data on traffic flow, congestion, and road conditions, cities can optimize traffic signal timings, dynamically reroute vehicles, and provide drivers with real-time traffic updates. This improves traffic efficiency, reduces congestion, and enhances overall transportation systems. However, there is a risk that malicious vehicles may provide false information and interfere, or in the worst-case scenario, cause chaos on the roads. In order to address this issue, we propose a Blockchain-enabled intrusion detection system (BIDS) for the IoV network, in which vehicles share their mobility patterns with the traffic management system. BIDS formulates the mobility pattern of the vehicles in the form of blocks that are changed together. The blocks are validated and confirmed as the vehicle reaches the next location as claimed. Otherwise, the following blocks will become invalid and obviously will not be considered in the traffic management system. Our simulations show that the BIDS method can detect up to 98% of malicious vehicles when only 5% of the vehicles are malicious, and up to 85% when 40% of the vehicles are malicious.
With the growing demand for Internet of Things devices and their usage in day-to-day life, security is the prime factor that needs to be considered. In Internet of Things devices, authentication factors such as biometric factors are usually sensitive in nature. There is a need for some strong authentication mechanism that ensures negligible data breaches in a system. Zero-knowledge authentication is a modern cryptographic technique that proves knowledge without its disclosure, ultimately boosting security by avoiding any kind of storage or exposing sensitive data. This paper aims to analyze the properties of zero-knowledge proof and how it helps in preserving the security of IoT devices. The paper not only discussed the IoT architecture and analyzed the design of zero-knowledge authentication in various Internet of Things networks but also mentioned the limitations and future directions.
The widespread adoption of Internet of Things devices has led to a significant rise in security concerns.Attackers can exploit the vulnerability of centralized control in softwaredefined networks (SDN) through distributed denial of service (DDoS) attacks on these networks.The concentration of control within a network introduces novel vulnerabilities and potential avenues for attacks.The present strategies employed for mitigating DDoS attacks face challenges arising from their constrained adaptability, inadequate allocation of resources, and reduced flexibility.The developing technology of blockchain offers a robust solution for cost-effective, optimized, and adaptable mitigation of inter and intra-domain SDN against DDoS attacks.This work utilizes the Hyperledger Fabric platform, a permissioned blockchain, to examine the detection of DDoS attacks using the entropy approach.The IP addresses of the victims are compiled into a blacklist, which is subsequently disseminated as transactions to generate a ledger of the blockchain over the network.Employing this method makes it unnecessary to obstruct the victim's ports.Two scenarios, namely, single and linear, have been employed to represent intradomain topology and one scenario for interdomain in the context of multicontroller environments.The experiment investigates the effects of two attack types, single attack and multi-attacker, across three different circumstances.The findings indicate that the duration of mitigation was decreased, demonstrating the efficacy of enhancing the overall network security with increased flexibility.This approach has promise for countering DDoS attacks.This work advances by using a permissioned network with an SDN to mitigate DDOS attacks and using drop packets rather than block ports.Using HLF makes setting various configurations possible, and this act can enhance performance.Results show that mitigation time in the three topologies (single, liner, and multi-controller) was 30, 21, and 48, respectively, at the victim side, while it takes 40, 43, and 60 at the controller side.
This research paper explores the potential of using blockchain technology in cloud computing security to mitigate DDoS, MITM, and SQL injection attacks. The paper discusses the traditional security measures used in cloud computing and how attackers can bypass these measures at each layer of the OSI model. The proposed solution suggests utilizing blockchain features at each layer of the OSI model to enhance cloud security measures. The paper provides a detailed explanation of how the proposed blockchain-based security measures can be used to prevent DDoS, MITM, and SQL injection attacks. It is crucial to remember that the suggested remedy has not been put to the test in real life, and more investigation and testing are required to confirm its viability and efficacy. Overall, the integration of blockchain technology into cloud computing security shows promising potential to enhance the security of cloud environments.
