Blockchain technology has transformed how data are stored and transactions are processed in a distributed environment. Blockchain assures data integrity by validating transactions through the consensus of a distributed ledger involving several miners as validators. Although blockchain provides multiple advantages, it has also been subject to some malicious attacks, such as a 51% attack, which is considered a potential risk to data integrity. These attacks can be detected by analyzing the anomalous node behavior of miner nodes in the network, and data analysis plays a vital role in detecting and overcoming these attacks to make a secure blockchain. Integrating machine learning algorithms with blockchain has become a significant approach to detecting anomalies such as a 51% attack and double spending. This study comprehensively analyzes various machine learning (ML) methods to detect anomalies in blockchain networks. It presents a Systematic Literature Review (SLR) and a classification to explore the integration of blockchain and ML for anomaly detection in blockchain networks. We implemented Random Forest, AdaBoost, XGBoost, K-means, and Isolation Forest ML models to evaluate their performance in detecting Blockchain anomalies, such as a 51% attack. Additionally, we identified future research directions, including challenges related to scalability, network latency, imbalanced datasets, the dynamic nature of anomalies, and the lack of standardization in blockchain protocols. This study acts as a benchmark for additional research on how ML algorithms identify anomalies in blockchain technology and aids ongoing studies in this rapidly evolving field.
The chapter aims to illuminate the digital forensics of cryptocurrencies and the dark web by reviewing the role of the elements and tools involved in their formation, such as blockchain, computers, and mobile phones, and learning evidence. It will focus on its methods and review the extent to which artificial intelligence and machine language can be relied upon in forensics on the dark web. The chapter identified the need to address several areas of digital cryptocurrency forensics, in which gaps can be filled by developing advanced solutions for cryptocurrency forensics. Further investigation is required in digital forensics concerning significant cryptocurrencies like Monero, Ethereum, Verge, Dogecoin, and others. This is necessary because these currencies are becoming increasingly popular among both legitimate users and evil individuals. The survey highlighted another research gap: the limited amount of substantial research on host-based cryptocurrency forensics, particularly in mobile-based cryptocurrency forensics.
Shereen Ismail, Raouf Mehannaoui, Eden Teshome Hunde, Hassan Reza
IoT devices are typically resource-constrained, with limited computational power, storage, and energy. Holochain, an emerging distributed ledger technology (DLT), offers the benefits of blockchain while overcoming its limitations, such as the reliance on consensus algorithms and a globally synchronized ledger. As a result, Holochain has garnered attention in the research community as a promising solution for distributed IoT applications. This paper reviews various DLTs in IoT distributed networks, focusing on the motivation for utilizing Holochain in these environments. We explore its key applications, challenges, and research insights. We propose the HoloSec framework, a conceptual security framework for IoT distributed networks that leverages Holochainâs agent-centric architecture, advanced cryptography, and machine learning (ML). The paper also illustrates the setup and implementation of a Holochain-based IoT network for a healthcare scenario and compares the performance of Holochain with traditional blockchain solutions. Initial experimental results show that Holochain achieves a latency of around 50 ms for data publishing and 30 ms for retrieval, with a throughput of approximately 20 transactions per second (TPS) on a single node, significantly outperforming blockchain, which shows higher latency (200 ms publish, 100 ms retrieve) and lower throughput (10 TPS). Finally, we examine key challenges associated with Holochain and outline future research directions aimed at enhancing its interoperability, scalability, security, and regulatory compliance in IoT environments.
Due to the immutable nature of blockchain, vulnerability detection in on-chain smart contracts is imperative to ensure the security of blockchain transaction. As smart contracts automate significant financial and operational transactions, detecting vulnerabilities before they are exploited is critical. Recently, the application of machine learning techniques to this domain has increased, primarily due to their powerful feature extraction capabilities and operational efficiency in detecting anomalies. Considerable efforts in past research have focused on mining semantic and syntactic features from off-chain source code of smart contracts, typically written in high-level languages like Solidity. However, on-chain smart contracts, which are represented in the form of opcodes, lack these high-level semantic features. This absence necessitates different approaches for effective vulnerability detection. Although on-chain smart contracts lack high-level semantic features, the limited number of characters in opcodes results in more distinct frequency patterns of code. Therefore, in this paper, we explore a multi-class vulnerability detection approach based on the frequency features of smart contract opcodes. This paper provides a simple yet effective feature embedding method for on-chain opcode contract. Experiments on both binary and multi-class vulnerability detection tasks have been conducted to validate its scalability and effectiveness.
