In smart contract development, practitioners frequently reuse code to reduce development effort and avoid reinventing the wheel. This reused code, whether identical or similar to its original source, is referred to as a code clone. Unintentional code cloning can propagate flaws and vulnerabilities, potentially undermining the reliability and maintainability of software systems. Previous studies have identified a significant prevalence of code clones in Solidity smart contracts on the Ethereum blockchain. To mitigate the risks posed by code clones, clone detection has emerged as an active field of research and practice in software engineering. Recent studies have extended existing techniques or proposed novel techniques tailored to the unique syntactic and semantic features of Solidity. Nonetheless, the evaluations of existing techniques, whether conducted by their original authors or independent researchers, involve codebases in various programming languages and utilize different versions of the corresponding tools. The resulting inconsistency makes direct comparisons of the evaluation results impractical, and hinders the ability to derive meaningful conclusions across the evaluations. There remains a lack of clarity regarding the effectiveness of these techniques in detecting smart contract clones, and whether it is feasible to combine different techniques to achieve scalable yet accurate detection of code clones in smart contracts. To address this gap, we conduct a comprehensive empirical study that evaluates the effectiveness and scalability of five representative clone detection techniques on 33,073 verified Solidity smart contracts, along with a benchmark we curate, in which we manually label 72,010 pairs of Solidity smart contracts with clone tags. Moreover, we explore the potential of combining different techniques to achieve optimal performance of code clone detection for smart contracts, and propose SourceREClone, a framework designed for the refined integration of different techniques, which achieves a 36.9% improvement in F1 score compared to a straightforward combination of the state of the art. Based on our findings, we discuss implications, provide recommendations for practitioners, and outline directions for future research.
Ethereum smart contracts determine state transition results not only by the previous states, but also by a mutable global state consisting of storage variables. This has resulted in state-inconsistency bugs, which grant an attacker the ability to modify contract states either through recursive function calls to a contract (reentrancy), or by exploiting transaction order dependence (TOD). Current studies have determined that identifying data races on global storage variables can capture all state-inconsistency bugs. Nevertheless, eliminating false positives poses a significant challenge, given the extensive number of execution paths that could potentially cause a data race. For simplicity, existing research considers a data race to be vulnerable as long as the variable involved could have inconsistent values under different execution orders . However, such a data race could be benign when the inconsistent value does not affect any critical computation or decision-making process in the program. Besides, the data race could also be infeasible when there is no valid state in the contract that allows the execution of both orders. In this paper, we aim to appreciably reduce these false positives without introducing false negatives. We present DivertScan , a precise framework to detect exploitable state-inconsistency bugs in smart contracts. We first introduce the use of flow divergence to check where the involved variable may flow to. This allows DivertScan to precisely infer the potential effects of a data race and determine whether it can be exploited for inducing unexpected program behaviors. We also propose multiplex symbolic execution to examine different execution orders in one time of solving. This helps DivertScan to determine whether a common starting state could potentially exist. To address the scalability issue in symbolic execution, DivertScan utilizes an overapproximated pre-checking and a selective exploration strategy. As a result, it only needs to explore a limited state space. DivertScan significantly outperformed state-of-the-art tools by improving the precision rate by 20.72% to 74.93% while introducing no false negatives. It also identified five exploitable real-world vulnerabilities that other tools missed. The detected vulnerabilities could potentially lead to a loss of up to $68.2M, based on trading records and rate limits.
