The rapid development of blockchain technology has led to a constant increase in its financial and technological value. However, this has also led to malicious attacks. Distributed denial-of-service attacks pose a considerable threat to blockchain technology out of many attacks due to its effectiveness and distributed nature. To protect the blockchain from DDoS attacks, researchers have proposed a large number of defensive schemes. However, these schemes are not well-suited for use in practical situations. In this work, we propose a DDoS attack detection scheme based on centralized federated learning, where multiple participating nodes locally train models and upload them to a central node for aggregation. Additionally, we propose a more suitable method for blockchain scenarios, using decentralized federated learning technology, where multiple nodes exchange models in a peer-to-peer manner to complete model training without a central server. We simulate DDoS attacks in blockchain and generate a large dataset by combining it with traditional network layer DDoS attack data to evaluate the effectiveness of our schemes. The experimental results show that the proposed schemes perform well in classification accuracy, demonstrating that our techniques can detect DDoS attacks effectively.
Adit Vakil, Manavi Jain, Shweta Joshi, Prof. Abhilasha Raghtate
The rapid rise of cryptocurrencies, particularly Bitcoin, has intensified research interest in understanding the factors influencing their price movements. Among these factors, social media sentiment has emerged as a crucial predictor, reflecting collective investor mood and market expectations. This paper provides a comprehensive survey of various sentiment analysis models applied in cryptocurrency markets, with a specific focus on the relationship between social media sentiment and Bitcoin price fluctuations. The study identifies the Aigents model as the most effective, showing significant improvements in predictive accuracy following fine-tuning. Findings reveal a predictive association between sentiment measures and price changes, typically with a latency of one to two days. The paper offers insights into the capabilities and limitations of existing Natural Language Processing (NLP) models in the context of cryptocurrency sentiment analysis, presenting practical implications for investors and analysts in navigating the volatile cryptocurrency markets. Key Words: cryptocurrency, Bitcoin, social media sentiment, natural language processing (NLP), sentiment analysis, price prediction, machine learning models, Aigents model, financial forecasting, Twitter, Reddit, artificial neural networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Random Forest, Naive Bayes, Extreme Gradient Boosting (XGBoost), predictive analytics, market behavior, interpretable AI.
Stefano Ferretti, Gabriele DâAngelo, Vittorio Ghini
This paper presents a study on the application of Heterogeneous Graph Neural Networks (HGNNs) for enhancing the security of complex social systems by identifying illicit and malicious behaviors. We focus on digital asset tokenization, a key component in the construction of many innovative social services, with the aim of classifying token exchanges and identifying illicit activities. Utilizing the Elliptic++ dataset, we demonstrate the efficacy of HGNNs in identifying illicit activities in token-based exchanging applications. In particular, we evaluate four different HGNN architectures, i.e. Heterogeneous GAT, Heterogeneous SAGE, HGT (Heterogeneous Graph Transformer), and HAN (Heterogeneous Attention Network). Our results underscore the importance of characterizing and describing interactions in these complex systems, both for studying the system dynamics and for activating mechanisms to cope with cybersecurity issues, like misuses and usurpation of resources in social systems.
G. Rajesh, Mercilin Raajini, S. Meyyappan, Rajapriya Raja ¡ 6 authors
Securing the transfer of messages is a challenging task for communication between vehicles in Vehicular Ad-Hoc Networks (VANETs). The effectiveness of vehicle-to-vehicle communication is of top priority in VANETs, where the lives and possessions of drivers are at stake. The ultimate objective of a vehicle network is to ensure fast and proper broadcasting of information concerning life-threatening incidents, such as traffic jams and accident reports. However, these messages are prone to attacks by misleading intruders attempting to modify or alter them during communication. Emergency communications are basically segregated on the basis of the message schema and safety level, primarily due to the lack of quick and reliable communication and the need to detect attacks. This paper proposes an efficient lightweight blockchain-based authentication system for the secure transfer of emergency messages in Vehicular Ad-Hoc Networks (VANETs). The main objectives are to develop an intrusion detection system for V2V communication using machine learning techniques and to use a lightweight Rivest-Shamir-Adleman(RSA) technique with three prime integers to ensure authentication. Additionally, Ganache and Ethereum blockchain technologies are utilized to create a secure message storage and retrieval system. The proposed system demonstrates improved encryption and decryption times and enhanced attack detection accuracy.