Bhupinder Kaur, Deepak Prashar, Leo MrĹĄiÄ, Ahmad Almogren ¡ 7 authors
Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.
Ahmed Abubakar Aliyu, Mohammed Ibrahim, Saâadatu Abdulkadir
Traditional Intrusion Detection Systems (IDSs) face significant challenges in keeping pace with the rapidly evolving landscape of cyber threats, primarily due to limitations in continuous learning and the accuracy of data classification and analysis. This often results in delayed detection and leaves networks susceptible to severe attacks. This paper introduces an innovative IDS empowered by blockchain technology to mitigate these shortcomings, leveraging continuous learning and selfâadaptive neural networks. The proposed system adopts a proactive approach by continuously assimilating intrusion logs, utilizing a Long ShortâTerm Memory (LSTM) core to discern patterns and enhance its realâtime threat detection capabilities, removing a major bottleneck in traditional IDS models by eliminating the need for manual tagging. To further strengthen the security measures, selfâupdating neural networks are embedded in each block of the blockchain, forming a decentralized âbrainâ that evolves defences against even the most sophisticated adversaries. These networks are securely housed in Trusted Execution Environments (TEEs) to maintain operational integrity, enabling tamperâproof operation and effective threat detection. Realâworld evaluations conducted on the Binance Smart Chain and Ethereum Classic datasets demonstrate the systemâs superior performance. With an impressive accuracy rate of 98.50% and a minimal false positive rate of 1.50%, the model demonstrates a remarkable ability to distinguish legitimate network activity from malicious intrusions.
The rapid growth of IoT devices raises challenges in identity management, security, traceability, and digital forensics. Traditional centralised registration methods face security and scalability issues. This paper proposes a Spidernetbased Multi-Blockchain architecture for registering IoT devices (DNAs) to enhance digital evidence acquisition. The multilayered model improves data distribution, redundancy, and fault tolerance. The Multi-Blockchain allows parallel processing and scalability for large IoT networks. A hybrid proof-of-stake and proof-of-activity consensus mechanism boosts security and energy efficiency. The Spidernet architecture outperforms traditional blockchains in transaction speed, latency, robustness, and digital evidence management, strengthening decentralised IoT ecosystems and digital forensics.
Network slicing constitutes a paradigm shift as it transforms a 5G network into a set of versatile sub-networks for designated users, with specific requirements on security levels and quality of service (QoS) demands. Therefore, organizations with very high security and non-negotiable QoS requirements, such as the military, are leveraging the utilization of 5G network slicing for their operations. However, some network management challenges remain before constructing a resilient network with 99.999% reliability to different attacks, while providing isolation, high throughput and low latency. In this respect, we propose a reinforcement learning-based multi-agent resilience framework, which comprises centralized training using global information and decentralized decision-making by individual agents, each corresponding to an access point, to autonomously adapt network slicing configurations based on the evolving tactical landscape. The proposed framework continuously assesses network conditions and threat scenarios, to dynamically allocate the required resources and mitigate vulnerabilities. Numerical results exhibit that our proposed framework effectively defends against different adversarial actions and maintains operational continuity without compromising the QoS.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini ¡ 5 authors
In the world of cryptocurrencies, the public listing of a new token often generates significant hype. In many cases, the price of the token skyrockets in a few seconds, and timing is crucial to determine the success or failure of an investment opportunity. In this work, we present an in-depth analysis of sniper bots, automated tools designed to buy tokens as soon as they are listed on the market. We leverage GitHub open-source repositories of sniper bots to analyze their features and how they are implemented. Then, we build a dataset of Ethereum and BNB Smart Chain (BSC) liquidity pools to identify operations performed using sniper bots. Our findings reveal 352,413 sniping operations on Ethereum and 1,716,917 on BSC for a total turnaround of $155,630,184 and $137,548,859, respectively. We find that Ethereum operations have a higher success rate but require a larger investment. Finally, we analyze possible countermeasures and mechanisms used in token smart contracts that can reduce the negative impact of sniper bots.
Adrian Victor VEVERA, Andreea CÄtÄlina CRÄCIUN, Mihail Dumitrache, Ionut SANDU ¡ 6 authors
The Domain Name System (DNS) remains a foundational component of the Internet infrastructure, which is frequently exploited by cybercriminals through increasingly diverse and sophisticated attack vectors.This paper explores the evolving cybersecurity challenges faced by domain name systems (DNSs) and their decentralized counterparts in the Web3 ecosystem, particularly the Ethereum Name Service (ENS), as such, it surveys both the established and novel attack patterns.Furthermore, it explores the implications of decentralized naming systems like the ENS, which introduced novel cybersecurity challenges within the Web3 environments and it highlights the future risks and possible research directions related to the transition to decentralized web services.This study provides a comparative analysis of the cyberattacks targeting the DNS and the ENS, highlighting the evolving threat landscape for each of the two ecosystems.By examining the architectural differences between the DNS and ENS, their common attack vectors, and their security mechanisms, it underscores both the distinct vulnerabilities inherent to each ecosystem and the overlapping risks they share.