Ruichao Liang, Jing Chen, Ruochen Cao, Kun He ¡ 8 authors
Smart contracts, as Turing-complete programs managing billions of assets in decentralized finance, are prime targets for attackers. While fuzz testing seems effective for detecting vulnerabilities in these programs, we identify several significant challenges when targeting smart contracts: (i) the stateful nature of these contracts requires stateful exploration, but current fuzzers rely on transaction sequences to manipulate contract states, making the process inefficient; (ii) contract execution is influenced by the continuously changing blockchain environment, yet current fuzzers are limited to local deployments, failing to test contracts in real-world scenarios. These challenges hinder current fuzzers from uncovering hidden vulnerabilities, i.e., those concealed in deep contract states and specific blockchain environments. In this paper, we present SmartShot, a mutable snapshot-based fuzzer to hunt hidden vulnerabilities within smart contracts. We innovatively formulate contract states and blockchain environments as directly fuzzable elements and design mutable snapshots to quickly restore and mutate these elements. SmartShot features a symbolic taint analysis-based mutation strategy along with double validation to soundly guide the state mutation. SmartShot mutates blockchain environments using contractâs historical on-chain states, providing real-world execution contexts. We propose a snapshot checkpoint mechanism to integrate mutable snapshots into SmartShotâs fuzzing loops. These innovations enable SmartShot to effectively fuzz contract states, test contracts across varied and realistic blockchain environments, and support on-chain fuzzing. Experimental results show that SmartShot is effective to detect hidden vulnerabilities with the highest code coverage and lowest false positive rate. SmartShot is 4.8Ă to 20.2Ă faster than state-of-the-art tools, identifying 2,150 vulnerable contracts out of 42,738 real-world contracts which is 2.1Ă to 13.7Ă more than other tools. SmartShot has demonstrated its real-world impact by detecting vulnerabilities that are only discoverable on-chain and uncovering 24 0-day vulnerabilities in the latest 10,000 deployed contracts.
Abhishek Bhattarai, Abdulhadi Sahin, Maryna Veksler, Ahmet Kurt ¡ 7 authors
As cryptocurrencies have become increasingly used as an alternative to regular cash and credit card payments, the wallet solutions/apps that facilitate their use have also become increasingly popular. This has also intensified the involvement of these crypto wallet apps in criminal activities such as ransom requests, money laundering, and transactions on dark markets. From a digital forensics point of view, it is crucial to have tools and reliable approaches to detect these wallets on devices and extract their artifacts quickly with greater efficiency. However, with current research and trends, forensic investigators still need to manually extract these file artifacts, which delays the time-sensitive investigation findings. As mobile devices increasingly facilitate cryptocurrency transactions, there emerges a critical gap and need for automated evidence extraction to detect crucial artifacts preventing illicit activities. Therefore, in this paper, we present a comprehensive framework that incorporates various machine learning (ML), image processing, and natural language processing (NLP) approaches to enable fast and automated extraction/triage of crypto-related artifacts from Android and iOS devices. Specifically, our method can automatically detect which crypto wallet exists on the device, their artifacts (i.e., database/log files), along with the crypto-related images, web browsing data, and SMS conversations. For each type of data, we offer a specific ML technique, such as Support Vector Machine, Logistic Regression, and Neural Networks, to detect and classify these files. Our evaluation results show very high accuracy compared to alternative tools: our wallet classification model achieves 91% recall, crypto-related image classification achieves 75% accuracy, browsing data achieves 100% accuracy, and the SMS message model achieves 85% accuracy.
Blockchain gaming platforms based on Non-Fungible Tokens (NFTs) have vastly grown and provide players with ownership to trade and monetize in-game assets. However, the integrity of NFT transactions in Proof-of-Stake (PoS) systems are prone to security threats due to the possible insertion of malicious blocks. Detection of these blocks is required to keep trust in the ecosystem. The proposed machine learning-based solution included detection of malicious blocks in PoS with focus on the performance of an LSTM Model. Following extensive testing with 3 models of LSTM, 1D CNN and GRU, the model achieved an accuracy of 86.9% making it most effective in regards to early identification of fraudulent transactions. This method augments the security of NFT gaming transactions and thus offers support against several malicious acts occurring in blockchain based ecosystems.
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.
Amid the surge of Non-Fungible Tokens (NFTs) in blockchain, this study introduces a meticulous methodology focusing on transaction behaviors to unveil rug pulls â a critical issue impacting financial security and trust in the NFT landscape. Using a Graph Isomorphism Network (GIN) model with 6 behavioral patterns obtained from transaction sequences, we create a âRug Pull Pattern Matcherâ model. We provide a comprehensive analysis by applying the model on two datasets â creatorâs transactions from 50 reputable NFT projects and 32 reported rug pulls. Our work utilizes automated labeling to categorize addresses and our analysis reveals several interconnected NFT creator activities. We present an in-depth mapping of fund flows and creator interactions exposing suspicious behaviors like artificial inflation and intricate network collaborations among creators. The results of our proposed model demonstrate the efficacy of our methodology with 75.4% accuracy and 85.9% precision on the dataset of reported rug pulls. This work provides comparative analyses of genuine and malicious creator networks to elucidate their structural differences, helping to identify genuine and potentially fraudulent NFT activities.
