Follow blockchain research across journals, conferences, and preprint repositories.
1,269 results · page 25 of 53
Zakaria Abou El Houda, Hajar Moudoud, Bouziane Brik, Lyes Khoukhi
International audience
Mohiuddin Ahmed, Clark Pagutaisidro, Apichart Alexander Pike, Yuting Yang · 5 authors
This paper presents a ransomware payment transactions repository, RansomCoin, and showcases the pattern analysis to understand the behaviour of ransomware attackers' money laundering tactics. The surge in ransomware attacks globally is an exciting research challenge and needs a sustainable solution. In this work, we created a bitcoin transaction dataset related to ransomware. In particular, we have focused on Qlocker, Medusa-Locker, BitPaymer, DarkSide, and Netwalker ransomware attacks to create an automation process that extracts data associated with these attacks from the blockchain. The automation details are publicly available via GitHub. The RansomCoin dataset will help law enforcement agencies trace the transaction and analyse the bitcoin movements in the blockchain. The dataset contains suspicious/normal flags, which can help focus on the wallet address flagged as suspicious. The k-NN algorithm from the family of anomaly detection techniques performs better in identifying suspicious transactions.
Aksham Sood
Today, almost all new Bitcoins are mined in mining pools, making their security crucial to the Bitcoin network as a whole. Distributed denial-of-service (DDoS) attacks are the most notable kind of attack against mining pools. Researchers have shown that when mining pools are big enough, they engage in distributed denial of service attacks. DDoS attacks targeting cryptocurrency are occurred, but that no pool has claimed credit for them as of yet. Therefore, we modify the play assessment method to account for the price of anti-DDoS tactics. Assuming we assume that each mining pool's computer power is solely used for mining, defense, and attack, we observe some exciting features. Keeping the peace is in the long term interest of mining pools (1) if a Cyber attack is really very unlikely to succeed. (2) Whether launching or defending against a distributed denial of (Denial - of - service) assault, mining pools' first objective is always to maximize the amount of available computational power for miners.
Ayodeji Falayi, Qianlong Wang, Weixian Liao, Wei Yu
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.
Manju Dahiya, Naman Mishra, Chinki Nagar, Ruby Bhati
The Cyber Financial domain has changed a lot over the past few years but with the digitalization of the same, the various gaps in the field of security have risen. In terms of Finance, although there has been the implementation of technology 4.0 there is a lack of proper regulations, and any countermeasures are still absent to make the system secure and prevent any attack on data integrity. This research builds a bridge in the security aspects of cyberspace as well as provides a comprehensive framework which is based on the blockchain network to improve cybersecurity. The various issues with the increasing number of intrusive attacks, data breaches, and system failures have made it important to adopt proper measures to curb the spread of such attacks. To make cyberspace more secure and understand the different types of intrusion attacks an IDS is developed to make use of self-learning to continually grow and thus be more efficient. The proposed VCM framework also makes use of a blockchain network for decentralization as well as the use of smart contracts are also discussed which gives an idea as to the depth of the cybersecurity which can be maintained with the help of this. All the proposed framework is centred around the vulnerabilities of cyberspace which are being continuously exploited by malicious attackers and thus the VCM acts in a comprehensive way to maintain the security, privacy as well as robustness of the system by enforcing certain regulations.
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.
Zhiqi Feng, Yongli Li, Xiaochen Ma
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.
Jian Su, Mengnan Jiang
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.
Muhammad Saad, Aziz Mohaisen
The biased distribution of cryptocurrency nodes across Autonomous Systems (ASes) increases the risk of spatial partitioning attacks, allowing an adversary to isolate nodes by hijacking AS prefixes. Prior works on spatial partitioning attacks have mainly focused on the Bitcoin network, showing that the prominent cryptocurrency network can be paralyzed by disrupting the physical topology through BGP hijacks.Despite the persisting threat of BGP hijacks, Bitcoin and other cryptocurrencies have not been frequently targeted, likely due to their shielded overlay topology, which limits the exposure of physical network anomalies. In this paper, we present a new perspective by examining the security of cryptocurrency networks, considering shared network resources (network interdependence). We conduct measurements extending beyond the Bitcoin network and analyze commonalities in Bitcoin, Ethereum, and Ripple node hosting patterns. We observe that all three networks are highly centralized, predominantly sharing the common ASes. We also note that among the three cryptocurrencies, Ripple does not shield its overlay topology, which can be exploited to learn about the physical network anomalies. The observed network anomalies present practical attack strategies that can be launched to target all three cryptocurrencies simultaneously.1We supplement our analysis by surveying recent BGP attacks on high-profile ASes and recognizing a need for application-level countermeasures. We propose attack countermeasures that reduce the risk of spatial partitioning, notwithstanding the increasing centralization of nodes and network interdependence.