Kai Wang, Michael Wen Tong, Jun Pang, Jitao Wang ¡ 5 authors
Recently, there is a surge in ransomware activities that encrypt usersâ sensitive data and demand bitcoins for ransom payments to conceal the criminalâs identity. It is crucial for regulatory agencies to identify as many ransomware addresses as possible to accurately estimate the impact of these ransomware activities. However, existing methods for detecting ransomware addresses rely primarily on time-consuming data collection and clustering heuristics, and they face two major issues: (1) The features of an address itself are insufficient to accurately represent its activity characteristics, and (2) the number of disclosed ransomware addresses is extremely less than the number of unlabeled addresses. These issues lead to a significant number of ransomware addresses being undetected, resulting in a substantial underestimation of the impact of ransomware activities. To solve the above two issues, we propose an optimized ransomware address detection method based on Bitcoin transaction relationships, named XRAD , to detect more ransomware addresses with high performance. To address the first one, we present a cascade feature extraction method for Bitcoin transactions to aggregate features of related addresses after exploring transaction relationships. To address the second one, we build a classification model based on Positive-unlabeled learning to detect ransomware addresses with high performance. Extensive experiments demonstrate that XRAD significantly improves average accuracy, recall, and F1 score by 15.07%, 19.71%, and 34.83%, respectively, compared to state-of-the-art methods. In total, XRAD detects 120,335 ransomware activities from 2009 to 2023, revealing a development trend and average ransom payment per year that aligns with three reports by FinCEN, Chainalysis, and Coveware.
Smart contracts have brought new developments to Blockchain technology, quickly finding widespread application in finance, law, and business sectors upon their emergence. However, security vulnerabilities in smart contracts have limited their growth. The existing methods for detecting vulnerabilities in smart contracts primarily focus on Ethereum's Solidity, lacking of vulnerability detection tools based on other smart contract languages such as C/C++. Therefore, this paper investigates the current state of vulnerability detection research for smart contract languages other than Ethereum's Solidity and proposes a vulnerability detection method for C/C++ smart contracts based on intermediate value transformation. This method combines Clang technology with the symbolic execution tool KLEE[l], utilizing the Intermediate Representation (IR) from LLVM (Low Level Virtual Machine) for research, andproduces a detection method capable of identifying eight types of code level vulnerabilities. Finally, it summarizes the current shortcomings of smart contract vulnerability detection methods and future research directions.
In essence, blockchain is a distributed, secure ledger that contains a hierarchical network of blocks that maintains a record of all transactions. Bringing cloud capabilities closer to computation tasks is the goal of edge computing. It is possible to overcome existing security and scalability issues with blockchain and edge computing. Security is strengthened by the integration of Blockchain technology into Industrial IoT (IIoT) intrusion detection frameworks. In the proposed framework, the decentralized and tamper-proof nature of Blockchain is leveraged to enhance trust and integrity at the edge of the network in the detection of malicious activities. It aims to identify and mitigate cyber threats in IIoT systems for a robust security solution. The framework's performance is measured using various IIoT scenarios, including detection accuracy, response time, delay time, and overall performance score. A combination of blockchain technology and edge-based intrusion detection significantly improves the security posture of IIoT networks, producing high detection accuracy and minimal delays. A more resilient, trustworthy and secure IIoT network can be achieved with this study, which advances secure IIoT architectures.
Tuan-Dung Tran, Nguyen Anh Tai, Tran The Anh, Phan The Duy ¡ 5 authors
The proliferation of connectivity through modern telecommunications has led to increased unwanted and disruptive calls. Such communications negatively impact user experience and trust in platforms. Currently, call filtering relies on centralized architectures that aggregate vast troves of sensitive user data within single entities, compromising privacy and ownership. Users have limited visibility into how inputs inform labeling, challenging autonomy and oversight. We present the Sentinel Call Platform, a novel blockchain-powered decentralized framework to mitigate unsolicited calls. It establishes a permissionless blockchain tailored for immutable storage of call logs and community rules, and employs a Proof-of-Spam consensus, facilitating transparent flagging of suspicious numbers through democratic participation. An initial prototype demonstrates authenticating calls while preserving anonymity. By removing centralized data flow and governance models, the solution aims to restore transparency, autonomy and trust. The modular framework integrates applications and consensus optimization. Evaluations indicate ability to handle throughput loads. This decentralized alternative enhances user protection against disruptive communications through distributed, open solutions with implications for blockchain application across data sovereignty domains.