Zsofia Baruwa, Sanjay Bhattacherjee, Sahil Rey Chandnani, Zhen Zhu
This work is the first study on the perceptions of social media users about cryptocurrency attacks. The double-spending or 51% attack being the most fundamental attack on cryptocurrencies, it is the focus of this study. As a first step, we create a first-of-its-kind comprehensive list of 31 events of 51% attacks on various proof-of-work cryptocurrencies, showing that these events are quite common. This list contradicts the general perception about the security of cryptocurrencies, particularly portrayed in the Executive Order establishing a Strategic Bitcoin Reserve and a Digital Asset Stockpile in the US. We design the methodologies for our new study of user perceptions around these attacks. We create datasets containing tweets from the time of the attack events, and compare them with benchmark data from normal times. We define parameters for profiling these datasets based on user perceptions â sentiments and emotions. We study the variation of these perception profiles, when a cryptocurrency is under attack and the benchmark otherwise, between multiple attack events of the same cryptocurrency, and between different cryptocurrencies. Our results confirm some expected overall behaviour and reactions while providing nuanced insights that may not be obvious or may even be considered surprising. Our code and datasets are publicly accessible.
As cyber-crimes have become more complex network forensics has become an essential element of cybersecurity investigations. However, conventional forensic techniques are confronted with challenges such as data privacy, integrity, and secure authentication of evidence. This paper suggests a privacy-preserving AI-augmented forensic framework that uses Zero-Knowledge Proofs (ZKP) for authenticating forensics securely and blockchain for tamper-evident forensic storage. The intended framework employs AI and ML strategies for real-time intrusion detection real-time intrusion detection, anomaly recognition, and cyber-attack attribution, radically enhancing forensic efficacy and investigative productivity. Experimental evidence obtained with the UNSW-NB15 dataset provides evidence that the AI model offers a detection rate of 97.5% accompanied by precision as high as 96.8% and a recall of as much as 98.2% to ensure good cyber threat classification. Moreover, the verification process of ZKP takes only 1.2 milliseconds, allowing for fast forensic validation with data confidentiality being maintained. The blockchain-based logging system for forensics has an overhead of merely 0.35 MB per transaction, allowing tamper-proof and scalable storage of forensic data. The findings confirm that integrating AI, ZKP, and blockchain improves forensic trustworthiness at the cost of reduced data exposure. This work adds to developing privacy-protecting forensic techniques and offers a secure, scalable solution for contemporary cybercrime investigations.
Morena Barboni, Guglielmo De Angelis, Andrea Morichetta, Andrea Polini
Blockchain technology is increasingly adopted in scenarios requiring trust and data integrity. On the Ethereum blockchain, the proxy pattern has become increasingly popular because it allows smart contract code to evolve while preserving stored data. However, a key challenge remains ensuring that such upgrades do not introduce breaking changes or cause disruptions to other contracts and off-chain systems. In this article, we introduce Catana , a framework that leverages historical transactions for Capture-Replay testing of proxy-based Upgradeable Smart Contracts (USCs). Catana assesses the potential impact of an upgrade by comparing the outcomes of replayed transactions with those from the previous version deployed on the main network. Additionally, it extracts and decodes contract state variables, providing deeper insights into how code changes affect the contract state, and helping developers mitigate issues before deployment. Experiments demonstrate that analyzing storage data accounts for the majority (about 86.5%) of detected disruptive upgrades. We also evaluate different policies for building replay test suites from historical transactions. Results identify a strategy that maximizes effectiveness while requiring a small number of replay test executions. Even a test suite containing just one transaction per each invoked method achieved good effectiveness (about 60%) in detecting disruptive upgrades.
Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang ¡ 6 authors
Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under the guise of uncertainty. We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage, which deliberately reduces confidence in targeted input regions, thereby covertly disadvantaging specific individuals. At the same time, Mirage maintains high predictive performance across all data points. To counter this threat, we propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence. Additionally, it employs zero-knowledge proofs of verified inference to ensure that reported confidence scores genuinely originate from the deployed model. This prevents the provider from fabricating arbitrary model confidence values while protecting the model's proprietary details. Our results confirm that Confidential Guardian effectively prevents the misuse of cautious predictions, providing verifiable assurances that abstention reflects genuine model uncertainty rather than malicious intent.