Stefanos Chaliasos, Jens Ernstberger, David Theodore, David A. Wong · 6 authors
Zero-knowledge proofs (ZKPs) have evolved from being a theoretical concept providing privacy and verifiability to having practical, real-world implementations, with SNARKs (Succinct Non-Interactive Argument of Knowledge) emerging as one of the most significant innovations. Prior work has mainly focused on designing more efficient SNARK systems and providing security proofs for them. Many think of SNARKs as "just math," implying that what is proven to be correct and secure is correct in practice. In contrast, this paper focuses on assessing end-to-end security properties of real-life SNARK implementations. We start by building foundations with a system model and by establishing threat models and defining adversarial roles for systems that use SNARKs. Our study encompasses an extensive analysis of 141 actual vulnerabilities in SNARK implementations, providing a detailed taxonomy to aid developers and security researchers in understanding the security threats in systems employing SNARKs. Finally, we evaluate existing defense mechanisms and offer recommendations for enhancing the security of SNARK-based systems, paving the way for more robust and reliable implementations in the future.
Prabhat Kumar, Danish Javeed, Randhir Kumar, A.K.M. Najmul Islam
Summary Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI‐based threat hunting is data‐driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat‐hunting capability. In this paper, we present a blockchain‐enabled eXplainable AI (XAI) for enhancing the decision‐making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof‐of‐Authority (C‐PoA) consensus. Second, a novel deep learning‐based threat‐hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi‐head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.
Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongside a thorough analysis of the various anomalies and frauds that undermine these networks. It describes an array of detection and prevention strategies, encompassing statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and comprehensive risk assessment techniques. Through case studies, we explore practical applications of anomaly and fraud detection in blockchain networks, extracting valuable insights and implications for both current practice and future research. Moreover, we spotlight emerging trends and challenges within the field, proposing directions for future investigation and technological development. Aimed at both practitioners and researchers, this paper seeks to provide a technical, in-depth overview of anomaly and fraud detection within blockchain networks, marking a significant step forward in the search for enhanced network security and reliability.
Smart grids are getting important in today’s power management, so with that, smart grid technologies are increasingly important too. There have been a lot of concerns about smart grid technologies being hacked, and as a result, some deep black box adversarial attacks have been conducted and presented. We propose a new experimental methodology for benchmarking smart grid security with black box attacks. Additionally, concerning the type of smart grids, Smart Power Grids, deep black box adversarial attacks which can be crafted using virtually no knowledge about the target due to the inherent complexity of content available in cryptographic libraries like SecLib or Bouncy Castle how it affects security of cyber-physical power systems. We identify potential impacts of deep black box attacks on Smart Power Grids as implemented by the Department of Energy in 1996, we evaluate existing protection methods, and we find out the pitfalls thereof. With the aim of overcoming the aforementioned drawbacks, we initiate a study on deep black box adversarial attacks against Smart Power Grids showing that statistically significant effects against a national Smart Power Grid are achievable with absolute security. We also probe detection of cyber security attacks on Smart Power Grids. We illustrate landscape of smart grids with numerous cyber threats and demonstrate the limitations of traditional security practices. We show the importance of machine learning to detect attacks and the unlikelihood of identification of dependable and efficient detection schemes. We describe quantum voting ensemble models as one of the most powerful techniques in the detection of cyber security attacks. Finally, we propose an experimental setup and evaluation criteria to detect cyber security attacks in smart grids using quantum voting ensemble models. Then, we talk about private data storage in blockchain based smart grid infrastructure. We give an introduction of block chain and its essentiality in smart grids. We discuss privacy issues in block chain based smart grids. We acknowledge the strength of privacy safeguards, but on the same wavelength, we realize their weaknesses. Next, we propose a quantum resistant encryption technique that enhances the privacy of smart grids. We propose quantum voting ensemble models as one of the most promising techniques to address the issue of private data storage in block chains. As a result, we provide a comparison between the proposed models and traditional approaches to privacy protection in smart grids based on an experimental performance review. Then, we propose a unified strategy to improve smart grid cyber security by incorporating deep black box attacks with quantum voting ensemble models. Finally, we disclose several benefits of such integration and perform an experimental evaluation to investigate the effectiveness of the unified approach. The results of our study identify security gaps in smart grids and propose state-of-the-art mechanisms to address them. The challenges of smart grids system require the amalgamation of blockchain, quantum voting ensemble models and deep black box adversarial attacks. We achieve this objective proposing a unified strategy. The results of this study will equally be helpful for future research and smart grid cyber security implementations.
Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.
Abstract: This paper investigates the integration of an Intrusion Detection System (IDS) within the context of blockchain technology. The objective is to enhance the security posture of blockchain networks by detecting and mitigating potential intrusions. Through a meticulous examination of the current threat landscape and the unique challenges posed by blockchain systems, this research proposes a robust IDS framework tailored to the specific requirements of decentralized and distributed ledger environments. The study employs [specific methodology/approach] to assess the effectiveness of the proposed IDS, presenting conclusive findings that contribute to the ongoing discourse on securing blockchain ecosystems. The implications of this research extend to bolstering the resilience of blockchain networks against emerging threat.
Globally, increasing amount of cryptocurrency mining demand presents both opportunities and challenges for electric energy systems. This research employs a data-driven method to predict cryptocurrency mining load-tripping events, specifically targeting the low-voltage ride-through (LVRT) problem. The study utilizes diverse low-voltage fault scenarios generated through electromagnetic transient program (EMTP) software as training data. For fault classification, a convolutional neural network (CNN) is employed to improve model accuracy. Additionally, model explainability is enhanced using a decision tree for forecasting tripping events. The proposed approach is validated on a 6-bus power system integrated with cryptocurrency mining facilities.
Cyber diplomacy is critical in dealing with the digital era's evolving cybersecurity dangers and possibilities.This article investigates the impact of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchains, and Quantum Computing on cyber diplomacy.AI holds the potential for proactive threat identification and response, while IoT enables international information sharing.Blockchains enable secure data sharing and document verification, but they also pose new threats, such as AI-driven cyber-attacks, IoT privacy breaches, blockchain vulnerabilities, and the potential for quantum computing to break encryption.This article conducts case study reviews in combination with secondary data analysis and emphasises the value of international cooperation in developing global norms and frameworks to control responsible technology adoption.Cyber diplomacy can promote cybersecurity, protect national interests, and foster mutual trust among nations in the digital sphere by capitalising on possibilities and reducing threats.
This research introduces innovative features tailored to capture distinctive characteristics of ransomware activity within the cryptocurrency ecosystem. The study employs a multifaceted analysis to delve into ransomware-related data encompassing transaction metadata, ransom analysis, behavioral patterns, and financial aspects. A feature selection algorithm is explored to discern ransomware transactions in Bitcoin (BTC) and the United States Dollar (USD) using the UGRansome dataset. This comprehensive dataset of ransomware-related transactions facilitates the proposal of novel features designed to capture the unique traits of ransomware activity. The correlation matrix and temporal analysis of these features contribute to a nuanced understanding of the dynamic nature of ransomware threats. The research presents the Ransomware Feature Selection Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) to effectively select crucial ransomware features. Evaluation metrics such as precision, recall, accuracy, and F1 score highlight the effectiveness of the RFSA. The analysis reveals that approximately 68% of ransomware incidents involve BTC transactions ranging from 1.46 to 2.56, with an average of 2.01 BTC transactions per attack. Moreover, ransomware causes financial damages ranging from 4.38 to 172.36 USD, with an average damage of 88.37 USD. The RFSA identifies 17 ransomware types and their associated malware to shed light on their characteristics. The study investigates the pricing of ransomware and reveals that TowerWeb is associated with the highest fee, amounting to 135.26 BTC, while CryptoLocker has the lowest fee, recorded at 10.51 BTC. Additionally, the impact of ransomware duration on financial gains and network flow is investigated, disclosing a correlation between extended duration and higher financial gains. The research achieves outstanding performance metrics, including an MI score of 95%, accuracy of 93%, recall of 92%, and precision of 89%. These results showcase the superiority of the proposed approach over existing studies, emphasizing the dynamic and adaptable nature of ransomware demands. The findings suggest that there is no fixed amount for specific cyberattacks. This underscores the importance of adapting to the evolving landscape of ransomware threats.
