Ashmit Mishra, Shivam Singh, Mohammad Ansari, Rajendra Kumar Dwivedi
Electronic democracy has addressed issues like anonymity and competency associated with traditional democracy, it brings its own challenges, including oversight of vote counting. The proposed blockchain based online voting system integrates advanced security features, including unique identification keys, fingerprint authentication, and one-time passwords. To make sure that the security and integrity of voting process, we employ 128-bit AES encryption, SHA-256, and blockchain technology. Votes are recorded as transactions within a blockchain, ensuring atomicity and integrity. This approach addresses the challenges of existing voting systems, offering an efficient and secure electronic voting solution.
Recent years saw the birth of cryptocurrencies, and among them is Bitcoin. The platform it offers allows people to move money without needing a middleman or revealing their true identity. All very good things that are aimed at fostering financial independence and privacy, but this also allows for bad stuff like fraud, money laundering, and aiding illegal practices. The problem arises when you consider how hard it is to scrutinize Bitcoin transactions. This is made even more difficult when trying to track illicit activities. That’s what makes even the mention of fraud put a frown on law enforcement officers’ faces. Our solution? Utilizing web scraping we set out on a data collection expedition across the Bitcoin blockchain. Our goal was simple: track down Bitcoin addresses associated with illicit activities. It may seem like an easy task but in reality, major roadblocks led us astray. Those who make use of their anonymity know exactly what they’re doing and often cover their tracks very well. But after countless hours of scouring online platforms, marketplaces, forums, and other places we managed to find a network of illicit Bitcoin addresses involved in fraudulent transactions and money laundering schemes. Moreover, the study delves into the ethical aspects of gathering publicly accessible data from blockchain technology. It explores the regulations governing web scraping and the moral considerations related to privacy and data collection in the realm of cryptocurrencies. By shedding light on the ethical principles that guide web scraping endeavors this research emphasizes the need for an approach that respects privacy rights while enabling effective identification and monitoring of illicit cryptocurrency activities. The findings of this investigation not reveal aspects of cryptocurrency transactions but also propose a robust methodology. This methodology can be utilized by stakeholders, including bodies, law enforcement agencies and the wider cryptocurrency community. It will aid in monitoring, regulating and fostering a lawful ecosystem, for cryptocurrencies. Our research emphasizes the need for an effort to establish a set of regulations that not only discourages illegal activities but also promotes the integration of cryptocurrencies into the mainstream financial sector.
Payment channels serve as an effective solution to the scalability problem of cryptocurrencies, which significantly increase transaction rates by allowing users to conduct large-scale offline transactions off-chain without posting everything to the blockchain. However, the existing payment channels lack privacy protection for the transaction amount and the linking relationship between the two parties to the transaction. Therefore, in order to address the scalability and privacy issues of cryptocurrencies such as Bitcoin, this paper proposes a zk-SNARKs-based anonymous payment channel (zk-APC), which supports an unlimited number of off-chain payments between the payer and the payee and protects the privacy of the participants. Specifically, the proposed scheme achieves relational anonymity and amount privacy for both on-chain and off-chain transactions in the payment channel through utilizing zero-knowledge proof (zk-SNARKs) and commitment schemes. This paper proves that the proposed method is more effective than similar schemes through a performance evaluation.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Recently, Oblivious Storage has been proposed to prevent privacy leakage from user access patterns, which obfuscates and makes it computationally indistinguishable from the random sequences by fake accesses and probabilistic encryption. The same data exhibits distinct ciphertexts. Thus, it seriously impedes cloud providers’ efforts to improve storage utilization to remove user redundancy, which has been widely used in the existing cloud storage scenario. Inspired by the successful adoption of removing duplicate data in cloud storage, we attempt to integrate obliviousness, remove redundancy, and propose a practical oblivious storage, PEO-Store. Instead of fake accesses, introducing delegates breaks the mapping link between a valid access pattern and a specific client. The cloud interacts only with randomly authorized delegates. This design leverages non-interactive zero-knowledge-based redundancy detection, discrete logarithm problem-based key sharing, and secure time-based delivery proof. These components collectively protect access pattern privacy, accurately eliminate redundancy, and prove the data delivery among delegates and the cloud. Theoretical proof demonstrates that, in our design, the probability of identifying the valid access pattern with a specific client is negligible. Experimental results show that PEO-Store outperforms state-of-the-art methods, achieving an average throughput of up to 3 times faster and saving 74% of storage space.
