The application of blockchain technology in electronic voting (e-voting) systems represents a promising solution to the perennial challenges of trust, transparency, and security in electoral processes. This study aims to identify a suitable blockchain protocol that supports trustworthy vote aggregation and has a suitable consensus algorithm to validate vote counting. Our research methodology includes an extensive literature review and a comparative analysis of different blockchain protocols. Considering this, we examine various consensus algorithms such as Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance among others, each of which presents unique strengths and challenges. In addition, this study enriches the existing body of knowledge by proposing a novel algorithm that works at the edge of the network to validate and aggregate votes on the blockchain. This newly proposed algorithm is designed to provide maximum security, reliability, and accuracy while minimizing computational resources and network overhead. Our comprehensive research and innovative proposal serve to strengthen the potential of blockchain protocols and their consensus algorithms in the field of electronic voting systems. The results of this research could significantly influence the development and implementation of secure, transparent, and reliable e-voting systems based on blockchain technology, paving the way for more democratic and accountable voting mechanisms.
Computer networks and internet services are increasingly threatened by attacks like Distributed Denial-of-Service (DDoS). DDoS attack mitigation techniques now in use are ineffective due to a lack of resources and a lack of adaptability. Using blockchains like Ethereum, DDoS attacks can be thwarted in innovative ways. With smart contracts, it is possible to track down the IP addresses of attackers without additional hardware. This study examines blockchain-based solutions to combat DDoS attacks for feasibility, effectiveness, as well as cost and performance. The cost model delves into economic aspects like gas, gas price, and Ether value. In it, the evaluation of various smart contracts for the signalization of DDoS attacks is documented and compared to assess three system variants, analyzing gas costs, deployment, speed, and accuracy. It also details Ethereum's ecosystem and how that affects smart contract design and it also acknowledges scalability challenges and suggests outsourcing data for a more scalable solution, advocating for specialized blockchains for DDoS signaling applications. The analysis provides insights into the gas costs associated with different variants, considering various scenarios and highlighting the trade-offs and efficiencies of each approach.
Blockchain is an innovative technology that gives built-in security to any software or application. There is a wide range of applications for blockchain, from risk management to financial services, crypto-currencies and the Internet of Things (IoT). This innovation is based on transparency, immutability, security, efficiency and decentralization. It is a trending topic since cryptocurrencies are a hot topic in the market. Blockchain is a combination of mathematics, cryptography, algorithms and models. In this paper, we present a general overview of the security aspects of blockchain technology.
The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT's sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.
The ability of blockchain technology to improve security and transparency across a range of industries has receivedA great deal of attention has been garnered lately in correcting the sentence.. In the domain of intrusion detection, where the identification and mitigation of cyber threats are paramount, blockchain has emerged as a promising solution. This abstract examines how blockchain is used in intrusion detection systems and emphasizes its advantages. Blockchain technology improves the security and integrity of intrusion detection systems by using a decentralized and immutable ledger. It provides an immutable audit trail, distributed consensus, and increased resilience to attacks. Moreover, blockchain fosters trust, transparency, and collaboration among stakeholders, enabling faster threat detection and response. This research can explore novel approaches to integrating blockchain into intrusion detection systems, providing stronger protection against cyber threats.Immutable Audit Trail: In the context of intrusion detection, the capacity of blockchain to produce an unalterable and transparent audit trail is of enormous value. Research in this area can focus on developing techniques to leverage the blockchain's audit trail for effective incident response, forensic investigations, and attribution of cyberattacks. We will use theweighted product model in this study, which is a research approach that gives weights to various factors and combines them to make conclusions based on their relative relevance in a weighted way. Taken as alternative is“IDS1, IDS2, IDS3, IDS4, IDS5, IDS6, IDS7, and IDS8”.Detection Quality, Performance, Stability, User Interface, Profile update, ConvenienceThe By this we can see that IDS4 has 1 RANK and IDS5 has the 8th RANK.In conclusion, blockchain technology holds great potential in the intrusion detection domain. Its decentralized and immutable nature can enhance the security and reliability of intrusion detection systems by providing transparent and tamper-proof logs of network activity. Blockchain-based solutions can improve threat detection, facilitate secure information sharing among entities, and enhance the overall resilience of intrusion detection systems. As the technology continues to evolve, further research and development in integrating blockchain with intrusion detection will unlock new possibilities for combating cyber threats.
