In light of the escalating complexity of the cyber threat environment, the role of Collaborative Intrusion Detection Systems (CIDSs) in reinforcing contemporary cybersecurity defenses is becoming ever more critical. This paper presents a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF), an innovative methodology aimed at enhancing the efficacy of threat detection and information dissemination. To address the issue of alert collisions during data exchange, an Alternating Random Assignment Selection Mechanism (ARASM) is proposed. This mechanism aims to optimize the selection process of domain leader nodes, thereby partitioning traffic and reducing the size of conflict domains. Unlike conventional CIDS approaches that typically rely on independent node-level detection, our framework incorporates a Weighted Random Forest (WRF) ensemble learning algorithm, enabling collaborative detection among nodes and significantly boosting the system’s overall detection capability. The viability of the BCIDF framework has been rigorously assessed through extensive experimentation utilizing the NSL-KDD dataset. The empirical findings indicate that BCIDF outperforms traditional intrusion detection systems in terms of detection precision, offering a robust and highly effective solution within the realm of cybersecurity.
Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) because of the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while in reality it is not using anonymous routing and forwarding its networking directly to the destination peer to reduce the networking overheads. Using profile detection to detect anonymous routing and false claims based on the deterministic features are vulnerable to spoofing, especially in the permissionless cryptocurrency bypassing registration control. We therefore design and build network fingerprinting using the networking behaviors to detect and classify the networking types. We build a network sensor to collect data on an active Bitcoin node connected to the Mainnet and apply supervised machine learning to classify if a peer node is using IP (not anonymous), Tor, or I2P. Our results show that our scheme is effective in accurately detecting the networking types and identifying spoofing attempts through supervised machine learning. Our machine learning model accurately classifies the networking types and detects fake claims of Tor usage with 90% accuracy and false claims of I2P with 87% accuracy in permisionless Bitcoin.
Internet Traffic Analysis and Secure E-voting
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques
Web 3.0 marks the beginning of a new era for the internet, characterized by distributed technology that prioritizes data ownership and value expression. Web 3.0 aims to empower users by providing them with ownership and control of their data and digital assets rather than leaving them in the hands of large corporations. Web 3.0 relies on decentralization, which uses blockchain technology to ensure secure user communication. However, Web 3.0 still faces many security challenges that might affect its deployment and expose users' data and digital assets to cybercriminals. This survey investigates the current evolution of Web 3.0, outlining its background, foundation, and application. This review presents an overview of cybersecurity risks that face a mature Web 3.0 application domain (i.e., decentralized finance (DeFi)) and classifies them into seven categories. Moreover, state-of-the-art methods for addressing these threats are investigated and categorized based on the associated security risks. Insights into the potential future directions of Web 3.0 security are also provided.
The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in a practical data marketplace is not trivial. In this paper, several potential attacks are identified when applying the ZKCP protocol in a practical public data marketplace. To address these issues, we propose SmartZKCP, an enhanced solution that offers improved security measures and increased performance. The protocol is formalized to ensure fairness and secure against potential attacks. Moreover, SmartZKCP offers efficiency optimizations and minimized communication costs. Evaluation results show that SmartZKCP is both practical and efficient, making it applicable in a data exchange marketplace.
Zubaida Rehman, Mark Gregory, Iqbal Gondal, Hai Dong · 5 authors
This paper presents a comprehensive study on eclipse attacks in blockchain networks by describing how eclipse attacks work, their effects, detection, and prevention. In this context, understanding and controlling network-level attacks, such as eclipse attacks, is an essential task in relation to assurance and reliability for decentralized systems that utilize blockchain technology. An eclipse attack is a sequence of network-layer attacks that monopolize the connections to a target node to isolate it from the rest of the network. Eclipse attacks that focus on node discovery manipulation, can have a substantial impact on a blockchain network, by increasing transaction computation cost, transaction censorship, and consensus disruption. We studied eclipse attacks on a blockchain network. The attack vectors were associated with node discovery manipulation, network partitioning, and information flow exploitation. This paper also reviews state-of-the-art detection methods and prevention strategies, shedding light on their effectiveness and limitations. Awareness of eclipse attacks and their effect provides the motivation for further research in developing practical and resilient security measures for blockchain networks.