Abstract Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.
Elizabeth M. Johnson, Victoria Grace Wilson, W. Thompson
Blockchain technology presents a decentralized and transparent solution for secure voting systems. This paper provides a comparative analysis of consensus protocols, such as Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS), to evaluate their effectiveness in a secure voting environment. Results show that PoS offers optimal security with reduced energy consumption, making it suitable for public elections.
Voting aims to provide the best decision or select the most selected option for the largest group of voters. Malicious parties gaining access, and otherwise tampering with election results, or the votes make this effort counterproductive. To alleviate this, this study examined the introduction of blockchain. The transparent and immutable nature of the blockchain makes this data impossible to alter and allows the election results to be transparent. To further increase the transparency of the system while keeping voters anonymous, a biometric based cryptography was introduced. The biometric was introduced as the source for the private key for each voter while a public was generated to act as the identity of the voter. The biometric trait of each individual is unique and cannot be forged, hence the identity of the voter is secured. The public key available cannot be traced by to the private key, hence, identity of the voter is anonymous. The system showed an encouraging performance after testing.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Yang Tian, Xin Liao, Li Dong, Yang Xu · 5 authors
Recent years have witnessed the booming growth of 5G and 6G technology, which has brought unprecedented massive data transmission, causing severe privacy issues. However, traditional information encryption and multimedia covert communication fail to protect the identities of communication parties and the originality of messages. The emergence of blockchain provides a promising solution to solve these problems. Its anonymity manages to hide the identities of communication parties, and immutability ensures the message is undestroyable. However, the existing blockchain-based covert communication schemes suffer the issues of low embedding capacity and high time cost. In this paper, an amount-based covert communication scheme over the blockchain is proposed, in which a unique coding method is devised for hiding messages into transaction amounts to improve the embedding capacity. Compared with existing address-based methods, the proposed scheme can apply any address and reduce the time of obtaining special addresses. Besides, we innovate the way to prove the concealment by calculating the relative entropy of the transaction amount between Bitcoin and the proposed scheme. The security of our method is demonstrated by comparing the probability of attackers acquiring secret messages under different adversary capabilities. The experimental results verify that the proposed approach outperforms the existing schemes regarding embedding capacity, time costs, number of transactions, concealment, and security.
Advanced Steganography and Watermarking Techniques
Due to the concentrated distribution of network traffic, the Internet is highly vulnerable to link flooding attack in Distributed Denial-of-Service attacks (DDoS-LFA), which utilizes the legitimate low-rate attack traffic to block the selected network area. In recent years, building trusted networks has been considered as a promising strategy to address the security issues. Nevertheless, deploying a trusted link defense mechanism in the attacked network area faces many challenges imposed by the smart scheme and legitimate disguise of DDoS-LFA. In order to overcome these challenges, we propose a blockchain-based DDoS-LFA detection and mitigation scheme, named CREDIT, to guarantee the security of attacked area, while existing works only use blockchain to share the detection results of traditional solutions. CREDIT uses blockchain to record and share the information of links and flows in real time, which enables routers in the protected area to easily trace the paths of all active flows and capture the fragile links. On the basis of link features, a credible deep learning method performed on randomly selected nodes is proposed to detect DDoS-LFA against data spoofing. When an attack alarm is raised, CREDIT performs similarity analysis to locate attackers and migrate suspicious traffic based on the flow features of alarm links. Experimental results based on real implementation and attack testbed show that, by integrating blockchain, CREDIT performs better than traditional non-blockchain-based DDoS-LFA defense methods when faced with data tampering.