Amjad Aldweesh, Mohammad Alauthman, Ahmad AlâQerem, Abdelraouf Ishtaiwi ¡ 6 authors
Blockchain's decentralized trust models are spurring rapid advances in applied cryptography. This chapter explores the accelerating convergence between cryptographic innovations and next-gen blockchain platforms. The authors provide background on modern cryptography and the distributed ledger tech behind blockchains. They then analyze how blockchain architectures are driving progress in zero-knowledge proofs, digital signatures, and secure multiparty computation. In turn, these advancing cryptographic mechanisms enable critical blockchain capabilities around privacy, scalability, accountability, and governance. The authors highlight gaps in existing cryptography-blockchain integration and present post-quantum research directions that fulfill decentralized ledgers' unique security needs. Their work frames a symbiotic blockchain-cryptography ecosystem where progress in both fields builds on each other to overcome limitations. This interdisciplinary synergy will catalyze more secure, functional, and efficient decentralized technologies.
Firmware updates are crucial for patching vulnerabilities in IoT devices within industrial control systems (ICS). However, this process poses security risks, especially with the increasing of Industry 4.0. While blockchain technology has for enhancing firmware update security, existing solutions overlook low-capability IoT devices and rely on centralized storage. This paper proposed a secure firmware update mechanism for Industrial IoT (IIoT) devices. Design the PULL delivery mechanisms using distributed ledger technology (DLT), specifically IOTA Streams for message publication, and decentralized storage for firmware files. Our approach ensures integrity and availability while facilitating the real-time execution of the latest updates on IIoT devices.
The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions.
Blockchain fuelled the innovation of numerous application fields. In particular, Web3 applications benefit the most because blockchain can be used to implement a rewarding system for users that contribute the most, thus increasing the overall social good provided by these platforms. One of the sectors that has benefited most from blockchain technology is the gaming sector through the so-called Play-to-Earn (P2E) model. The P2E Blockchain Video Games allow players to earn rewards in the form of tokens or NFTs, by having an impact on the social good. Unfortunately, bot accounts could exploit these platforms, which defeats the purpose of having a reward system because they invalidate the social good introduced by the rewards. In this paper, we provide an analysis geared towards detecting suspicious behaviour in P2E blockchain-based games by exploiting Gods Unchained as a case study. Using the gameâs official APIs, we download 12 monthsâ worth of playersâ activity. Analysing the data, we detect two groups of players with abnormal activity. Additionally, analysing the playersâ graph, we find communities made of the best players with similar activity. Lastly, we observe that users with suspicious behaviour belong to these communities.
The last few years have witnessed the widespread use of blockchain technology in several works, due to its effectiveness in terms of privacy, security, and trustworthiness. However, the Cyber-attacks challenges represent a real threat to systems based on this technology. The resort to the systems of anomaly detection focused on deep learning, also called deep anomaly detection, is an appropriate and efficient means to tackle cyber-attacks on the blockchain. This paper provides an overview of the blockchain technology concept, its characteristics, challenges and limitations, and its systems taxonomy. Numerous blockchain cyber-attacks are discussed such as 51% attacks, selfish mining attacks, double spending attacks, and Sybil attacks, etc. Furthermore, we surveyed an overview of deep anomaly detection systems with their challenges and unresolved issues. In addition, this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment, also presenting several methods that enhance the security features of anomaly detection systems. Finally, we discussed the benefits and drawbacks of these recent advanced approaches in light of three categories, which are discriminative, generative, and hybrid learning with other methods based on graphs and highlighting the ability of the proposed approaches to perform real-time anomaly detection.
Nimish Kumar Patel, Narendra Shirke Anagha, Santhosh Kumar B J
Intrusion Detection and prevention System are most common in cyber security. The aim of this undertaking is to delve into blockchain security and specifically apply machine learning techniques in building an Intrusion Detection and Prevention System (IDPS). Given the increasing popularity of blockchain technology, it has become more important to ensure the integrity, privacy and security of distributed ledgers. Our study intends to counteract this by examining new methods for data analysis which can help identify and prevent various forms of threats on blockchain networks. The IDPS will detect any malicious activity or suspicious moves through streaming real-time information about block chain. In this research paper we have evaluated effectiveness and efficacy of our proposed methodology in protecting blockchain networks from such types of attacks as well as intrusions.
