S. Vijayalakshmi, Rohan Yellamilli, R Shobikaa, V Subasree · 6 authors
Network Intrusion Detection Systems are critical in bolstering an organization's security infrastructure. The goal of our paper is to design and build a specialized intrusion detection system for simulated military scenarios. The solution classifies network accesses using machine learning techniques and seamlessly integrates the Ethereum blockchain for secure anomalous access record-keeping and auditing. The machine learning component classifies the records as normal or anomalous using the ensemble learning technique, which is the random forest classifier. Feature selection techniques like Variance Threshold, Spearman's correlation coefficient, Pearson's correlation coefficient, Mutual Information and Chi square tests were used to improve the accuracy of the model and reduce dimensionality. The final accuracy of the model was brought to 96.69%. It then sends the records to the blockchain environment for storage. To ensure data integrity and transparency, anomalous access events will be time-stamped, hashed using IPFS, and published to the blockchain. By providing a hash value and a unique id as input, the user will be able to add new anomalous entries. Furthermore, the user will be able to examine the hashed anomalous data for each csv file using the proper id.
Mingyi Liu, Jun Ho Huh, HyungSeok Han, Jaehyuk Lee · 8 authors
Decentralized Finance (DeFi) offers a whole new investment experience and has quickly emerged as an enticing alternative to Centralized Finance (CeFi). Rapidly growing market size and active users, however, have also made DeFi a lucrative target for scams and hacks, with 1.95 billion USD lost in 2023. Unfortunately, no prior research thoroughly investigates DeFi users' security risk awareness levels and the adequacy of their risk mitigation strategies. Based on a semi-structured interview study (N = 14) and a follow-up survey (N = 493), this paper investigates DeFi users' security perceptions and commonly adopted practices, and how those affected by previous scams or hacks (DeFi victims) respond and try to recover their losses. Our analysis shows that users often prefer DeFi over CeFi due to their decentralized nature and strong profitability. Despite being aware that DeFi, compared to CeFi, is prone to more severe attacks, users are willing to take those risks to explore new investment opportunities. Worryingly, most victims do not learn from previous experiences; unlike victims studied through traditional systems, DeFi victims tend to find new services, without revising their security practices, to recover their losses quickly. The abundance of various DeFi services and opportunities allows victims to continuously explore new financial opportunities, and this reality seems to cloud their security priorities. Indeed, our results indicate that DeFi users' strong financial motivations outweigh their security concerns - much like those who are addicted to gambling. Our observations about victims' post-incident behaviors suggest that stronger control in the form of industry regulations would be necessary to protect DeFi users from future breaches.
BACKGROUND: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks. OBJECTIVE: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance. METHODS: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism. RESULTS: Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications. CONCLUSIONS: We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.
Zixin Wang, Bin Cao, Yao Sun, Chenxi Liu · 6 authors
Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.
This paper introduces ZK-Team, a solution addressing the challenges faced by organizations in enabling shared ownership of accounts among team members. The conventional method involves smart contracts, but usability issues and privacy concerns hinder practicality. ZK-Team leverages Ethereum’s Account ion (ERC-4337) and zero-knowledge proofs to allow shared accounts without dedicated EOAs (Externally Owned Accounts) for gas fees, ensuring privacy. Our solution includes an innovative mechanism for transparently managing zero-knowledge inputs, minimizing on-chain storage without relying on an off-chain storage. The paper discusses both the theoretical aspects of the solution and its implementation available as an open-source project.
Cryptocurrency is frequently used in a variety of virtual transactions to secure transactions. The few years have seen an increase in online scams, and with the advent of cryptocurrencies and their masquerading, it has never been easier for scammers. A cryptocurrency is a type of digital currency that can be exchanged for virtual goods and services over the Internet. Despite cryptocurrencies containing complex codes being kept secret which are meant to protect or preserve their security, capable hackers can nevertheless breach them. Ransomware attacks have emerged as a significant method of malware intrusion in recent years, usually, ransomware attacks take place through a website like linking the link the file gets corrupted. But here is the major scam during Bitcoin transactions. Therefore, we require improved ransomware prediction methods utilizing a suitable machine learning algorithm and a publicly accessible dataset. Using a variety of features to predict malicious transactions, abnormal Bitcoin transactions are used to evaluate these strategies.