Jadyn Kimber, Enrico Branca, Andrei Natadze, Natalia Stakhanova
The increasing number of Ethereum scams is causing significant concern within the blockchain community, costing users millions of dollars annually. Yet, our understanding of how these scams operate remains limited. In this study, we present the first end-to-end analysis of crypto scams using a large set of malicious Ethereum accounts as a case study. We examine the tactics these scams employ on social media platforms to deceive users and convince them to transfer funds to malicious accounts. Our analysis explores the full life cycle of these scams, considering both their distribution through social media and their activity on the Ethereum blockchain. We identify several unique aspects of Ethereum phishing scams that have not been documented in prior literature and find that these scams generally persist significantly longer and result in greater financial losses compared to traditional phishing scams studied in earlier research.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
Modern blockchain applications are often constrained by a trade-off between user experience and trust. Chainless Apps present a new paradigm of application architecture that separates execution, trust, bridging, and settlement into distinct compostable layers. This enables app-specific sequencing, verifiable off-chain computation, chain-agnostic asset and message routing via Agglayer, and finality on Ethereum - resulting in fast Web2-like UX with Web3-grade verifiability. Although consensus mechanisms have historically underpinned verifiable computation, the advent of zkVMs and decentralized validation services opens up new trust models for developers. Chainless Apps leverage this evolution to offer modular, scalable applications that maintain interoperability with the broader blockchain ecosystem while allowing domain-specific trade-offs.
Francesco Salzano, Cosmo Kevin Antenucci, Simone Scalabrino, Giovanni Rosa ¡ 6 authors
Abstract The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances, manually labeled at the line level with vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the efficacy of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78% found vulnerabilities, taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and by conducting the largest empirical evaluation of tools to date.
The chapter delves into the digital forensics of cryptocurrencies, focusing on the role of blockchain, computers, and mobile phones in gathering evidence. It underscores the pressing need for immediate and extensive research in digital cryptocurrency forensics, especially for significant cryptocurrencies increasingly attracting legitimate and malicious users. The chapter also highlights a critical gap in research on host-based cryptocurrency forensics, particularly mobile-based forensics. Most studies on host-based forensics concentrate on outdated operating systems or platforms, highlighting the necessity for more current versions, and the urgency of updating your methods in host-based forensics is apparent. Cryptocurrency forensics primarily involves the analysis of publicly accessible blockchains using clustering heuristics and machine learning-based analysis to identify anonymous entities or provide investigation guidance. Security and vulnerability assessment studies are crucial in examining forensic methods for cryptocurrencies, offering insights into potential exploits.
Large Language Models (LLMs) have shown great promise in code analysis and auditing; however, they still struggle with hallucinations and limited context-aware reasoning. We introduce SmartAuditFlow, a novel Plan-Execute framework that enhances smart contract security analysis through dynamic audit planning and structured execution. Unlike conventional LLM-based auditing approaches that follow fixed workflows and predefined steps, SmartAuditFlow dynamically generates and refines audit plans based on the unique characteristics of each smart contract. It continuously adjusts its auditing strategy in response to intermediate LLM outputs and newly detected vulnerabilities, ensuring a more adaptive and precise security assessment. The framework then executes these plans step by step, applying a structured reasoning process to enhance vulnerability detection accuracy while minimizing hallucinations and false positives. To further improve audit precision, SmartAuditFlow integrates iterative prompt optimization and external knowledge sources, such as static analysis tools and Retrieval-Augmented Generation (RAG). This ensures audit decisions are contextually informed and backed by real-world security knowledge, producing comprehensive security reports. Extensive evaluations across multiple benchmarks demonstrate that SmartAuditFlow outperforms existing methods, achieving 100 percent accuracy on common and critical vulnerabilities, 41.2 percent accuracy for comprehensive coverage of known smart contract weaknesses in real-world projects, and successfully identifying all 13 tested CVEs. These results highlight SmartAuditFlow's scalability, cost-effectiveness, and superior adaptability over traditional static analysis tools and contemporary LLM-based approaches, establishing it as a robust solution for automated smart contract auditing.