Ensuring robust security in the Internet of Things (IoT) landscape is of paramount importance. This research article presents a novel approach to enhance IoT security by leveraging collaborative threat intelligence and integrating blockchain technology with machine learning (ML) models. The iOS application acts as a central control centre, facilitating the reporting and sharing of detected threats. The shared threat data is securely stored on a blockchain network, enabling ML models to access and learn from a diverse range of threat scenarios. The research focuses on implementing Random Forest, Decision Tree classifier, Ensemble, LSTM, and CNN models on the IoT23 dataset within the context of a Collaborative Threat Intelligence Framework for IoT Security. Through an iterative process, the models’ accuracy is improved by reducing false negatives through the collaborative threat intelligence system. The article investigates the implementation details, privacy considerations, and the seamless integration of ML-based techniques for continuous model improvement. Experimental evaluations on the IoT23 dataset demonstrate the effectiveness of the proposed system in enhancing IoT security and mitigating potential threats. The research contributes to the advancement of collaborative threat intelligence and blockchain technology in the context of IoT security, paving the way for more secure and reliable IoT deployments.
Abstract Sybil attacks are a very serious challenge in social networks including, the Social Internet of Things (SIoT). This paper introduces the SybilPSIoT method, in which a hybrid prevention and detection decentralized approach is proposed in SIoT based on smart contracts. The owner adds his objects to the smart contract. However, hostile owners can create Sybil things. This paper formally presents a model that uses a signed SIoT network with objects and identifiers as network nodes and information about the type of nodes (acknowledgers). Assuming the relationship between the edge marks between nodes and the node type, the proposed method uses trust paths between verification and desired nodes using a Bayesian inference model and structural balance patterns to judge the target node in these paths. It also uses game theory to control access owners to prevent Sybil from creating new things based on a cost‐benefit function. Based on the analysis method, a validating effect proportional to the path length on the target object was presented. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.
The block chain era is hastily traction as a sturdy approach to community security. In its conventional shape, block chain integrated is a distributed, immutable, and cryptographically-secured ledger, composed of virtual built in integrated shared amongst a disbursed community of nodes. This distributed ledger presents a platform for relaxed storage and verification of digital assets, built-inclusive of integrated crypto currencies, at the same time as built-integrated users with transactional privateness. As block chain-integrated technology advances, new applications, and protocols are built-integrated to resolve a variety of building situations related to network security. One such application is the utility of smart contracts to verify that community transactions are built-integrated, secure, and legitimate. By means of leverage building block chain integrated distributed ledger generation, clever contracts are capable of automating the execution of transactions at the same time as concurrently verifying their authenticity. This technique is built-in vital integrated fields of built finance and healthcare, as those built industries should manipulate and display access to treasured statistics and builtings. Moreover, smart contracts can be used to the right of entry to manage and authentication protocols, present built integrated customers with greater comfy get entry to data or sources. Any other use case there is for the deployment of distributed denial-of-carrier (DDoS) assault prevention protocols. DDoS attacks are one of the most luxurious and unfavorable online safety threats, and they use big amounts of bandwidth to crush a built in integrated or network-integrated infrastructure.
This paper designs a distributed multi-center identity authentication system based on blockchain technology. By supporting fine-grained declaration descriptions and privacy credentials related to zero-knowledge proof, the system achieves discretionary control of entities, privacy protection of user identities, and trusted data exchange. Since most blockchain-based identity authentication systems are based on public blockchain platforms, they are in essence traditional centralized identity management and verification methods. Therefore, this study establishes an open, transparent, trustworthy underlying identity architecture for users and upper-level applications, demonstrating the effectiveness and usability of the designed system through performance analyses and experiments.
A. Darwiesh, M. Elhoseny, A. H. El-Baz, Mohamed A. Nour
This study presents a new approach to predict cryptocurrencies risks based on social media platform indicators. This method can help cryptocurrency investors in their future decision-making. It utilizes advanced techniques such machine learning and natural language processing. Furthermore, a case study on American cryptocurrency investors is provided to help them identify and assess the risks associated with their purchases. Moreover, performance metrics are computed to demonstrate the capabilities of the new approach.
Muyun Gao, Shenwen Lin, Xin Tian, Xi He · 6 authors
Abstract There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K‐Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels; then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified.