Ayodeji Adeniran, Mohammed Alkinoon, Aziz Mohaisen
We present our results on analyzing and understanding the behavior and security of various metaverse platforms incorporating cryptocurrencies. We obtained the top metaverse coins with a capitalization of at least 25 million US dollars and the top metaverse domains for the coins, and augmented our data with name registration information (via whois), including the hosting DNS IP addresses, registrant location, registrar URL, DNS service provider, expiry date and check each metaverse website for information on fiat currency for cryptocurrency. The result from virustotal.com includes the communication files, passive DNS, referrer files, and malicious detections for each metaverse domain. Among other insights, we discovered various incidents of malicious detection associated with metaverse websites. Our analysis highlights indicators of (in)security, in the correlation sense, with the files and other attributes that are potentially responsible for the malicious activities.
Yuheng Zhang, Liu Pin, Guojun Wang, Peiqiang Li · 8 authors
With the evolution of blockchain technology, the issue of transaction security, particularly on platforms like Ethereum, has become increasingly critical. Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions. In these attack scenarios, malicious actors monitor other users' transaction activities, then strategically submit their own transactions with higher fees. This ensures their transactions are executed before the monitored transactions are included in the block. The primary objective of this paper is to delve into a comprehensive classification of transactions associated with front-running attacks, which aims to equip developers with specific strategies to counter each type of attack. To achieve this, we introduce a novel detection method named FRAD (Front-Running Attacks Detection on Ethereum using Ternary Classification Model). This method is specifically tailored for transactions within decentralized applications (DApps) on Ethereum, enabling accurate classification of front-running attacks involving transaction displacement, insertion, and suppression. Our experimental validation reveals that the Multilayer Perceptron (MLP) classifier offers the best performance in detecting front-running attacks, achieving an impressive accuracy rate of 84.59% and F1-score of 84.60%.
Leandro Cunha, Miguel A. Brito, Domingos F. Oliveira, Ana Paula Martins
The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.
Advances in blockchain technology have attracted significant attention across the world. The practical blockchain applications emerging in various domains, ranging from finance, healthcare, and entertainment, have quickly become attractive targets for adversaries. The novelty of the technology coupled with the high degree of anonymity it provides made malicious activities even less visible in the blockchain environment. This made their robust detection challenging. This article presents EtherShield, a novel approach for identifying malicious activity on the Ethereum blockchain. By combining temporal transaction information and contract code characteristics, EtherShield can detect various types of threats and provide insight into the behavior of contracts. The time-interval-based analysis used by EtherShield enables expedited detection, achieving comparable accuracy to other approaches with significantly less data. Our validation analysis, which involved over 15,000 Ethereum accounts, demonstrated that EtherShield can significantly expedite the detection of malicious activity while maintaining high accuracy levels (86.52% accuracy with 1 hour of transaction history data and 91.33% accuracy with 1 year of transaction history data).
Sepideh HajiHosseinKhani, Arash Habibi Lashkari, Ali Mizani Oskui
Smart Contracts (SCs) are crucial in maintaining trust within blockchain networks. However, existing methods for analyzing SC vulnerabilities often lack accuracy and effectiveness, while approaches based on Deep Neural Networks (DNN) struggle with detecting complex vulnerabilities due to limited data availability. This paper proposes a novel approach to analyze Smart Contracts (SCs) vulnerabilities. Our method leverages an advanced form of Genetic Algorithm (GA) and includes the development of a comprehensive benchmark dataset consisting of 36,670 Solidity source code samples. The primary objective of our study is to profile vulnerable SCs effectively. To achieve this goal, we have devised an analyzer called SCsVulLyzer based on Genetic Algorithms, designed explicitly for profiling SCs. Additionally, we have carefully curated a new dataset encompassing a wide range of examples, ensuring the practical validation of our approach. Furthermore, we have established three distinct taxonomies that cover SCs, profiling techniques, and feature extraction. These taxonomies provide a systematic classification and analysis of information, improving the efficiency of our approach. Our methodology underwent rigorous testing through experimentation, and the results demonstrated the superior capabilities of our model in detecting vulnerabilities. Compared to traditional and DNN-based approaches, our approach achieved higher precision, recall, and F1-score, widely used metrics for evaluating model performance. Across all these metrics, our model showcased exceptional results. The customization and adaptations we implemented within the Genetic Algorithm significantly enhanced its effectiveness. Our approach detects smart contract vulnerabilities more efficiently and facilitates robust exploration. These promising results highlight the potential of GA-based profiling to improve the detection of smart contract vulnerabilities, contributing to enhanced security in blockchain networks.