Salem S. M. Khalifa, Ali Mohamed E. Ejmaa, Abdulmawla Mohammad Ali Najih, Mohamed Abd Arahman Masoud Zneen
A transition to democratic rule is considered the first step down a long road towards Libya’s recovery and prosperity. Thus, it strives to improve the country’s elections by introducing new technologies. A blockchain is a distributed ledger that is characterised by independence and security. Therefore, it has been widely applied in various fields ranging from credit encryption and digital currency. With the development of internet technology, electronic voting (E-voting) systems have been greatly popularised. However, they suffer from various security threats, which create a sense of distrust among existing systems. Integrating blockchain with online elections is a promising trend, which could lead to make an election transparent, immutable, reliable, and more secure. In this paper, we present a literature review and a case analysis of blockchain technology. Moreover, a framework for an E-voting system based on blockchain is proposed. The methodology is adopted on the basis of three activities, they are identification of the relevant literature about E-voting, system modelling, and the determination of suitable technological tools. The framework is secure and reliable. Thus, it could help increase the number of voters and ensure a high level of participation, as well as facilitate free and fair electoral processes
R. Hanumantharaju, Shreenath Kn, Sowmya BJ, Srinivasa Kg
Intrusion detection is a familiar phrase in the information and network security domain. An Intrusion Detection System (IDS) is a device or software that will keep track of the networks, for unlawful movements, and policy breaches that arise within the network. There are different forms of IDS, Host Intrusion Detection System (HIDS) helps in identifying unauthorized activities on the host, Network Intrusion Detection System (NIDS) helps in identifying attacks in the network, whereas Distributed Intrusion Detection System (DIDS) consists of multiple IDS over a large area of network where individual IDS communicates with each other or with the central the authorized central server. The proposed work has a three-layered architecture for DIDS for securing data sharing among different IDS. The bottom layer uses multiple IDS, the fog layer is supported with Blockchain functionality, and the cloud service at the upper layer stores required data permanently for future analysis. The fog computing-based architecture for DIDS tries to implement the application in a scalable and trustless environment using distributed ledger technology. The evaluation of the proposed work is carried out for fog, cloud, and integrated fog-cloud with the Blockchain functionality and without Blockchain functionality in measuring performance metrics related to throughput, service latency, response time, block creation time, and block execution time.
This paper presents the first comprehensive analysis of an emerging cryptocurrency scam named "arbitrage bot" disseminated on online social networks. The scam revolves around Decentralized Exchanges (DEX) arbitrage and aims to lure victims into executing a so-called "bot contract" to steal funds from them. To entice victims and convince them of this scheme, we found that scammers have flocked to publish YouTube videos to demonstrate plausible profits and provide detailed instructions and links to the bot contract. To collect the scam at a large scale, we developed a fully automated scam detection system namedCryptoScamHunter, which continuously collects YouTube videos and automatically detects scams. Meanwhile,CryptoScamHunter can download the source code of the bot contract from the provided links and extract the associated scam cryptocurrency address. Through deployingCryptoScamHunter from Jun. 2022 to Jun. 2023, we have detected 10,442 arbitrage bot scam videos published from thousands of YouTube accounts. Our analysis reveals that different strategies have been utilized in spreading the scam, including crafting popular accounts, registering spam accounts, and using obfuscation tricks to hide the real scam address in the bot contracts. Moreover, from the scam videos we have collected over 800 malicious bot contracts with source code and extracted 354 scam addresses. By further expanding the scam addresses with a similar contract matching technique, we have obtained a total of 1,697 scam addresses. Through tracing the transactions of all scam addresses on the Ethereum mainnet and Binance Smart Chain, we reveal that over 25,000 victims have fallen prey to this scam, resulting in a financial loss of up to 15 million USD. Overall, our work sheds light on the dissemination tactics and censorship evasion strategies adopted in the arbitrage bot scam, as well as on the scale and impact of such a scam on online social networks and blockchain platforms, emphasizing the urgent need for effective detection and prevention mechanisms against such fraudulent activity.