Bitcoin, which was launched in 2009, is one of the most popular blockchains. To sustain and secure the system, enough mining power must be needed. However, various factors can encourage miners to leave the Bitcoin network. This risk should be assessed in advance by introducing metrics to detect hash rate changes. For this purpose, this paper proposes a statistical method to determine whether there is a significant difference in computational power for the two given periods. Our proposal consists of three hypothesis tests that consider Bitcoin difficulty adjustments. The first and second tests detect hash rate changes in the mining resources across the Bitcoin network. The third focuses on shifts in the distribution of mining power among different pools. We conducted simulations to elucidate the statistical properties of the detection power of the first and second methods. In addition, we apply our three statistical tests to the Bitcoin block data at height 756,000 through 836,640 (from September 28, 2022 to March 28, 2024). Based on the inference results, we discuss how the mining power had fluctuated by some intervention effects, such as the drop in the Bitcoin price. This provides insights to understand and evaluate the stability of Bitcoin.
Ransomware attacks, exploiting cryptocurrencies like Bitcoin for ransom payments, represent a significant cybersecurity threat. Detecting these malicious activities within the Bitcoin network is challenging due to complex transaction patterns and blockchainâs inherent anonymity. Understanding these patterns is crucial for effective defense mechanisms. However, existing research lacks comprehensive analysis of ransomware behavior on the Bitcoin network, leaving gaps in understanding. Moreover, current detection strategies often struggle to accurately identify ransomware activities. To address these gaps, this study conducted experimental research using the BitcoinHeist dataset. Employing machine learning techniques and feature engineering, the analysis aims to decipher transaction patterns and identify ransomware characteristics. The model achieves an accuracy of 85%, demonstrating its effectiveness in detecting ransomware activities. By bridging theoretical knowledge with empirical analysis, this research enhances understanding and aids in developing robust defense strategies against ransomware attacks.
Togzhan Barakbayeva, Zhuo Cai, Amir Kafshdar Goharshady, Karaneh Keypoor
Correlated equilibria are a standard solution concept in game theory and generalize Nash equilibria. In a 2-player non-cooperative game in which player i has action set A_i, a correlated equilibrium is a self-enforcing probability distribution Ď over A_1 * A_2. Specifically, when a strategy profile (s_1, s_2) in A_1 * A_2 is sampled according to Ď, each player i can observe their own component s_i, but not the other player's component. Knowing s_i and Ď, player i cannot increase their expected payoff by defecting and playing a strategy s'_i different from s_i. Correlated equilibria are ubiquitous and crucial in mechanism design, including in the design of blockchain-based protocols which aim to incentivize honest behavior. A correlated equilibrium depends on a centralized and impartial oracle, often called the ''external signal'' in game theory literature, to sample a strategy profile and disclose each player's component to them, while keeping the other player's component secret. However, there is currently no trustless method to achieve this on the blockchain without centralization or relying on trusted third-parties. In this work, we address this challenge and provide two novel protocols, one based on oblivious transfer and the other based on zkSNARKs to replace the public signal with a smart contract. We prove that our approaches are secure and provide the desired privacy properties of a correlated equilibrium, while also being efficient in terms of gas usage and thus affordable in practice.
V. R. Elangovan, S. Thanga Revathi, Nusrat Jabeen T, Ahmed J. Obaid ¡ 5 authors
Block chain technology has been used in many different fields, including banking, logistics, healthcare, and government. However, billions have been lost to cyber attacks on block chain apps in the last several years. An efficient approach for identifying malicious behavior in block chain networks is urgently required. Anomaly detection is a well-studied issue with a lengthy history of research. Anomalies are, in a nutshell, unusual or improbable occurrences. Theft and other illicit activity in financial networks are often outliers. Participants in the network want to spot anomalies as soon as possible to safeguard the overall safety and security of the system. However, fraud and anomaly detection techniques are constantly developing along with the financial industry. Furthermore, the most secure approach being brought into money is block chain technology. The number of scams, however, increases every year alongside these innovative technology. This is why we suggested a safe Anomaly detection technique that combines Machine Learning with Deep Learning. For the purpose of classifying Anomaly transactions, we used a unique hybrid RFMLP approach. For Bitcoin transaction anomaly detection, the RFMLP combines Random Forest (RF) with multilayer perceptron (MLP). We found that our RFMLP was more accurate than competing algorithms.