R. Saravanan, S. Santhiya, K Shalini, V.S Sreeparvathy
Anomaly detection is one of the challenging problems encountered by the modern network security industry. In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Despite being an effective tool, the blockchain is not impervious to cyberattacks. For instance, a successful 51% attack on Ethereum Classic exposed security flaws in the technology. Attacks can be viewed from a statistical standpoint as an aberrant finding that strongly deviates from the norm. Machine learning is a science whose objective is to discover insights, trends, and anomalies in massive data sets; as a result, it can be used to detect blockchain attacks. In this work, we define a federated learning-based anomaly detection system that is trained using aggregate data gathered from observing blockchain activity on the end device itself. Experiments on the whole historical logs of the Ethereum Classic network demonstrate our model’s ability to accurately identify assaults that have been made public while also automatically signing digital transactions for further protection. Therefore, it is necessary to create an anomaly detection system that can monitor networks for any dangerous actions and produce findings for the management authority in the end device itself. Several classification techniques and machine learning algorithms have been taken into consideration in our suggested article to categorize the accurate model.
Ashwini Bhoware, Kapil Jajulwar, Atul Deshmukh, Kuldeep Dabhekar · 6 authors
To mine a blockchain on IP Networks, one must do several tasks related to chain management, rule optimization, verification, and hash generation design. Various consensus model subsets may benefit from the various blockchain mining techniques proposed by researchers. Most of these techniques, however, are rather complicated, which slows down the mining process for large-scale blockchains. Overly simplistic models that include unnecessary redundancies are inefficient and have little practical use. To solve these issues and boost blockchain mining efficiency in large-scale deployments, the authors of this paper propose creating a novel hybrid bioinspired approach. The proposed IP Network model is adaptable to almost all consensus procedures and may be easily combined with dynamic consensus models with few alterations. After collecting performance and context-specific data from the underlying blockchains, the technique uses Genetic Algorithm (GA) that distributes these range sets among miner nodes that support trust, allowing for high-performance mining while maintaining a high degree of trust under actual application situations. The model was tested against Proof-of-Stake (PoS), Proof-of-Work (PoW), Proof-of-Trust (PoT), and Practical Byzantine Fault Tolerance (PBFT) based consensus algorithms to ensure its effectiveness in real-world scenarios. Mining latency, energy consumption, and computational complexity were used as metrics against which this performance was measured. This analysis revealed that the proposed model has the potential to decrease mining latency by 4.5%, energy usage by 3.9%, and compute complexity by 4.1% across a variety of consensus mechanisms, making it suitable for a number of real-time applications.
Iyad Katib, Mahmoud Ragab
The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%.
Rihab Jmal, Walid Ghabri, Ramzi Guesmi, Badr M. Alshammari · 6 authors
By bringing smart and advanced solutions, the Internet of Things (IoT) has opened up new dimensions of innovative services and processing power for contemporary living standards. IoT has a wide range of devices and communication entities as a result of the widespread applications of these services, making network management a challenging task. Therefore, it is critical to redesign the IoT network’s management. The inherent programmability and centralized capabilities of software-defined networking (SDN) make network management simpler, enable network abstraction, make network evolution easier, and have the potential to handle the IoT network. However, security issues still present the IoT dilemma. Distributed Denial of Service (DDoS) attacks are among the most significant security threats in IoT systems. This paper studies in-depth DDoS attacks in IoT and in SDN. A review of different detection and mitigation techniques based on SDN, blockchain and machine learning models is conducted. A holistic, secure IoT system is proposed on the basis of SDN with multicontrollers. Blockchain is considered to guarantee security in distributed SDN-IoT networks and ANN to improve the detection and mitigation process.
Wenjuan Li, Yu Wang, Jin Li
No abstract is available for this record.
Hanting Chu, Pengcheng Zhang, Hai Dong, Yan Xiao · 6 authors
No abstract is available for this record.