William Villegas-Ch, Jaime Govea, Rommel Gurierrez, Aracely Mera-Navarrete
The exponential growth of the Internet of Things (IoT) has boosted connectivity across various sectors, such as Industry 4.0 and smart cities. However, this expansion has also exposed IoT devices to critical vulnerabilities, including spoofing, DoS attacks, and unauthorized access. Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. In this work, we propose a hybrid solution that combines Blockchain and artificial intelligence (AI) to improve security and operational efficiency in IoT networks. Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. At the same time, the authentication latency is reduced to 15 ms in networks with 1000 connected devices, 66.6% faster than traditional solutions. In addition, the energy consumption of the hybrid system is 31.8% lower than that of conventional approaches, validating its scalability and efficiency in large-scale IoT networks.
Muwafaq Jawad, Ali A. Yassin, Hamid asadi, Zaid Ameen Abduljabbar · 7 authors
The Internet of Health Things (IoHT) is a network of healthcare devices, software, and systems that enable remote monitoring and healthcare services by gathering real-time health data through sensors. Despite its significant benefits for modern smart healthcare, IoHT faces growing security challenges due to the limited processing power, storage capacity, and self-defense capabilities of its devices. While blockchain-based authentication solutions have been developed to leverage tamper-resistant decentralized designs for enhanced security, they often require substantial computational resources, increased storage, and longer authentication times, hindering scalability and time efficiency in large-scale, time-critical IoHT systems. To address these challenges, we propose a novel four-phase authentication scheme comprising setup, registration, authentication, and secret construction phases. Our scheme integrates chaotic-based public key cryptosystems, a Light Encryption Device (LED) with a 3-D Lorenz chaotic map algorithm, and blockchain-based fog computing technologies to enhance both efficiency and scalability. Simulated on the Ethereum platform using Solidity and evaluated with the JMeter tool, the proposed scheme demonstrates superior performance, with a computational cost reduction of 40% compared to traditional methods like Elliptic Curve Cryptography (ECC). The average latency for registration is 1.25 ms, while the authentication phase completes in just 1.50 ms, making it highly suitable for time-critical IoHT applications. Security analysis using the Scyther tool confirms that the scheme is resistant to modern cyberattacks, including 51% attacks and hijacking, while ensuring data integrity and confidentiality. Additionally, the scheme minimizes communication costs and supports the scalability of large-scale IoHT systems. These results highlight the proposed scheme’s potential to revolutionize secure and efficient healthcare monitoring, enabling real-time, tamper-proof data management in IoHT environments.
Prof. R. C. Pachhade, Shubham Gaikwad, Aditya Sawwase, Dahihande Rohan
The idea focuses on enhancing the security and reliability of data exchange between military units. Traditional methods of secure communication often involve centralized systems, which can be vulnerable to breaches and single points of failure. By utilizing blockchain technology, the implementing idea introduces a decentralized approach that ensures data integrity and security through a distributed ledger system. In this system, blockchain provides a tamper-proof record of all communications, ensuring that data is encrypted, verified, and resistant to unauthorized access. This decentralized model eliminates the need for a central authority, reducing potential vulnerabilities and increasing the resilience of the communication network. As a result, the system aims to offer a more secure, reliable, and robust solution for confidential data transmission between army stations, enhancing operational security and efficiency.