The Internet of Things (IoT) has revolutionized industries and daily life by connecting devices, sensors, and systems to autonomously collect, process, and share data. While offering immense possibilities, the distributed nature of IoT networks introduces significant security challenges. Conventional security measures struggle to address IoT's unique needs, and blockchain solutions face limitations. IOTA presents the Tangle, a novel Distributed Ledger Technology (DLT) tailored for IoT applications, offering feeless transactions, scalability, and decentralized consensus. This survey explores IOTA-based security solutions, focusing on access control, authentication, data integrity, confidentiality, threat modeling, risk assessment, performance, and scalability in IoT environments. Through a comprehensive review of literature, we highlight the efficacy of IOTA in enhancing IoT security. We identify future research directions, including interoperability, integration with emerging technologies, real-world deployments, and advanced trust management, to further strengthen IoT security using IOTA technology.
The advent of 6G technology has paved the way for unprecedented advancements in the Internet of Things (IoT), ushering in an era of hyper-connectivity and ubiquitous communication. However, with the proliferation of interconnected devices in 6G-enabled IoT ecosystems, the risk of malicious intrusions and new cyber threats becomes more prominent. Furthermore, the incorporation of AI into 6G networks introduces additional security concerns, such as the risk of adversarial attacks on AI models and the potential misuse of AI for cyber threats. Consequently, securing the extensive and diverse array of connected devices poses a substantial challenge in the 6G environment and needs reconsideration of prior security traditional methods. This paper aims to address these challenges by proposing a novel collaborative intrusion detection system (CIDS) that relies on AI and blockchain technologies. The collaborative nature of the proposed CIDS fosters a collective defense approach, where nodes within the IoT network actively share threat intelligence, enabling rapid response and mitigation. The effectiveness of the proposed system is evaluated through comprehensive simulations and proof-of-concept experiments. The results demonstrate the systemâs ability to effectively detect and mitigate falsified and zero-day attacks, thereby fortifying the security infrastructure of 6G -enabled IoT environments.
Andrew Jeffery, Julien Maffre, Heidi Howard, Richard Mortier
Software services are increasingly migrating to the cloud, requiring trust in actors with direct access to the hardware, software and data comprising the service. A distributed datastore storing critical data sits at the core of many services; a prime example being etcd in Kubernetes. Trusted execution environments can secure this data from cloud providers during execution, but it is complex to build trustworthy data storage systems using such mechanisms. We present the design and evaluation of the Ledger-backed Secure Key-Value datastore (LSKV), a distributed datastore that provides an etcd-like API but can use trusted execution mechanisms to keep cloud providers outside the trust boundary. LSKV provides a path to transition traditional systems towards confidential execution, provides competitive performance compared to etcd, and helps clients to gain trust in intermediary services. LSKV forms a foundational core, lowering the barriers to building more trustworthy systems.
The application of Internet of Vehicles (IoV) technology has greatly improved usersâ driving experience, but it also faces some challenges: 1) the central server is not powerful enough to support the rapid growth of IoV identity authentication requests and 2) there is a privacy leakage issue during vehicle authentication. To address these issues, we propose an anonymous authentication scheme based on trustworthy roadside unit group (TRUG)-PBFT main secondary chains and zero-knowledge proof (ZKP). First, to enhance authentication efficiency, we propose the TRUG-PBFT consensus algorithm. It improves the traditional PBFT by optimizing the PBFT consensus process, reducing the number of consensus nodes using fractional grouping, and selecting main node using verifiable random functions (VRFs). Second, we use a lattice-based ZKP scheme to achieve anonymous authentication of vehicles, and important data in the vehicle authentication process is stored by the main chain maintained by the base station group and the secondary chain maintained by the roadside unit group. Finally, experimental results demonstrate that compared to PBFT consensus, TRUG-PBFT in terms of consensus efficiency is improved by approximately 33%, and the authentication schemeâs computational cost is only 7.08 ms, superior to existing authentication schemes.
Khadija Begum, Md Ariful Islam Mozumder, Moon-Il Joo, HeeâCheol Kim
The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.