As digital technologies increase interconnectivity among us, the need to safeguard our network infrastructure from sophisticated cyber threats has never been more important. This chapter provides a review of the modern cybersecurity technologies that seek to protect our networks. A discussion of strategic approaches for protecting networks, tools needed, and the future of technology in cyber- protection. It has considered traditional forms of cybersecurity protection (e.g., firewalls, intrusion detection systems; IDS) along with modern AI-driven cyber threat detection and response. We discuss predecessor and subsequent paradigms of cybersecurity protection, including, but not limited to, zero trust architecture, the continued monitoring of networks as an operational method, and distributed ledger technology using blockchain; blockchain solutions like smart contracts and protocols (e.g., Hyperledger). Examining the trends in the future of cybersecurity protections, and highlight some of those that include predictive analytics and automated remediation of malware threats via Automated Threat Remediation, threat intelligence sharing, and a collaborative approach to countering threats. In summary, this chapter reinforced the importance of a low-latency, adaptive, and multi-layered defense approach to evolved cyber threats, and highlighted the need for organizations to demonstrate compliance with global standards and regulatory frameworks.
Open access
Network Security and Intrusion Detection
Advanced Research in Systems and Signal Processing
Small and medium size enterprises are becoming critical in driving innovations and economic growth in digital economy. However SME growing reliance on digital technologies exposes to cybersecurity attacks such as data breaches and phishing attacks and ransoms ware attack leads to greater financial loss, reputational challenges and business closure. In order to protect the SME business operation and their process data against cyber security attacks, many researchers applies emerging technologies such as Artificial intelligence and blockchain. Despite of many advantages of the implementing blockchain towards decentralization and transparency while artificial intelligence approaches towards predicting and classifying attacks, it is mandatory to establish an integrated solution to enhance security of the distributed servers of the SME. In this paper, blockchain integrated convolution neural network is designed to predict and classify the user with user level to secure access of data in blockchain enabled distributed servers. Initially Blockchain is established to business process data of the SME with immutable ledger for fostering trust and transparency. Convolution Neural Network establishes access control mechanism to blockchain distributed server to authenticate user against unauthorized access and predict the user level of access to data. In Blockchain, trusted nodes can validate the transaction and request for data access through generation of new transaction by user. User request is logged in blockchain which leads to data transparency and support detect the malicious user to retrieve data in the blockchain. Convolution Neural Network processes the log data of blockchain which contain user request. The user requests were processed in the convolution layer to extract the spatial temporal features. Extracted feature were embedded as spatial embedding and temporal embedding and applied to Max pooling layer. Max pooling layer reduces spatial dimension of the feature map. Spatially reduced feature map is applied to fully connected layer which contains activation function and softmax function to authenticate user and categorize the user with level of access to the data. Experimental analysis of the model is performed in the blockchain platform named as hyperledger which enables convolution neural network for authenticate user and categorize level of user towards data access. Performance analysis of the model proves that model is more secure and accurate against detecting authorized user and classifying user on their level access to data.
Cooperation among telecom carriers and datacenter providers (DCPs) is essential to ensure the resiliency of network-cloud ecosystems. To enable efficient cooperative recovery in case of traffic congestion or network failures, we introduce a novel, to our knowledge, multi-entity cooperation platform (MCP) for implementing cooperative recovery planning. The MCP is built over distributed ledger technology (DLT), which ensures decentralized and tamper-proof information exchange among stakeholders to achieve open and fair cooperation. We experimentally demonstrate a proof-of-concept DLT-based MCP on a testbed. We showcase a DCPâcarrier cooperative planning process and the corresponding recovery in the data-plane, showing the possibility of multi-entity cooperation for quick recovery of network-cloud ecosystems.