Heena Rathore, Siva Sai, Akshay Gundewar
Connected Vehicles (CVs), an integral part of the future of intelligent transportation systems, use communication and sensing technologies to communicate among vehicles and infrastructure. However, as vehicles become interconnected, the vulnerability of their components to anomalies and deliberate malicious activity increases. In both cases, it is vital to detect and exclude anomalous data from the decision-making process. While deep learning techniques are gaining popularity for anomaly detection due to their adaptability, they are computationally expensive and require long training times. To overcome this challenge, this paper uses a directed acyclic graph (DAG) based distributed ledger technique and combines it with social psychology principles of ability, integrity, and benevolence to calculate the reputation of vehicles. We introduce the probability of malevolence, a measure of quality, which is a function of the error measurements (between ground truth and reported values) and reputation metrics. We introduce various anomalies such as bias, noise, short, multi-short, drift, multi-drift, stuck-at, and parasite chain attack in the simulated data from the Intelligent Driver Module framework on road topology such as uphill, ring, on-ramp, off-ramp, and road-works to validate the efficacy of the proposed framework in identifying the anomalies. Simulation results show that the malevolence factor serves as an efficient metric for automatically determining the types of anomalies in the CV network.
Anyu Yang
Cryptocurrency represented by Bitcoin is a very popular topic in recent years. However, the prosperity of cryptocurrency drives an increasing number of applications published. Some malicious or vulnerable programs are also detected and reported these years. To do a deeper study into security of cryptocurrency application, this paper learns common vulnerabilities, threat models inside normal applications, and taint analysis, a useful vulnerability-detecting tool, concludes a common and useful methodology for threat detection in application programs, especially Android apps. This approach uses static taint analysis to detect vulnerabilities inside a given Android application, classify them into common vulnerability categories and then make conclusions. This paper does research in analyzing statistics of threats in common cryptocurrency apps in Google play store and draw conclusions on the status of cryptocurrency software as well. Finally, some suggestions are provided at the end of this paper. These recommendations apply to application programmers, app store administrators, scholars and experts in related area, government officer and users. This set of analysis process can be applied to analyze any type of application programs.
Dhanasak Bhumichai, Ryan Benton
An eclipse attack is a sort of cyberattack in which attackers aim to isolate a local node on blockchain network layers and eclipse its connections from neighbor nodes. Detection of eclipse attacks is challenging because there is no dataset that can be used as input features for machine learning algorithms to make predictions. In addition, most of the network traffic is normal, but very few network traffic is eclipse network traffic. This condition is an unequal distribution of classes, which is considered an imbalanced dataset. At the same time, the boundary of normal and eclipse network traffic cannot be considered independent, which is regarded as overlapping sample data. To cope with these challenges, this paper introduces a new approach to distinguish normal and eclipse network traffic on the Ethereum network layers. To obtain datasets, an eclipse attack environment is established and launched on the real Ethereum blockchain platforms. Network traffic is captured under three conditions; before the eclipse attacks are launched, while the eclipse codes are launched, and after the eclipse attacks are launched. The collected data considered imbalanced and overlapped data is used as input datasets for iForest algorithms to learn and construct the principal profile of the eclipse network traffic. At the same time, a Dynamic Weighted Entropy algorithm is deployed to measure and enhance the quality of the overlapping subset generated by the iForest algorithms from the original dataset. Finally, the overlapping subset is used as an input feature for the Random Forest algorithm to distinguish eclipse network traffic from normal network traffic. This paper lays the groundwork for implementing an efficient mechanism to detect eclipse attacks in Ethereum network layers.
Dhanasak Bhumichai, Ryan Benton
Eclipse attacks are considered dangerous potential vulnerabilities of peer-to-peer networks and can cause serious consequences. Detecting eclipse attacks has become a crucial challenge that still lacks comprehensive studies, especially significant characteristics that can efficiently be used to classify eclipse network traffic. To fulfill a research gap, this paper aims to generate new sets of network traffic features that can be efficiently used by machine learning models to detect eclipse attacks by systemically analyzing and synthesizing network traffic features in the Ethereum network layers. After thoroughly analyzing and synthesizing, the newly created features are organized into five categories based on the mechanisms used to manipulate them. The first group is the common network traffic features that can be directly extracted from the blockchain network layers. The second category is the Entropy value of network traffic features that are calculated using an information entropy mechanism to represent the average amount of events in networks. Since the essential characteristics of eclipse attacks are centralized distribution and high probability distribution, the φ-entropy divergence algorithm is deployed to deal with this challenge in the third category. The fourth group is the statistic of the communication of the data package, which implements statistical methods to calculate how packages and data are transmitted via the blockchain networks. The last one is the statistic of data package structures which uses statistical techniques to calculate the characteristics of data packages. Forty-nine characteristics of network traffic features are used to represent the network traffic features in a way that can be easily understood and processed by the learning algorithms in detecting eclipse attacks in the Ethereum blockchain.