In a blockchain-based data trading platform, data users can purchase data sets and computing power through encrypted smart contracts. The security of smart contracts is important as it relates to that of the data platform. However, due to the inability to apply to detection rules with complex structures and the inefficiency of detection, existing malicious code detection methods are not suitable for the encrypted smart contracts in blockchain-based data trading platforms with high transaction rate requirements. In this paper, a practical and privacy-preserving malicious code detection method is proposed for encrypted smart contract in blockchain-based data trading platform. Specifically, we design two kinds of miners to act as the malicious rule processor and the detector respectively for inspecting the encrypted smart contract. The rule processor generates an obfuscated map with the original open-source malicious rule set. The detector performs a malicious inspection algorithm by inputting the obfuscated map and the randomized tokens, where the latter is generated from smart contract. Then, we theoretically analyze the security syntax of the proposed method. The analysis results demonstrate the proposed scheme can achieve$\mathcal {L}$-secure against adaptive attacks. Extensive experiments are carried out through the open-source real rule sets, which show that the proposed scheme can reduce communication time and communication overhead.
As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender’s identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender’s identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value).
Open access
Internet Traffic Analysis and Secure E-voting
Adversarial Robustness in Machine Learning
Advanced Steganography and Watermarking Techniques
Pei Zhang, Qingfeng Cheng, Mingliang Zhang, Xiangyang Luo
Covert communication is a crucial technology that hides information in the redundant structure of the file and transmission through public channel to achieve the secure delivery of information. The blockchain network, with the characteristics of anonymity, decentralization and tamper-proofing, can make up for the shortcomings of multimedia-based covert communication, which include the easy exposure of the identity for both parties, the vulnerability to destruction during communication and the weak robustness of the channel. Therefore, the blockchain network is an ideal channel for covert communication. Nevertheless, the existing covert communication methods face certain challenges based on blockchain, such as the lack of a secure channel for transferring the master key, low embedding capacity, and weak detection resistance. In view of this, this paper proposes a covert communication method based on Shamir threshold and STC mapping, which is suitable for public chain networks. The proposed method first decomposes the master key into sub-keys by introducing Shamir scheme, and the sub-keys are shared with the help of transaction amounts on a blockchain. Then, a mapping relation is established to ensure that the transaction amounts carrying the secret are evenly distributed. Finally, secret information is hidden in the mapping relationship and the transaction amount is interwoven, which is published to the blockchain through transactions to complete covert communication. The introduction of Shamir threshold breaks the limitation that master key cannot be safely transmitted due to the lack of a secure channel in the research of covert communication based on blockchain, thereby enhances the security of the method. Meanwhile, Shamir threshold scheme based on public chain, can solve the issue that the master key cannot be reconstructed due to dishonest participants providing invalid subkeys on traditional network. In addition, the proposed STC mapping can not only improve the detection resistance but also increase the embedding capacity. A series of experimental results illustrate that the proposed method is more resistant to detection, and the embedding efficiency is enhanced by 27.56 times compared with existing public chain-based covert communication methods, effectively reducing the number of transactions and saving resource consumption.
Advanced Steganography and Watermarking Techniques
Considered to be the next-generation (NextG) Internet, the Metaverse faces various security risks inherited from its predecessor and new specialized threats. It is even more challenging to mitigate these issues in a large-scale setting with numerous wearable devices such as augmented, virtual reality (AR/VR) headsets. In this article, we aim to analyze the security aspect of the Metaverse thoroughly, focusing on blockchain and machine learning (ML) solutions. Firstly, we present a 4-layer architecture of the Metaverse and discuss potential solutions for Metaverse security based on blockchain and ML. Next, we develop a decentralized collaborative intrusion detection system (CIDS) based on blockchain and federated learning (FL) that allows such the Metaverse users to collaboratively protect this digital world. This helps solving the scalability and single-point-of-failure (SPoF) issues of traditional security approaches. Finally, we outline some key challenges and discuss future research directions for Metaverse security.
Blockchain-based Electronic voting (e-voting) systems hold great promise for enhancing the efficiency and safeguarding the integrity of traditional electoral processes. The adoption of blockchain technology, a decentralized ledger system, has become essential to modern e-voting systems, fortifying the security of the electoral process. This research, introduces a practical perspective by fragmenting the blockchain into smaller, constituency-based blockchains, departing from the conventional monolithic single blockchain structure. This approach effectively tackles the formidable challenges associated with elections involving numerous constituencies and a vast electorate. The proposed constituency-based blockchain architecture significantly enhances the adaptability and performance of blockchain technology, enabling it to efficiently manage large voter populations in any country by offering practical recommendations for large scale elections.