Qianrui Zhao, Yinan Wang, Bo Yang, Ke Shang · 8 authors
Cross-chain bridges are crucial mechanisms for facilitating interoperation between different blockchains, allowing the flow of assets and information across various chains. Their pivotal role and the vast value of assets they handle make them highly attractive to attackers. Major security incidents involving cross-chain bridge projects have been occurring frequently, resulting in losses of several billion due to cyber attacks. The diversity of vulnerability exploitation methods by hackers is vast, but not entirely untraceable. There are scarce research outcomes studying cross-chain bridge cyber incidents, and we have conducted a study based on the most recent cross-chain bridge security incidents. We introduce the working principles, components, and architecture of cross-chain bridges, explain the categorization mechanisms of the trust layer in cross-chain bridges, summarize four categories of hacker vulnerability exploitation techniques from real cases, and propose preventative measures for cross-chain bridge security.
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propose CustodianFL, a two-staged anomaly detection method specifically designed for FL deployments. In the first stage, it flags suspicious client activities. In the second stage that is activated only when needed, it further examines these candidates using Three-Sigma Rule to identify and exclude truly malicious local models from FL training. To ensure integrity and transparency within the FL system, CustodianFL integrates zero-knowledge proofs, enabling clients to cryptographically verify the server's detection process without relying on the server's goodwill. CustodianFL operates without unrealistic assumptions and avoids interfering with FL training in attack-free scenarios. It bridges the gap between theoretical advances in FL security and the practical demands of real FL systems. Experimental results demonstrate that CustodianFL consistently delivers performance comparable to benign cases, highlighting its effectiveness in identifying and eliminating malicious models with high accuracy.
Chuyi Yan, Chen Zhang, Meng Shen, Ning Li · 8 authors
Abstract Ethereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium . First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively.
Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams, and a variety of other frauds emerge. Existing Ponzi scheme detection approaches based on heterogeneous transaction graph modeling leverages semantic information between node (account) pairs to establish connections, overlooking the semantic attributes inherent to the edges (interactions). To overcome this, we construct heterogeneous Ethereum interaction graphs with multiple triplet interaction patterns to better depict the real Ethereum environment. Based on this, we design a new framework named multi-triplet augmented heterogeneous graph neural network (MAHGNN) for Ponzi scheme detection. We introduce the Conditional Variational Auto Encoder (CVAE) to capture the semantic information of different triplet interaction patterns, which facilitates the characterization on account features. Extensive experiments demonstrate that MAHGNN is capable of addressing the problem of multi-edge interactions in heterogeneous Ethereum interaction graphs and achieving state-of-the-art performance in Ponzi scheme detection.
Yanan Gong, K. P. Chow, Siu Ming Yiu, Hing Fung Ting
Bitcoin is a widely used decentralized cryptocurrency. The proportion of Bitcoin transactions used for illegal activities is increasing. Mixing services are commonly applied to enhance anonymity and make transaction records more challenging to follow and analyze. The current research on peeling chains is generally based on heuristic algorithms to identify change addresses. However, due to the characteristics and limitations of the Bitcoin blockchain, there is no such ground truth to ensure the accuracy of each derived change address. This research analyzes the peeling chain patterns based on self-change addresses. The use of self-change addresses implies that the input address and the address used for receiving the change are controlled by the same entity. Also, each chain's transaction details and generated chain parameters are further verified for more precise results. Combining the two methods ensures the accuracy of the extracted peeling chains to some extent. And the corresponding behavior pattern of the extracted chains is studied.
The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%.
An increasing number of Internet of Things (IoT) applications are based on a federated environment.Examples include the creation of federations of NATO countries and non-NATO entities participating in missions (Federated Mission Networking) or the interaction of civilian services and the military when providing Humanitarian Assistance And Disaster Relief.Federations are often formed on an ad hoc basis, with the primary goal of combining forces in a federated mission environment at any time, on short notice, and with optimization of the resources involved.One of the leading security challenges in a federated environment of separate IoT administrative domains is effective identity and access management, which is the basis for establishing a relationship of trust and secure communication between IoT devices belonging to different partners.When carrying out missions involving the military and ensuring security, meeting requirements for immediate interoperability is important.In the paper, an attempt has been made to develop a system architecture framework for secure and reliable data streams distribution in a multi-organizational federation environment, where data authentication is based on IoT device identity (fingerprint).Moreover, a hardware-software IoT gateway has been proposed for the verification process and the integration of Hyperledger Fabric's distributed ledger technology, the Apache Kafka message broker, and data-processing microservices implemented using the Kafka Streams API library.The performance tests conducted confirm the suitability of the developed system framework for processing and distributing audiovideo data in a federation IoT environment.Also, a high-level security and reliability assessment was conducted in the paper.