Nitesh Ghodichor, Raj Thaneeghavl, Dinesh Kumar Sahu, Gautam M. Borkar · 5 authors
MANET is a collection of mobile nodes that communicate through wireless networks as they move from one point to another. MANET is an infrastructure-less network with a changeable topology; as a result, it is very susceptible to attacks. MANET attack prevention represents a serious difficulty. Malicious network nodes are the source of network-based attacks. In a MANET, attacks can take various forms, and each one alters the network's operation in its unique way. In general, attacks can be separated into two categories: those that target the data traffic on a network and those that target the control traffic. This article explains the many sorts of assaults, their impact on MANET, and the MANET-based defence measures that are currently in place. The suggested SRA that employs blockchain technology (SRABC) protects MANET from attacks and authenticates nodes. The secure routing algorithm (SRA) proposed by blockchain technology safeguards control and data flow against threats. This is achieved by generating a Hash Function for every transaction. We will begin by discussing the security of the MANET. This article's second section explores the role of blockchain in MANET security. In the third section, the SRA is described in connection with blockchain. In the fourth phase, PDR and Throughput are utilised to conduct an SRA review using Blockchain employing PDR and Throughput. The results suggest that the proposed technique enhances MANET security while concurrently decreasing delay. The performance of the proposed technique is analysed and compared to the routing protocols Q-AODV and DSR.
Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro
Abstract Smart contracts are nowadays at the core of most blockchain systems. Like all computer programs, smart contracts are subject to the presence of residual faults, including severe security vulnerabilities. However, the key distinction lies in how these vulnerabilities are addressed. In smart contracts, when a vulnerability is identified, the affected contract must be terminated within the blockchain, as due to the immutable nature of blockchains, it is impossible to patch a contract once deployed. In this context, research efforts have been focused on proactively preventing the deployment of smart contracts containing vulnerabilities, mainly through the development of vulnerability detection tools. Along with these efforts, several heterogeneous vulnerability classification schemes appeared (e.g., most notably DASP and SWC). At the time of writing, these are mostly outdated initiatives, even though new smart contract vulnerabilities are consistently uncovered. In this paper, we propose OpenSCV, a new and Open hierarchical taxonomy for Smart Contract vulnerabilities, which is open to community contributions and matches the current state of the practice while being prepared to handle future modifications and evolution. The taxonomy was built based on the analysis of the existing research on vulnerability classification, community-maintained classification schemes, and research on smart contract vulnerability detection. We show how OpenSCV covers the announced detection ability of the current vulnerability detection tools and highlight its usefulness in smart contract vulnerability research. To validate OpenSCV, we performed an expert-based analysis wherein we invited multiple experts engaged in smart contract security research to participate in a questionnaire. The feedback from these experts indicated that the categories in OpenSCV are representative, clear, easily understandable, comprehensive, and highly useful. Regarding the vulnerabilities, the experts confirmed that they are easily understandable.
P D Prakruthi, K Yashawanth, D. L. Chethan, Bhuvan Kumar · 7 authors
AWS is an upcoming technology along with decentralized infrastructure. This knowledge is employed in variety of different domains including cloud computing, finance, energy, messaging and others. AWS really takes up vital part in military message passing which enables certainty and safety of messages by dodging changes created to data that is collected in blocks. AWS needs the access where each user is an authorized user. In this paper we will be proposing different cryptographic techniques along with AWS technology to ensure safe and secure passage of messages between different teams in the defence sector.
Haaroon Yousaf, Naomi A. Arnold, Renaud Lambiotte, Timothy LaRock · 8 authors
Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.
Aradhita Bhandari, Aswani Kumar Cherukuri, Firuz Kamalov
The modern world is connected by technology, with endless end devices, servers, infrastructures, and other resources creating a complex and interesting cyber landscape, constantly changing and improving, with new technologies discovered and vulnerabilities uncovered every day. In the last few years, machine learning and Blockchain technology have independently gained robust solutions for many cybersecurity problems. Machine learning has allowed for the development of smarter security systems that can automate processes such as intrusion or anomaly detection, allocation of resources, and scalability of operations. Blockchain technology has opened up more possibilities for decentralized systems that maintain or enhance security and privacy. Further, Blockchain technology has also renewed interest in smart contracts that automatically uphold agreements between parties involved. Unfortunately, their popularity has also exposed new vulnerabilities to other threats. The unique features of Blockchain and machine learning leave room for each to improve the other. Blockchain offers decentralized security and trust for the models and data for machine learning, while machine learning provides intelligent decision-making to anomaly detection, scalability, and efficiency in Blockchain networks. This work highlights how an interplay between Blockchain and ML would allow both technologies to assist 130 cybersecurity-related use cases. In this chapter, cybersecurity is defined, and common vulnerabilities are outlined, following which each of the technologies mentioned above is discussed in detail. Finally, developments involving both technologies are presented, and areas for future research are identified.