Abstract Timed-release encryption (TRE) is a cryptographic primitive that can control the decryption time and has significant application value in time-sensitive scenarios. To solve the reliability issue of nodes in existing TRE anonymous interaction schemes, we propose a blockchain-based TRE protocol for anonymous query time trapdoors. In our protocol, the recipient divides the encrypted trapdoor request information into n ciphertext fragments using secret sharing technology near the decryption time, and employs the idea of onion routing to perform layer-by-layer encryption, creating onion-type data transmitted through middlemen selected from the smart contract. After receiving the ciphertext fragments, the time server integrates them to obtain the trapdoor request information and returns the corresponding time trapdoor to the recipient. This allows the recipient to query any time trapdoor anonymously. Our protocol provides a normative design for the smart contract and specific constraints on the participants’ behavior. Compared with the related anonymous query trapdoor schemes, our protocol improves the probability of successful queries. Security analysis shows that our protocol can resist release-ahead attack, interruption attack, eavesdropping attack, and replacement attack. Performance analysis shows that our protocol outperforms related protocols regarding anonymity, efficiency, and flexibility, achieving highly efficient anonymous interactions. Finally, we conducted an experiment in the Ethereum Rinkeby test network. For the settings of ciphertext fragment number $$n=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math> and ciphertext fragment threshold $$t=2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>t</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> , the gas consumption for a user to execute the contract was $5.66, which was higher than the contract cost of related schemes, but the contract execution cost was within an acceptable range.
Wisam Ali Mahmood, Jumana Waleed, Ayad R. Abbas, Haya Alaskar · 6 authors
Electronic voting systems have often associated with issues of security, transparency, and limited accessibility, particularly for people with disability, elderly, and visually impaired voters. These systems, impeded by complex authentication procedures and non-intuitive user interfaces, have also struggled to ensure voter trust and widespread participation. To address these issues, this paper presents a new electronic voting system that combines the gesture recognition with blockchain technology. The proposed system has two phases, the first one is a user identification phase using a pre-trained dataset based on both cvzone and Dlib tools for face and finger identifications. Gestures from the face and hands, such as eyebrow and nose movements are used as authentication mechanisms as well as a mean of input into the system. The second is a voting process phase which uses the blockchain technology. This new approach strengthens the security and transparency of voting through a tamper-proof ledger provided by the blockchain technology, as well as enhances considerably the inclusiveness and also user friendliness. Real time simulations show that the user interaction times for different age groups ranges between (24.3s to 29.7s) and the accuracy rate for gesture types varies between (88.7s to 92.5s). Moreover, the simulation results indicated that real time voting using our proposed system generated 100% precision and 99% recall. Our system showed high accuracy in gesture recognition and profound security when using Equal Error Rate quality measure. Different to other approaches, the interface of our proposed system, designed for a wide audience of users, greatly simplifies the voting process.
Voice over Internet Protocol (VoIP) networks are vulnerable to caller-ID (caller-identification) spoofing attacks due to the open nature of Session Initiation Protocol (SIP) signaling. Caller-ID spoofing is a critical security threat in modern telecommunication systems, allowing attackers to impersonate legitimate callers and gain access to sensitive information. While these attacks pose a significant threat to the telecom and financial industries, the existing solutions are limited to only closed-circuit options for subscribers of the same service provider. In this paper, we present a novel blockchain-based solution to effectively prevent caller-ID spoofing attacks in real time. Our approach employs a low-latency consensus algorithm to manage and verify end-to-end the caller-ID information of Internet Service Providers (ISPs) and institutions. We propose a two-step verification process, in which the accuracy and integrity of Automatic Number Identification (ANI) information is verified at different stages of the call. The proposed solution initiates a renewal of the ISP registration on every caller-ID change, making it unaffected by unusual situations such as roaming, the use of an IP-PBX (Internet Protocol Private Branch Exchange), or the use of a VPN (Virtual Private Network). We also discuss the proposed solution’s feasibility and potential deployment issues, including its integration into existing RFC (Request for Comments) efforts and the necessary regulations for service providers to demonstrate compliance. Furthermore, we address future research directions, such as handling complex call scenarios such as call forwarding and teleconference calls. Our approach not only improves the security of telecommunication systems but also provides an efficient and scalable solution to prevent caller-ID spoofing attacks.