The rapid expansion of the Internet of Things (IoT) on a global scale has facilitated the convergence of revolutionary technologies such as artificial intelligence (AI), blockchain, and cloud computing. The integration of these technologies has paved the way for the development of intricate infrastructures, such as smart homes, smart cities, and smart industries, that are capable of delivering advanced solutions and enhancing human living standards. Nevertheless, IoT devices, while providing effective connectivity and convenience, often rely on traditional network interfaces that can be vulnerable to exploitation by adversaries. If not properly secured and updated, these legacy communication protocols and interfaces can expose potential vulnerabilities that attackers may exploit to gain unauthorized access, disrupt operations, or compromise sensitive data. To overcome the security challenges associated with smart home systems, we have devised a robust framework that leverages the capabilities of both AI and blockchain technology. The proposed framework employs a standard dataset for smart home systems, from which we first eliminated the anomalies using an isolation forest (IF) algorithm using random partitioning, path length, anomaly score calculation, and thresholding stages. Next, the dataset is utilized for training classification algorithms, such as K-nearest neighbors (KNN), support vector machine (SVM), linear discriminate analysis (LDA), and quadratic discriminant analysis (QDA) to classify the attack and non-attack data of the smart home system. Further, an interplanetary file system (IPFS) is utilized to store classified data (non-attack data) from classification algorithms to confront data-manipulation attacks. The IPFS acts as an onsite storage system, securely storing non-attack data, and its computed hash is forwarded to the blockchain’s immutable ledger. We evaluated the proposed framework with different performance parameters. These include training accuracy (99.53%) by the KNN classification algorithm and 99.27% by IF for anomaly detection. Further, we used the validation curve, lift curve, execution cost of blockchain transactions, and scalability (86.23%) to showcase the effectiveness of the proposed framework.
With the rapid development of information technologies, industrial Internet has become more open, and security issues have become more challenging. The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge. However, endogenous security lacks a scientific and formal definition in industrial Internet. Therefore, firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis. Secondly, the endogenous security innovation mechanism is clearly defined. Thirdly, an improved clone selection algorithm based on federated learning is proposed. Then, we analyze the threat model of the industrial Internet identity authentication scenario, and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof. We conduct identity authentication experiments based on two types of blockchains and compare their experimental results. Based on the experimental analysis, Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet. Internet of Things Application (IOTA) public blockchain can be used for data aggregation analysis of Internet of Things (IoT) edge nodes. Finally, we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.
Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organizations identify and respond to security risks in a prompt manner. ML and DL techniques can analyze large amounts of information and detect patterns that may indicate the presence of malicious or cyber attack activities. Therefore, this study focuses on the design of blockchain-assisted hybrid metaheuristics with a machine learning-based cyber attack detection and classification (BHMML-CADC) algorithm. The BHMML-CADC method focuses on the accurate recognition and classification of cyber attacks. Moreover, the BHMML-CADC technique applies Ethereum BC for attack detection. In addition, a hybrid enhanced glowworm swarm optimization (HEGSO) system is utilized for feature selection (FS). Moreover, cyber attacks can be identified with the design of a quasi-recurrent neural network (QRNN) model. Finally, hunter–prey optimization (HPO) algorithm is used for the optimal selection of the QRNN parameters. The experimental outcomes of the BHMML-CADC system were validated on the benchmark BoT-IoT dataset. The wide-ranging simulation analysis illustrates the superior performance of the BHMML-CADC method over other algorithms, with a maximum accuracy of 99.74%.