In the rapidly evolving landscape of decentralized systems, ensuring the integrity and trustworthiness of smart contracts is paramount for developers. This paper presents a comprehensive strategy for enhancing smart contract security by focusing on specific high-risk areas, including Integer Overflow, Dangerous Delegate Calls, Timestamp Dependency, Reentrancy Vulnerabilities, Race Conditions, and Sybil attacks. Despite the growing significance of smart contracts in blockchain ecosystems, a notable research gap exists in the development of specialized tools capable of providing real-time vulnerability detection and mitigation guidance. To bridge this gap, our research introduces the ‘GRIFFIN’ - Smart Contracts. Vulnerability Detector is a powerful tool that has been rigorously tested and validated. Our study has yielded significant results, demonstrating the efficacy of the GRIFFIN in proactively identifying and mitigating critical vulnerabilities within a diverse dataset of 12,000 real-world solidity smart contracts. The tool leverages state-of-the-art static analysis techniques and machine learning algorithms, achieving superior accuracy rates when compared to existing solutions. This heightened accuracy not only empowers developers but also boosts the overall robustness and dependability of smart contract ecosystems. The cornerstone of our research is the development and validation of a practical, user-centric solution. By providing actionable insights, code snippets, and real-time feedback to developers, GRIFFIN equips them with the knowledge and tools needed to address vulnerabilities swiftly and effectively. This innovative approach is not merely an academic endeavor but a significant stride towards cultivating resilient and dependable smart contract environments. It instills a culture of security-conscious development practices, ensuring that the smart contracts crucial to decentralized systems can operate with the highest level of trust and reliability. Index Terms— Smart Contracts; Integer overflow; DangerousDelegate call; Timestamp Dependence; Reentrancy Attack; RaceCondition; Sybil Attack; Static Analysis; Detection
Xueyan Tang, Yuying Du, Alan Lai, Ze Zhang · 5 authors
This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.
The prosperity of Ethereum attracts many users to send transactions and trade crypto assets. However, this has also given rise to a new form of transaction-based phishing scam, named TxPhish. Specifically, tempted by high profits, users are tricked into visiting fake websites and signing transactions that enable scammers to steal their crypto assets. The past year has witnessed 11 large-scale TxPhish incidents causing a total loss of more than 70 million.
Computer networks and internet services are increasingly threatened by attacks like Distributed Denial-of-Service (DDoS). DDoS attack mitigation techniques now in use are ineffective due to a lack of resources and a lack of adaptability. Using blockchains like Ethereum, DDoS attacks can be thwarted in innovative ways. With smart contracts, it is possible to track down the IP addresses of attackers without additional hardware. This study examines blockchain-based solutions to combat DDoS attacks for feasibility, effectiveness, as well as cost and performance. The cost model delves into economic aspects like gas, gas price, and Ether value. In it, the evaluation of various smart contracts for the signalization of DDoS attacks is documented and compared to assess three system variants, analyzing gas costs, deployment, speed, and accuracy. It also details Ethereum's ecosystem and how that affects smart contract design and it also acknowledges scalability challenges and suggests outsourcing data for a more scalable solution, advocating for specialized blockchains for DDoS signaling applications. The analysis provides insights into the gas costs associated with different variants, considering various scenarios and highlighting the trade-offs and efficiencies of each approach.