In the world of decentralized finance and smart contracts, the Ethereum blockchain stands as a pillar of innovation. However, with innovation comes the responsibility to fortify the systems against potential vulnerabilities. Reentrancy attacks pose a significant threat, exploiting vulnerabilities in the fabrication of smart contracts. These attacks can have severe consequences, compromising the integrity of transactions and jeopardizing the trust placed in blockchain technology. Many reentrancy solutions like yenta and ReVulDL have been implemented and various detection approaches have been proposed by various researchers. This paper proposed a “Hybrid Locking” mechanism to prevent Ethereum reentrancy which involves the combination of dynamic mutex locks and hierarchical locking which is lacking yet in research. This represents an innovative security enhancement for smart contracts by leveraging the proven concepts from concurrency control; establishing a multi-layered defense against reentrancy attacks, and addressing vulnerabilities that led to financial losses. This compelling solution press security issue in decentralized finance and proposed Hybrid Locking Mechanism shows promise, further empirical validation, and considerations for practical implementation which strengthen its contribution to the field of blockchain using context-aware defense mechanism against reentrancy attacks. The Hybrid Locking Mechanism demonstrated a 24.14% increase in effectiveness in preventing re-entrance compared to the average effectiveness of other approaches.
This study investigates the human errors that enable hackers to exploit and carry out social engineering attacks on the non-fungible token (NFT) ecosystem. The aim is to improve the design of decentralized applications that use NFTs to help non-technical users follow security best practices and address remaining user-side vulnerabilities. The study methods included a survey examining participants’ expertise regarding NFTs and cybersecurity, a remote security usability study investigating the pain points and common security best practices and a follow-up interview to examine participants’ experience with a crypto wallet configuration. The results show how human cognitive bias affects users’ decision to be cautious, users’ difficulty with security methods, and improvements to lessen users’ cognitive load. As NFTs expand beyond the cryptocurrency circle, multiple scams and thefts arise due to late adopters not knowing the security best practices. Therefore, increasing the public’s NFT security awareness is key to mitigating potential threats.
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explores the intersection of reinforcement learning and data privacy, specifically addressing the Multi-Armed Bandit (MAB) problem with the Upper Confidence Bound (UCB) algorithm. We introduce zkUCB, an innovative algorithm that employs the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) to enhance UCB. zkUCB is carefully designed to safeguard the confidentiality of training data and algorithmic parameters, ensuring transparent UCB decision-making. Experiments highlight zkUCB's superior performance, attributing its enhanced reward to judicious quantization bit usage that reduces information entropy in the decision-making process. zkUCB's proof size and verification time scale linearly with the execution steps of zkUCB. This showcases zkUCB's adept balance between data security and operational efficiency. This approach contributes significantly to the ongoing discourse on reinforcing data privacy in complex decision-making processes, offering a promising solution for privacy-sensitive applications.
Umamaheswari Rajasekaran, A. Malini, Vandana Sharma
With the rise of social media and high technology utilisation by different age groups, the digital structure arrangement has also changed from being simple node links to the interconnected complex networks. A digital mesh has been introduced with multiple consumers, interconnected webs, cloud storage, ubiquitous applications, and large data sets. These vast sets of networks can be connected to billions of things from devices to sensors and people to autonomous agents as per the requirements providing various services and information. Blockchain technology has automated payments by encouraging cryptocurrencies and introducing a shared and distributed ledger where data, logs, and transactions can be held in a decentralised manner with a more trusted and secure platform. This paper briefs the basic idea that digital mesh is leading the technology rise and how various applications, devices, services, and objects integrate artificial intelligence in them to automate and process the traditional human activities. Further, an overview of some basic features of blockchain is presented along with benefits of its integration with artificial intelligence.
Decentralized Finance (DeFi) enables many novel applications that were impossible in traditional finances. However, it also introduces new types of vulnerabilities. An example of such vulnerabilities is a composability bug between token contracts and Decentralized Exchange (DEX) that follows the Constant Product Market Maker (CPMM) model. This type of bug, which we refer to as CPMM composability bug, originates from issues in token contracts that make them incompatible with CPMMs, thereby endangering other tokens within the CPMM ecosystem. Since 2022, 23 exploits of such kind have resulted in a total loss of 2.2M USD. BlockSec, a smart contract auditing company, reported that 138 exploits of such kind occurred just in February 2023. In this paper, we propose CPMMX , a tool that automatically detects CPMM composability bugs across entire blockchains. To achieve such scalability, we first formalized CPMM composability bugs and found that these bugs can be induced by breaking two safety invariants. Based on this finding, we designed CPMMX equipped with a two-step approach, called shallow-then-deep search. In more detail, it first uses shallow search to find transactions that break the invariants. Then, it uses deep search to refine these transactions, making them profitable for the attacker. We evaluated CPMMX against five baselines on two public datasets and one synthetic dataset. In our evaluation, CPMMX detected 2.5x to 1.5x more vulnerabilities compared to baseline methods. It also analyzed contracts significantly faster, achieving higher F1 scores than the baselines. Additionally, we applied CPMMX to all contracts on the latest blocks of the Ethereum and Binance networks and discovered 26 new exploits that can result in 15.7K USD profit in total.