Covert channels in blockchain networks achieve undetectable and reliable communication, while transactions incorporating secret data are perpetually stored on the chain, thereby leaving the secret data continuously susceptible to extraction. MTMM (IEEE Transactions on Computers 2023) is a state-of-the-art blockchain-based covert channel. It utilizes Bitcoin network traffic that will not be recorded on the chain to embed data, thus mitigating the above issues. However, we identify a distinctive pattern in MTMM, based on which we propose a comparison attack to accurately detect MTMM traffic. To defend against the attack, we present an improvement named ORIM, which exploits the permutation of transaction hashes within inventory messages to transmit secret data. ORIM leverages a pseudo-random function to obscure the transaction hashes involved in the permutation to ensure unobservability. The obfuscated values, rather than the original transaction hashes, are utilized to encode the confidential data. Furthermore, we introduce a variable-length encoding scheme predicated on complete binary trees. This scheme considerably amplifies the bandwidth and facilitates efficient encoding and decoding of secret data. Experimental results indicate that ORIM maintains unobservability and that ORIM’s bandwidth is approximately$3.7\times $of MTMM.
Advanced Steganography and Watermarking Techniques
The coming 6G wireless network is poised to achieve unprecedented data rates, latency, and integration with newer technologies like AI and IoE. On the other hand, along with this kind of growth in the AI domain and the large-scale connectivity in 6G. It is also going to raise many security concerns at the level of intrusion detection and prevention. For intrusion detection, centralized approaches won’t be able to work effectively, therefore there is an utmost need to design decentralized and privacy-preserving solutions. In this work, we propose a novel secure gradients exchange algorithm for distributed intrusion detection in 6G networks. Our method is designed to take into account the use of Federated Learning with secure multi-party computation and blockchain technology. This way ensures that the collaborating parties are able to conduct the training of intrusion detection models in a secure and collaborative manner by retaining privacy in the data. Gradient compression and adaptive secure aggregation strategies are used to further optimize communication overhead and computational complexity. Therefore, our design works in a robust and efficient manner with the high data rates and huge connectivity that 6G networks will provide. To achieve our goal, experiments using the CICIoT2023 dataset were performed, and results showed that our federated learning-based hybrid model composed of CNN1D and a multi-head attention mechanism outperformed other well-known deep learning models in terms of performance. It achieved the highest average accuracy with 79.92%, the highest average detection rate with 77.41%, and a low false alarm rate with 2.55%.
Minjae Seo, Jaehan Kim, Myoungsung You, Seungwon Shin · 5 authors
Blockchain technology has ushered in a transformative paradigm of decentralized and transparent systems, offering innovative solutions across diverse sectors. While these systems strive for unparalleled transparency and trustlessness in a fully distributed framework, permissionless blockchains, such as Bitcoin and Ethereum, encounter vulnerabilities due to their intrinsically public nature. Addressing these vulnerabilities, the emergence of permissioned blockchains presents a fortified alternative, incorporating rigorous access controls and authentication protocols to ensure participation exclusivity and transaction confidentiality. Nevertheless, a keen observation reveals that, despite encryption, the operational traffic within these blockchains manifests distinct time-series patterns and operational relations during sensitive data exchanges. Such patterns hold the potential to inadvertently expose critical details about the network, encompassing its topology and the operational dependencies among nodes. In light of this revelation, we introduce a pioneering blockchain fingerprinting mechanism, denoted as gShock. This system meticulously analyzes periodic patterns and the context of operational relations from the collected blockchain network traffic. It employs a Graph Neural Network (GNN)-based model, adept at capturing the intricate characteristics innate to specialized blockchain operations. Through empirical experiments conducted in a realistic permissioned blockchain environment, comprising various nodes, we ascertain that gShock demonstrates a remarkable proficiency in classifying blockchain operational traffic with an F1-score of$\geq 96$% and identifying individual dependencies with a macro F1-score of$\geq 93$%.
Open access
Advanced Steganography and Watermarking Techniques