G. Ramsudhan, G. Hrudaya, Nandhakumar B.S, R B U R G U S ¡ 5 authors
The concern for security and privacy have skyrocketed as IoT devices in smart homes gain popularity. Unauthorized access, data tampering, and cyberattacks are growing threats. Existing centralized security models, which rely on a single structure, are vulnerable to such threats and hence a more advanced strategy is needed. This paper aims to discuss the Blockchain-Enabled Secure IoT Architecture (BESIA) which use Ethereum smart contracts, permits decentralized authentication, and cryptographic hashing to secure the architecture. To increase device communication, MQTT is used as well as TLS v1.2 encryption to enhance data protection. The approach provides access control using a novel Hierarchical Trust Model (HTM) where the device authentication and registration are fully secured using SHA-256 hashing and asymmetric cryptography. By performing security audits via MitM intrusions, ARP spoofing, and MQTT injection attacks, the systems demonstrated a 68% increase in attack resistance and 85 % decrease in unsolicited access tries after the blockchain was integrated. To conclude, this provides a prescription for a self-sustaining and scalable framework which utilizes real-time encryption and automatic attack intervention combined with the distributed ledger technology (DLT) to enhance smart home security in IoT ecosystems.
Software defined networking (SDN) increasingly integrates multiple controllers from diverse vendors to enhance network scalability, flexibility, and reliability. However, such heterogeneous deployments pose significant security threats, especially at the east-west interface which is connecting these controllers. Existing solutions are inadequate for ensuring robust protection across multi-vendor SDN environments as most of them are meant to a specific type of attacks, use centralized solution, or designed for homogeneous SDN environments. This study proposes a blockchain-based security framework to address existing security gaps within heterogeneous SDN environments. The framework establishes a decentralized, robust, and interoperable security layer for distributed SDN controllers. By utilizing the Ethereum blockchain with customized smart contract-based checks, the proposed approach enables mutual authentication among controllers, secures data exchange, and controls network access. The framework effectively mitigates common SDN threats such as distributed denial-of-service (DDoS), man-in-the-middle (MitM), false data injection, and unauthorized access. Experimental results highlight the practicality of the solution, achieving a stable throughput of approximately 20 transactions per second with an average authentication latency of 28-40 ms. These results demonstrate that the proposed framework not only enhances inter-controller communication security but also maintains the network performance, making it a reliable and scalable solution for real-world SDN deployments.
R. N. Kulkarni, Chetna Kaushal, Ismail Keshta, Mukesh Soni ¡ 5 authors
As a prime exemplar of the Internet of Things (IoT), the vehicle-to-vehicle network assumes a pivotal position in the realm of intelligent transportation. It provides various online services for vehicles and reduces the risk of accidents for drivers. However, during communication, the vehicle-to-vehicle network generates sensitive information, such as vehicle location and routes. Enhancing the anonymity of vehicle identities in secure services is a research interest in vehicle-to-vehicle network security, especially in Zero Trust network security. This article introduces an anonymous identity authentication scheme based on batch verification algorithms, leveraging the principles of Zero Trust security. It expands the scope of anonymous authentication methods for IEEE WAVE security services by incorporating techniques such as anonymous credentials and zero-knowledge proofs, in accordance with the tenets of the Zero Trust model. Furthermore, it offers a mechanism for identity recovery via a trusted third party, thereby establishing a holistic 186 security framework. Experimental results indicate that when the number of signatures for batch verification exceeds 11, the computational cost of the proposed scheme is more efficient than some comparative schemes. Based on this, the article suggests the optimal cycle for batch verification in the DSRCâs BSM and vehicle proximity payment applications while maintaining a zero-trust security posture.
With the development of blockchain technology and the emergence of various cryptocurrencies, security issues related to blockchain and cryptocurrencies have become increasingly prevalent and have received widespread attention in recent years. To facilitate future research, this paper provides a bottom-up summary of methodologies at various levels, including data collection, data mining, anomaly detection and analysis. We aim to clarify the relationships between these levels and provide a technical guideline for researchers. Moreover, we conduct a fine-grained classification of data mining methods and anomaly detection and analysis techniques to highlight their differences and reflect the existing research gaps and future challenges.
In this work, we evaluate the state of the art of Web3 while seeking to provide developers with a clear understanding of the Web3 architecture. We seek to provide a cybersecurity-focused, practical, and up-to-date assessment of Web3, equipping developers with the knowledge necessary to utilize Web3 to enhance the security of their applications. We analyze the security advantages Web3 introduces over Web2 while critically evaluating its limitations. Through an in-depth analysis of the security of Web3 applications, this work provides practical insights to developers on creating Web3 applications. Our analysis provides a framework Web3 application that emphasizes the unique security benefits of Web3. Overall, we found that the Web3 architecture offers inherent cybersecurity benefits such as increased data control and security, identity management, and data inventory management, and reducing the risk of cyber attacks for platform providers.