Reentrancy, a notorious vulnerability in smart contracts, has led to millions of dollars in financial loss. However, current smart contract vulnerability detection tools suffer from a high false positive rate in identifying contracts with reentrancy vulnerabilities. Moreover, only a small portion of the detected reentrant contracts can actually be exploited by hackers, making these tools less effective in securing the Ethereum ecosystem in practice. In this paper, we propose BlockWatchdog, a tool that focuses on detecting reentrancy vulnerabilities by identifying attacker contracts. These attacker contracts are deployed by hackers to exploit vulnerable contracts automatically. By focusing on attacker contracts, BlockWatchdog effectively detects truly exploitable reentrancy vulnerabilities by identifying reentrant call flow. Additionally, BlockWatchdog is capable of detecting new types of reentrancy vulnerabilities caused by poor designs when using ERC tokens or user-defined interfaces, which cannot be detected by current rule-based tools. We implement BlockWatchdog using cross-contract static dataflow techniques based on attack logic obtained from an empirical study that analyzes attacker contracts from 281 attack incidents. BlockWatchdog is evaluated on 421,889 Ethereum contract bytecodes and identifies 113 attacker contracts that target 159 victim contracts, leading to the theft of Ether and tokens valued at approximately 908.6 million USD. Notably, only 18 of the identified 159 victim contracts can be reported by current reentrancy detection tools.
Public cloud platforms have employed Trusted Execution Environment (TEE) technology to provide confidential computing services. However, applications running on cloud TEEs are susceptible to rollback or forking attacks. Their states can be rolled back to an outdated version or split into multiple conflicting versions, violating state continuity. Existing solutions against these attacks either rely on centralized trust assumption (e.g., trusted server) or have limited performance (e.g., tens of state updates per second). In this paper, we introduce Narrator-Pro (an upgrade to the original Narrator), a secure and practical distributed system that utilizes blockchain technology and TEEs to provide high-performance state continuity protection for TEE applications in the cloud. Specifically, we use the blockchain to initialize the system, which lays down the decentralized trust base with minimal interaction overhead. Meanwhile, we leverage the distributed system composed of TEEs to provide fast and unlimited state updates. We have implemented a proof-of-concept of Narrator-Pro in Intel SGX and conducted extensive evaluations in both the WAN and the LAN. Our results show that in a LAN environment with 5 nodes, Narrator-Pro can support around 8k state updates per second with a latency of 3.58ms. This performance is 30x higher than ROTE and 70× higher than using a TPM counter.
Kainat Ansar, Mansoor Ahmed, Saif Ur Rehman Malik, Markus Helfert · 5 authors
Context Data breaches caused by insiders are on the rise, both in terms of frequency and financial impact on organizations. Insider threat originates from within the targeted organization and users with authorized access to an organization’s network, applications, or databases commit insider attacks. Motivation Insider attacks are difficult to detect because an attacker with administrator capabilities can change logs and login records to destroy the evidence of the attack. Moreover, when such a harmful insider attack goes undetected for months, it can do a lot of damage. Such data breaches may significantly impact the affected data owner’s life. Developing a system for rapidly detecting data breaches is still critical and challenging. General Data Protection Regulation (GDPR) has defined the procedures and policies to mitigate the problems of data protection. Therefore, under the GDPR implementation, the data controller must notify the data protection authority when a data breach has occurred. Problem Statement Existing data breach detection mechanisms rely on a reliable third party. Because of the presence of a third party, such systems are not trustworthy, transparent, secure, immutable, and GDPR-compliant. Contributions To overcome these issues, this study proposed a GDPR-compliant data breach detection system by leveraging the benefits of blockchain technology. Smart contracts are written in Solidity and deployed on a local Ethereum test network to implement the solution. The proposed system can generate alert notifications against every data breach. Results We tested and deployed our proposed system, and the findings indicate that it can accomplish the insider threat mitigation objective. Furthermore, the GDPR compliance analysis of our system was also evaluated to make sure that it complies with the GDPR principles (such as right to be forgotten, access control, conditions for consent, and breach notifications). The conducted analysis has confirmed that the proposed system offers capabilities to comply with the GDPR from an application standpoint.