S. M. Dilip Kumar, Namrta Tanwar, Namrta Tanwar, Aakarsh Chandna ¡ 5 authors
The blockchain technology has disrupted the earlyage digital banking through concepts like bitcoin and ether [1,3].In this study, some major elements of the blockchain technology are examined-decentralized networks, smart contracts, cryptographic techniques, and consensus mechanisms of Proof of Work and Proof of Stake usage-and understanding how they contribute to safe, peer-to-peer transactions without intermediaries [2,5].Bitcoin can do no more than about seven transactions a second (TPS) is a very paltry competition of an impressive 30 to 40 TPS of Ethereum.This depicts the ongoing scalability challenges that need to be tackled by initiatives linked with Ethereum 2.0 and the Lightning Network [4,9].While most industries, apart from banking, have effectively made their blockchain applications and transparency useful-Supply Chain Management, Healthcare, and DeFi-currently poses challenges of transaction speed limitations, the vagueness of regulations, and energy consumption by mining [8].Emerging trends include Non-Fungible Tokens (NFTs), Central Bank Digital Currencies (CBDCs), and privacy enhanced through zero-knowledge proofs.There is hope for excellent feedback on the future of the blockchain from these and other initiatives yet to come into reality.
Sheng Zhang, Tan Kia Quang, Shen Wang, Shengchen Duan ¡ 6 authors
Scam contracts on Ethereum have rapidly evolved alongside the rise of DeFi and NFT ecosystems, utilizing increasingly complex code obfuscation techniques to avoid early detection. This paper systematically investigates how obfuscation amplifies the financial risks of fraudulent contracts and undermines existing auditing tools. We propose a transfer-centric obfuscation taxonomy, distilling seven key features, and introduce ObfProbe, a framework that performs bytecode-level smart contract analysis to uncover obfuscation techniques and quantify obfuscation complexity via Z-score ranking. In a large-scale study of 1.03 million Ethereum contracts, we isolate over 3 000 highly obfuscated contracts and identify two scam archetypes, three high-risk contract categories, and MEV bots that employ a variety of obfuscation maneuvers such as inline assembly, dead code insertion, and deep function splitting. We further show that obfuscation substantially increases both the scale of financial damage and the time until detection. Finally, we evaluate SourceP, a state-of-the-art Ponzi detection tool, on obfuscated versus non-obfuscated samples and observe its accuracy drop from approximately 80 percent to approximately 12 percent in real-world scenarios. These findings highlight the urgent need for enhanced anti-obfuscation analysis techniques and broader community collaboration to stem the proliferation of scam contracts in the expanding DeFi ecosystem.
Marco A. C. da Silva, Luis Hideo Vasconcelos Nakamura, Geraldo P. Rocha Filho, LuĂs Veiga ¡ 5 authors
With the advancement of technologies for data registration in distributed networks, the concern of users and developers of computerized solutions with the privacy of sensitive data has increased. Thus, this work addresses a conceptual solution for an ontology-based framework so that any entity willing to provide a service using Distributed Ledger Technology (DLT) networks can model the set of privacy attributes of its system according to the business rules of its service. The solution proposed in this work encompasses the development of an architecture aimed at providing computational support for the privacy design of the actors involved in the offering and consumption of services implemented in DLTs. The architecture also includes a framework called ONSPRIDE, which uses previously stored domain ontologies to translate business rules into requirements and privacy. We conducted a proof of context by comparing the performance of two Hyperledger Fabric networks. For this purpose, we conducted a controlled experiment in which both networks operate a smart contract that manages attendance records for outdoor events. The main difference between the networks is that one uses a Certificate Authority (CA) to issue access certificates, while the other issues certificates manually. We compared the results obtained through the reports generated by the Hyperledger Caliper tool. In addition, the performance of the initialization and connection of agents in a Self-Sovereign Identity system was measured. The results of this study provide valuable insight that can help developers choose the most suitable ledger type for their Hyperledger projects and support decision-making regarding adopting a Self-Sovereign Identity system.
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.
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.
In response to the Cybersecurity Law, organizations face numerous management and technical requirements. Detection techniques such as vulnerability scanning and penetration testing are employed to identify risks. Addressing these vulnerabilities demands substantial manpower, time, and financial resources. Security concerns also arise during digital file transmission and remediation efforts. This study proposes a security detection platform with step-by-step implementation guidelines, enabling resource-limited units to replicate the setup and address security gaps. It compares detection results between open-source and commercial tools, highlighting key differences and offering remediation strategies. Numerous digital files (e.g., test reports) are generated during testing. To ensure secure storage and sharing, the system integrates IOTAâs distributed ledger and IPFS, generating HASH values and uploading files on-chain to preserve integrity and authenticity. The objective is to deliver a scalable, cost-effective security detection framework that enhances system resilience while minimizing resource consumption.