F. B. Tebueva, V. I. Petrenko, D. Zh. Satybaldina, M. G. Ogur · 5 authors
Objective: to enhance the effectiveness of monitoring and responding to multivector attacks in a decentralized Internet of Things (IoT) environment by integrating federated learning, deep autoencoders, and the distributed IOTA ledger. The priorities include accurate attack detection, minimizing false positives, reducing response time, and preserving data privacy. Method: a problem-oriented system was developed, combining local monitoring on IoT nodes with autoencoders for anomaly detection, federated learning using the FedAvg algorithm for collective model updates, and decentralized alert dissemination via the distributed IOTA ledger. The system implements secure exchange of model parameters, digital message signing, and asynchronous response through a publish/subscribe network. Results: experimental studies on the real N-BaIoT dataset simulating multivector attacks demonstrated high detection accuracy (approximately 95%), achieving an F1-score above 94%, with false positive rates around 4%. The system's response time did not exceed 5 seconds, significantly improving operational reaction to attacks. Federated learning provided steady improvement in model quality considering data distribution and heterogeneity. The architecture proved scalable, fault-tolerant, and capable of effectively detecting complex threats across multiple system levels. Practical value: the solution is implementable in industrial IoT, smart cities, and medical networks to enhance cybersecurity while maintaining privacy and reducing network load. Scientific novelty: the study presents a comprehensive synthesis of federated learning, deep autoencoders, and distributed ledger technology for effective monitoring of multivector attacks in decentralized IoT environments. The proposed approach combines the advantages of distributed learning and blockchain mechanisms to achieve high adaptability, accuracy, and security in rapidly growing and diverse IoT infrastructures
In recent years, blockchain technology, as an innovative information technology, has received widespread attention in academia and industry. However, its limitations in mechanism design and the completeness of supporting infrastructure, combined with the immaturity of security concepts, have exposed blockchain systems to severe security threats and challenges. This study aims to address critical security issues in blockchain technology by proposing a blockchain security architecture based on Web attack principles. The architecture adopts a negotiated consensus mechanism and integrates real-time protection techniques from the field of cybersecurity, designing an innovative framework capable of identifying and restricting malicious nodes. With dynamic isolation as its core strategy, the architecture detects abnormal behaviors and temporarily isolates malicious nodes, preventing further damage to the blockchain network. The results demonstrate that this architecture successfully addresses the bottlenecks of inadequate targeted defense in existing blockchain systems and significantly improves operational efficiency and security. Experimental validation indicates that the architecture exhibits substantial practical value in scenarios such as decentralized finance (DeFi) and supply chain management, laying a solid foundation for the widespread application of blockchain technology in real-world settings.
The decentralized, transparent, and immutable ledger system of blockchain has fundamentally changed data security and digital transactions. Blockchain has built-in security safeguards, yet it is still vulnerable to flaws and attacks. In this review paper, the authors will examine the threats and vulnerabilities that blockchain technology faces and the mitigation factors that can be used to overcome these issues. The authors discuss the significant threats like the 51% attack, double spending attack and many more that compromise the integrity of blockchain technology further authors discusses the vulnerabilities that are present in consensus mechanisms, smart contracts, network level, cryptography and privacy. These vulnerabilities expose blockchain networks to potential exploits and operational risks. To overcome these threats and challenges, the paper also discusses several countermeasures that are used for strengthening the blockchain network. It includes consensus mechanism enhancement through hybrid models and enhancing network-level protection against DDoS and routing attacks. This paper also discusses about the significance of quantum resistance cryptographic algorithms, privacy-enhancing technologies like zero-knowledge proofs, and scalability solutions such as layer 2 protocols and sidechains. This review paper also includes the current research and advancements in security blocks and provides a detailed understanding of the present work and future initiatives in the blockchain system.
D. Kavitha, Kiruthika Venkataramani, N. R., S. Ravikumar
ABSTRACT The Internet of Things (IoT) is transforming numerous sectors but also presents unique security challenges due to its interconnected and resource‐constrained devices. This study introduces the Bidirectional Gaussian Hummingbird Optimized End‐to‐End Blockchain (BGHO‐E2EB) model, designed to detect and classify cyberattacks within IoT environments. Unlike preventive approaches, the developed model focuses on real‐time detection and categorization of attacks, enabling timely responses to emerging threats. The proposed model integrates blockchain technology through Ethereum‐based smart contracts to enhance the security and integrity of data exchanges within IoT networks. Additionally, a Gaussian Artificial Hummingbird Algorithm is employed for optimal feature selection, minimizing data dimensionality and computational load. A Bidirectional Long Short‐Term Memory (Bi‐LSTM) network further improves the model's capability by accurately detecting and categorizing cyber threats based on selected features. The Adam optimizer is used for efficient parameter tuning within the Bi‐LSTM network, ensuring high‐performance cyberattack detection. The proposed model was evaluated using established IoT security benchmarks, including the UNSW‐NB15, BOT‐IoT, and NSL‐KDD datasets, accomplishing an accuracy of 98.7%, precision of 96.3%, and security level of 99.5%, significantly outperforming traditional methods. These results demonstrate the effectiveness of BGHO‐E2EB as a robust tool for detecting and classifying cyberattacks in IoT networks, making it suitable for real‐world deployment in dynamic IoT environments where security is paramount.