Due to the rapid advancement of cyber technology, virtual property is becoming more susceptible to hostile activity. Security structures that are mostly built on block chains (BCSSs) have gained traction. Disbursed ledger structures protected by encryption and consensus procedures are checked by BCSSs to protect industries against cyber attacks, data loss, and unwanted access. They provide users with a distributed ledger machine that is encrypted, shared, and may be used to manage virtual risks and digital property. It follows objectives to investigate how BCSSs affect cyber security. One way to perform a qualitative synthesis of the literature is to examine and analyze relevant academic materials and publications that discuss the challenges and advantages faced by BCSSs in the field of cyber security. The evaluation's findings will help determine the overall effect of BCSS on cyber security and offer recommendations for BCSS setups that will improve cyber security defense. To identify gaps in current cyber security measures, all findings will be compared with existing studies. It will provide invaluable analysis and insights into how best to use BCSSs for increased cyber security in comparison to traditional security architectures. When enforcing BCSSs, it will also recognize remarkable practices and tactics for businesses and professional clients.
This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within the education sector.
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.
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.
Purpose This paper aims to investigate optimal anti-counterfeiting strategies for non-fungible token (NFT) platforms in decentralized digital marketplaces. Using a game-theoretic model, it analyzes how verification policies shape interactions among platforms, genuine creators, counterfeiters and consumers. Addressing a theoretical gap, the study models the incentives and trade-offs platforms face when deciding whether and how much to verify product authenticity. It evaluates the impact of these decisions on consumer surplus and creator welfare, providing insights for platform operators and regulators seeking to balance profitability, authenticity and stakeholder interests. Design/methodology/approach A game-theoretic model examines strategic interactions among NFT platforms, genuine creators, counterfeiters and consumers across varying verification levels. The model incorporates dual sales channels (authentic and dubious), platform commissions and consumer heterogeneity in quality preference. By solving for subgame perfect equilibria, the analysis reveals how verification intensity influences counterfeiter entry, pricing and welfare outcomes. Comparative statics and equilibrium analysis provide managerial implications. The framework highlights the trade-offs platforms face in balancing verification costs with consumer trust and marketplace efficiency. Findings The study reveals counterintuitive results. First, the relationship between verification intensity and counterfeiter entry is non-monotonic – moderate verification can increase counterfeit activity by softening price competition between the dual channels. Second, stronger verification may reduce consumer surplus as price increases outweigh trust benefits. Third, the optimal verification level depends on verification costs and commission structures. Moderate verification often emerges as optimal, while excessive verification can harm both consumers and genuine creators. Research limitations/implications This study contributes to the literature on digital platform governance and anti-counterfeiting by introducing a formal game-theoretic model tailored to decentralized NFT marketplaces. It advances understanding of how verification strategies influence market structure and welfare outcomes in environments with limited enforcement. The findings challenge conventional views that stronger verification always benefits consumers and sellers, revealing nuanced trade-offs in decentralized platforms. These insights provide a foundation for future research on optimal platform design and regulation in blockchain-enabled, trust-sensitive digital ecosystems. Practical implications This paper offers actionable guidance for NFT platform managers in designing effective anti-counterfeiting strategies. It reveals that moderate verification – rather than maximal enforcement – is often optimal, even without cost constraints, as excessive verification can unintentionally reduce consumer surplus and original creator welfare. The findings highlight the importance of aligning verification intensity with platform commission structures. Regulators are also advised to consider incentive-compatible policies that promote trust while preserving market participation. These insights support more balanced and efficient governance in decentralized digital marketplaces. Social implications This study highlights the broader societal impact of anti-counterfeiting strategies in decentralized digital economies. It shows that well-intentioned verification efforts may unintentionally harm consumers by increasing prices and limiting access to authentic digital goods. The findings call for a more nuanced understanding of how policy and platform design affect consumer welfare, creator livelihoods and digital trust. By emphasizing the trade-offs between authenticity and accessibility, the study informs policymakers and platform designers about the importance of inclusive, efficient verification mechanisms that protect stakeholders without stifling innovation or participation in the rapidly evolving NFT and blockchain ecosystems. Originality/value This study fills a theoretical gap by modeling decentralized NFT marketplaces with limited enforcement and uncertainty. It is among the first to analyze anti-counterfeiting strategies in this context, offering practical guidance for platform operators and regulators. The findings enrich digital platform governance literature by highlighting nuanced trade-offs in verification strategies.