Mimouna Abdullah Alkhonaini, Manal Abdullah Alohali, Mohammed Aljebreen, Majdy M. Eltahir · 8 authors
Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.
Zheng Che, Meng Shen, Zhehui Tan, Hanbiao Du · 9 authors
With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, we first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method.
Cyber-Physical Systems (CPS) have become a research hotspot due to their vulnerability to stealthy network attacks like ZDA and PDA, which can lead to unsafe states and system damage. Recent defense mechanisms for ZDA and PDA often rely on model-based observation techniques prone to false alarms. In this paper, we present an innovative approach to securing CPS against Advanced Persistent Threat (APT) injection attacks by integrating machine learning with blockchain technology. Our system leverages a robust ML model trained to detect APT injection attacks with high accuracy, achieving a detection rate of 99.89%. To address the limitations of current defense mechanisms and enhance the security and integrity of the detection process, we utilize blockchain technology to store and verify the predictions made by the ML model. We implemented a smart contract on the Ethereum blockchain using Solidity, which logs the input features and corresponding predictions. This immutable ledger ensures the integrity and traceability of the detection process, mitigating risks of data tampering and reducing false alarms, thereby enhancing trust in the system's outputs. The implementation includes a user-friendly interface for inputting features, a backend for data processing and model prediction, and a blockchain interaction module to store and verify predictions. The integration of blockchain with Machine learning enhances both the precision and resilience of APT detection while providing an additional layer of security by ensuring the transparency and immutability of the recorded data. This dual approach represents a substantial advancement in protecting CPS from sophisticated cyber threats.
Clement Daah, Amna Qureshi, Irfan Awan, Savas Konur
The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.
Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these approaches often suffer from high false positive rates and lack the ability to directly illustrate how vulnerabilities can be exploited in attacks.
The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. The current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. In this paper, we present ScamSweeper, a novel framework to identify web3 scams on Ethereum. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts. Our experiments indicate that ScamSweeper exceeds the state-of-the-art in detecting web3 scams.
Cross-chain bridges, one of the foundational infrastructures of blockchain, provide the infrastructure and solutions for inter-operability, asset liquidity, data transfer, decentralized finance, and cross-chain governance between blockchain networks. However, because cross-chain bridges often have to handle communication and asset transfers between multiple blockchains, they involve complex protocols and technologies. This complexity increases the likelihood of vulnerabilities and potential attacks. In order to ensure the security and reliability of cross-chain bridges, this article launches a thorough investigation of existing cross-chain bridge projects, clarifying bridging mechanisms, bridge types, and security features. The following part goes into the subject of security and sheds light on the considerable challenges faced by cross-chain bridges. It conducts a thorough analysis of security flaws, covering problems like smart contract vulnerabilities, centralization risks, liquidity issues, and oracle manipulations. Furthermore, this study promotes a compendium of security solutions and best practises, pointing the way toward a cross-chain bridge scenario that is more secure.
Anand Srivatsa, Ananthapadmanabha Thammaiah, M. V. Likith Kumar, D Rajeshwari · 5 authors
Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internet-connected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance.
André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors
Cross-chain bridges are a type of middleware for blockchain interoperability that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose \toolName~(Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using \toolName, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. \toolName~was able to successfully identify the transactions that led to losses of \$611M and \$190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (\CCTX) that these bridges should not have accepted, failed attempts to exploit Nomad, over \$7.8M worth of tokens locked on one chain but never released on Ethereum, and \$200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 \CCTXS~across three blockchains, capturing more than \$4.2B in token transfers.
Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures.