The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and actuators with industrial software tools and applications. IIoT aims to enhance manufacturing and industrial processes by capturing and analyzing real-time industrial data. However, the heterogeneous and homogeneous nature of IIoT networks makes them vulnerable to several security threats. As data is transmitted over an insecure communication medium, intruders may intercept communication among different entities and perform malicious activities. Consequently, ensuring the security and privacy of data transmitted in IIoT networks is essential. Motivated by the aforementioned challenges, this article presents a deep-learning-integrated blockchain framework for securing IIoT networks. Specifically, first, we design a private blockchain-based secure communication among the IIoT entities using session-based mutual authentication and key agreement mechanism. In this approach, the Proof-of-Authority (PoA) consensus mechanism is used for verification of the transactions and block creation based on the voting of miners over the cloud server. Second, we design a novel deep-learning-based intrusion detection system that combines contractive sparse autoencoder (CSAE), attention-based bidirectional long short-term memory (ABiLSTM) networks, and softmax classifier for cyberattack detection. The practical implementation of blockchain and deep-learning techniques proves the effectiveness of the proposed framework.
Joseph Bamidele Awotunde, Tarek Gaber, L V Narasimha Prasad, Sakinat Oluwabukonla Folorunso · 5 authors
The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.
Aliyu Ahmed Abubakar, Jinshuo Liu, Ezekia Gilliard
Abstract Intrusion Detection System (IDS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false‐positive rates. To improve the accuracy of an IDS, blockchain technology can be used as it provides a secure, decentralized, immutable ledger that can track suspicious activity over time and also identify intrusions globally. In this paper, the authors propose a novel methodology to improve the accuracy of blockchain‐based IDS. The approach combines different intrusion detection algorithms using a blockchain‐integrated architecture. It is based on the fusion principle and weighted votes, which the authors used to determine their results. The authors tested the system on DARPA 99 and MIT‐Lincoln Labs datasets using accuracy and false‐positive rate as their two metrics. The system achieved 92.6% accuracy and 7.4% false‐positive rates, indicating that the proposed system significantly increases the accuracy while reducing the false‐positive rate, opening up new opportunities for the development of highly accurate networks.
Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks.
This paper addresses the issue of blockchain protocol risks, a foundational category of risks affecting Distributed Ledger Technology (DLT) which underpins digital assets, smart contracts, and decentralised applications. It presents a comprehensive risk management framework developed in collaboration with financial institutions, blockchain development teams and regulators that applies a traditional risk management taxonomy to address certain overlooked blockchain protocol risks. The approach offers a structured way to identify, measure, monitor and report blockchain protocol risks. The paper provides real-world use cases to demonstrate the practicality and implementation of the proposed framework. The findings of this work contribute to the evolving understanding of blockchain protocol risks and provide valuable insights on how these risks affect the adoption of DLT by financial institutions.
The recent surge in the attention garnered by blockchain technology, an immutable ledger enabling decentralized transactions, is noteworthy.However, the security of blockchain remains susceptible to various attacks, including distributed denial-of-service (DDoS) attacks, which have increasingly targeted Bitcoin services.In response, deep learning algorithms have emerged as a potent solution to complex problems within the realm of information science.This study proposes a novel approach, utilizing these algorithms within hybrid frameworks, to address intricate cybersecurity issues.The methodologies were implemented and fine-tuned within a Python environment.Initially, a technique known as data augmentation was applied to an experimental domain aimed at verifying efficiency and boosting precision in complex datasets.Data augmentation, a method of generating new data points from existing ones, artificially enhances the volume of data.A Conditional Table Generative Adversarial Network (CTGAN) approach was adopted for the creation of tabular synthetic data.The utilization of synthetic data was found to enhance the model's performance and robustness compared to the exclusive use of original data.Subsequently, a binary classification hybrid deep learning model, incorporating Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms, was proposed for the detection of DDoS attacks within cryptocurrency networks.The proposed model was then validated using actual instances of DDoS attacks within the Bitcoin service dataset.The validation process incorporated a test set comprising 20% of the augmented data.Evidently, the proposed model outperformed standard deep learning implementations, achieving an impressive accuracy of approximately 95.84%.This study, therefore, presents a promising approach to mitigating DDoS attacks within the Bitcoin ecosystem.