The Internet of Vehicles (IoV), the latest generation of Vehicular Ad-hoc Networks (VANET) enables real-time, intelligent communication between vehicles and nearby transport infrastructures like roadside units, cloud servers, etc. It supports secure Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and other communication forms, which are vital in intelligent transportation systems. However, the dependence on wireless channels introduces risks, such as Man-In-The-Middle (MITM) attacks, replay attacks, and impersonation attacks, that lead to potential data leakage. Furthermore, many existing schemes rely on a centralized Trusted Authority (TA) for mutual authentication, which creates scalability limitations and increases latency. To address these challenges, this paper proposes a privacy-preserving mutual authentication and key agreement protocol for IoV using blockchain and a multi-TA network, which enables decentralized V2V and V2I authentication. Additionally, a lightweight Few-Shot Learning (FSL)-based module is integrated at the Zone Manager (ZM) level to classify real-time traffic conditions based on limited labeled samples. This enhances traffic control without compromising security. The distributed ledger managed by multiple TAs ensures synchronized access to authenticated credentials, facilitating secure communication across different zones. The security and performance analyses demonstrate the proposed scheme’s security and efficiency over existing methods.
Salabat Khan, Mansoor Khan, Muhammad Asghar Khan, Fei Luo · 9 authors
Authentication and revocation are the key mechanisms to ensure the security of the Cooperative Intelligent Transportation System (C-ITS). C-ITS relies on the Vehicular Public Key Infrastructure (VPKI) for anonymous authentication and device revocation. Several works complemented the VPKI-based authentication and revocation process. However, several security and performance issues exist in both mechanisms. This article presents PBatch: Pseudonym Certificate Batch Authentication based on Distributed Ledger Technology. PBatch addresses challenges specific to the authentication and revocation process to achieve 1000 authentications per second. PBatch relies on the concept of batching pseudonym certificates by offloading heavy validation operations such as certificate chain and revocation status validation to local edge servers. This enables vehicles to validate a batch of pseudonym certificates with a fixed number of verification operations, thus simplifying the authentication of the pseudonym certificate at the end devices. Furthermore, a caching-based message authentication mechanism is introduced to validate a relatively larger number of safety messages. We also introduced a Generative Artificial Intelligence (GAI) based cache management mechanism for safety messages caching and fetching. Finally, experiments and security analysis are conducted to investigate PBatch performance and security. The results show that PBatch is more secure, feasible, and scalable than the leading VPKI-based authentication proposals.
The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.
The growth of the Metaverse brings new security problems that traditional perimeter-based defenses can’t manage. This research proposes and tests an integrated Zero-Trust Architecture aimed to solve these weaknesses by merging artificial intelligence (AI)-driven behavioral threat detection, blockchain-based decentralized identification, and post-quantum cryptography. For anomaly detection, the architecture uses a federated ResNet-50 model; for data management that meets regulatory standards, it uses a Hyperledger Fabric-based identification system with Zero-Knowledge Succinct Non-Interactive Argument of Knowledge; and for key exchange that is immune to quantum attacks, it uses the CRYSTALS-Kyber algorithm. Penetration testing, a Delphi study with 20 experts, and user surveys all show that the architecture greatly improves security metrics. This system has a False Acceptance Rate (FAR) of 5.2%, which is 42.7% lower than the 9.1% FAR baseline of rule-based systems, 99.1% protection against Sybil attacks, and strong quantum resilience with a 1.2× latency penalty compared to AES-256. The approach also partially complies with the General Data Protection Regulation by using cryptographic erasure proofs. But these security improvements come at a cost: AI inference now uses 3.1 times more graphics processing unit resources. The results show that the suggested architecture creates a scalable, empirically validated basis for protecting decentralized virtual environments, striking a good balance between security, compliance, and performance trade-offs.