Francisco Javier Aguilar Feijóo, Diego Fernando Andaluz Espinosa
This research aims to determine the incidence of computer attacks on servers with the Linux operating system of local government entities. The study is limited to the decentralized autonomous government (GAD) of the Ecuadorian Amazon. Initially, the most common computer attacks that have affected organizations in recent years were determined using statistical reports from important computer security companies positioned as leaders in Gartner’s magic quadrant. Phishing and distributed denial of service (DDoS) attacks are established as computer attacks under study. Computer attacks are carried out before and after mitigation measures are established. With the help of the information systems risk analysis and management methodology (MAGERIT), the vulnerability, level of impact, and risk computer attacks cause on servers with the Linux operating system are determined. This research aims to serve as a guide to the information technology departments of local governments in implementing mechanisms that safeguard the most important asset of an organization, such as information. Keywords: computer attack, phishing, DDoS, MAGERIT, Linux. Resumen La presente investigación tiene como finalidad determinar la incidencia de los ataques informáticos en los servidores con sistema operativo Linux de entidades de gobierno local. El estudio está delimitado a un gobierno autónomo descentralizado (GAD) de la Amazonía ecuatoriana. Inicialmente se determina los ataques informáticos más comunes que han afectado a las organizaciones en los últimos años haciendo uso de reportes estadísticos de importantes empresas de seguridad informática posicionadas como líderes en el cuadrante mágico de Gartner. Se establece como ataques informáticos objeto de estudio los ataques de phishing y de denegación de servicio distribuido (DDoS). Se realizan ataques informáticos antes y después de establecer las medidas de mitigación y con la ayuda de la metodología de análisis y gestión de riesgos de los sistemas de información (MAGERIT) se determina la vulnerabilidad, el nivel de impacto y el riesgo que los ataques informáticos provocaban en los servidores con sistema operativo Linux. El presente trabajo de investigación pretende ser de gran utilidad y servir de guía a los departamentos de tecnologías de la información de gobiernos locales en la implementación de mecanismos que salvaguarden el activo más importante de una organización como lo es la información. Palabras Clave: ataque informático, phishing, ddos, magerit, linux.
Fatma S. Alrayes, Mohammed Aljebreen, MOHAMMED ALGHAMDI, Faheed A. F. Alrslani · 8 authors
Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.
In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure real-time responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorr-like zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.
The Internet of Things (IoT) refers to a network where different smart devices are interconnected through the Internet. This network enables these devices to communicate, share data, and exert control over the surrounding physical environment to work as a data-driven mobile computing system. Nevertheless, due to wireless networks' openness, connectivity, resource constraints, and smart devices' resource limitations, the IoT is vulnerable to several different routing attacks. Addressing these security concerns becomes crucial if data exchanged over IoT networks is to remain precise and trustworthy. This study presents a trust management evaluation for IoT devices with routing using the cryptographic algorithms Rivest, Shamir, Adleman (RSA), Self-Adaptive Tasmanian Devil Optimization (SA_TDO) for optimal key generation, and Secure Hash Algorithm 3-512 (SHA3-512), as well as an Intrusion Detection System (IDS) for spotting threats in IoT routing. By verifying the validity and integrity of the data exchanged between nodes and identifying and thwarting network threats, the proposed approach seeks to enhance IoT network security. The stored data is encrypted using the RSA technique, keys are optimally generated using the Tasmanian Devil Optimization (TDO) process, and data integrity is guaranteed using the SHA3-512 algorithm. Deep Learning Intrusion detection is achieved with Convolutional Spiking neural network-optimized deep neural network. The Deep Neural Network (DNN) is optimized with the Archimedes Optimization Algorithm (AOA). The developed model is simulated in Python, and the results obtained are evaluated and compared with other existing models. The findings indicate that the design is efficient in providing secure and reliable routing in IoT-enabled, futuristic, smart vertical networks while identifying and blocking threats. The proposed technique also showcases shorter response times (209.397 s at 70% learn rate, 223.103 s at 80% learn rate) and shorter sharing record times (13.0873 s at 70% learn rate, 13.9439 s at 80% learn rate), which underlines its strength. The performance metrics for the proposed AOA-ODNN model were evaluated at learning rates of 70% and 80%. The highest metrics were achieved at an 80% learning rate, with an accuracy of 0.989434, precision of 0.988886, sensitivity of 0.988886, specificity of 0.998616, F-measure of 0.988886, Matthews Correlation Coefficient (MCC) of 0.895521, Negative predictive value (NPV) of 0.998616, False Positive Rate (FPR) of 0.034365, and False Negative Rate (FNR) of 0.103095.
Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.
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 D’Angelo, 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.
Kai Wang, Michael Wen Tong, Jun Pang, Jitao Wang · 5 authors
Recently, there is a surge in ransomware activities that encrypt users’ sensitive data and demand bitcoins for ransom payments to conceal the criminal’s 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.
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 game’s official APIs, we download 12 months’ worth of players’ activity. Analysing the data, we detect two groups of players with abnormal activity. Additionally, analysing the players’ 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.
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 σ over A_1 * A_2. Specifically, when a strategy profile (s_1, s_2) in A_1 * A_2 is sampled according to σ, each player i can observe their own component s_i, but not the other player's component. Knowing s_i and σ, 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.
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.
Khadija Begum, Md Ariful Islam Mozumder, Moon-Il Joo, Hee‐Cheol 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.