Ahmad M. Almasabi, Ahmad B. Alkhodre, Maher Khemakhem, Fathy Eassa ¡ 6 authors
IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensorâs data points. Five sensors were run in a SimPy simulation environment to check and examine our frameworkâs capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.
Rami Almatarneh, Mohammad Aljaidi, Ayoub Alsarhan, Sami Aziz Alshammari ¡ 6 authors
The rapid evolution of Web 4.0, characterized by decentralized systems, real-time data processing, and AI-driven interfaces, presents serious security threats such as SQL injection (SQLi) attacks, adversarial model poisoning, and IoT device spoofing. This paper presents a unified AI-blockchain framework designed to address these vulnerabilities, incorporating bidirectional LSTM networks for SQLi detection, Trimmed Mean aggregation with a reputation system for model poisoning defense, and CNN-based IoT authentication anchored to a decentralized blockchain. Evaluated on the Bitcoin OTC trust network, the framework clearly shows outstanding performance, with SQLi detection achieving 96.2% accuracy (94.8% precision and 92.5% recall), far outperforming traditional rule-based systems such as Snort (82.1% accuracy). The success rate of model poisoning attacks is reduced from 78% (in the absence of defense) to just 12% through the application of Trimmed Mean aggregation and dynamic reputation scoring, while IoT spoofing detection attains a 91.3% F1-score through cosine similarity-based matching of network traffic embeddings. The blockchain layer, which uses Delegated Proof-of-Stake (DPoS) consensus, achieves 1,450 transactions per second (TPS) with a validation latency of only 220 milliseconds, ensuring efficient real-time auditability. Furthermore, user trust scores increased by 48% after implementation (4.3/5 vs. 2.9/5 before implementation), confirming the framework's practical impact. Nevertheless, some limitations still persist, such as the 15% latency overhead due to federated learning and the use of synthetic IoT data, which may limit or reduce the framework's real-world applicability. The proposed combination of AI-based adaptive threat detection and blockchain-based tamper-proof transparency will pave the way for secure, user-focused architectures in Web 4.0, providing a scalable framework to address the evolving cyber threats in decentralized environments.
Voting-Based Distributed Ledger Technologies proved efficiency and security in protecting IoT networks. They allow faster approval time, identify malicious information, and isolate adversaries through repetitive queries to adjacent peers asking their opinions about the validity of each transaction. They enable a decentralized scheme that securely constructs and stores all data types, implying either monetary values, system logs, analytical information, or triggered actions. Several consensus algorithms were introduced to enrich distributed IoT networks with data integrity, transparency, resilience, and trust. This work surveys the main challenges for deploying distributed ledgers in IoT environments. It also introduces an adaptive DLT-based framework that standardizes the fundamental required modules for securing communication among heterogeneous IoT devices. The framework comprises four integrated modules that work simultaneously to ensure data integrity and network security. Practical simulation is also performed to evaluate the effectiveness of this framework through a parameterized model. The experimental results conclude that integrating the four functional modules is essential for network efficiency, reliability, and resilience.
Autonomous Vehicles (AV) proliferation brings important and pressing security and reliability issues that must be dealt with to guarantee public safety and help their widespread adoption. The contribution of the proposed research is towards achieving more secure, reliable, and trustworthy autonomous transportation system by providing more capabilities for anomaly detection, data provenance, and real-time response in safety critical AV deployments. In this research, we develop a new framework that combines the power of Artificial Intelligence (AI) for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity including sensor failures in AVs. Through Long Short-Term Memory (LSTM) networks, our approach continually monitors associated multi-sensor data streams to detect anomalous patterns that may represent cyberattacks as well as hardware malfunctions. Further, this framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity, while offering transparent forensic features. Moreover, immediate automated response mechanisms are deployed using smart contracts when anomalies are found. This makes the AV system more resilient to attacks from both cyberspace and hardware component failure. Besides, we identify potential challenges of scalability in handling high frequency sensor data, computational constraint in resource constrained environment, and of distributed data storage in terms of privacy.
bstract In order to give users instant access to market data, portfolio management features, and analytical tools, this research paper introduces CryptoTracker, a feature-rich real-time cryptocurrency tracking application. The system makes use of contemporary web technologies to provide a responsive interface that is constantly updated to reflect the state of the market. In addition to outlining the system's functionality, architecture, and implementation specifics, we also go into the difficulties in creating trustworthy cryptocurrency tracking tools in a volatile market. Keywords: Cryptocurrency, Real-time tracking, Portfolio management, Web technologies, Financial analysis, Data visualization