Consumer electronics devices, such as refrigerators, washing machines, TVs, smartphones, and household appliances, have become integral to human activities. However, these devices are vulnerable to security breaches and cyber-criminal threats, which can result in the theft and misuse of sensitive information. Existing security surveys and proposed schemes have encountered limitations in terms of redundancy and effectiveness. In this paper, we present a novel approach that ensures secure and transparent communication in consumer electronics. We introduce a multi-criterion decision-making model called TOPSIS, along with a weighted product model, to enhance the security and accuracy of the system. Furthermore, our proposed scheme employs a blockchain system for continuous tracking and monitoring of devices, ensuring accountability and surveillance of their past communications. Through comprehensive validation and verification against existing approaches using various security metrics, our proposed scheme demonstrates superior performance and effectiveness.
Raghu Raman, Vinith Kumar Nair, Prema Nedungadi, Indrakshi Ray · 5 authors
The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world. This work carries out the bibliometric study on the research conducted on Darkweb over the last decade. The findings illustrate that most research on Darkweb can be clustered into four areas based on keyword co-occurrence analysis: (i) network security, malware, and cyber-attacks, (ii) cybercrime, data privacy, and cryptography, (iii) machine learning, social media, and artificial intelligence, and (iv) drug trafficking, cryptomarket. National Science Foundation from the United States is the top funder. Darkweb activities interfere with the Sustainable Development Goals (SDG) laid forth by the United Nations to promote peace and sustainability for current and future generations. SDG 16 (Peace, Justice, and Strong Institutions) has the highest number of publications and citations but has an inverse relationship with Darkweb, as the latter undermines the former. This study highlights the need for further research in bitcoin, blockchain, IoT, NLP, cryptocurrencies, phishing and cybercrime, botnets and malware, digital forensics, and electronic crime countermeasures about the Darkweb. The study further elucidates the multi-dimensional nature of the Darkweb, emphasizing the intricate relationship between technology, psychology, and geopolitics. This comprehensive understanding serves as a cornerstone for evolving effective countermeasures and calls for an interdisciplinary research approach. The study also delves into the psychological motivations driving individuals towards illegal activities on the Darkweb, highlighting the urgency for targeted interventions to promote pro-social online behavior.
Zsofia Baruwa, Sanjay Bhattacherjee, Sahil Rey Chandnani, Zhen Zhu
This work is the first study on the effects of attacks on cryptocurrencies as expressed in the sentiments and emotions of social media users. Our goals are to design the methodologies for the study including data collection, conduct volumetric and temporal analyses of the data, and profile the sentiments and emotions that emerge from the data. As a first step, we have created a first-of-its-kind comprehensive list of 31 events of 51% attacks on various PoW cryptocurrencies, showing that these events are quite common contrary to the general perception. We have gathered Twitter data on the events as well as benchmark data during normal times for comparison. We have defined parameters for profiling the datasets based on their sentiments and emotions. We have studied the variation of these sentiment and emotion 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.
This paper presents the first comprehensive analysis of an emerging cryptocurrency scam named "arbitrage bot" disseminated on online social networks. The scam revolves around Decentralized Exchanges (DEX) arbitrage and aims to lure victims into executing a so-called "bot contract" to steal funds from them. To entice victims and convince them of this scheme, we found that scammers have flocked to publish YouTube videos to demonstrate plausible profits and provide detailed instructions and links to the bot contract. To collect the scam at a large scale, we developed a fully automated scam detection system namedCryptoScamHunter, which continuously collects YouTube videos and automatically detects scams. Meanwhile,CryptoScamHunter can download the source code of the bot contract from the provided links and extract the associated scam cryptocurrency address. Through deployingCryptoScamHunter from Jun. 2022 to Jun. 2023, we have detected 10,442 arbitrage bot scam videos published from thousands of YouTube accounts. Our analysis reveals that different strategies have been utilized in spreading the scam, including crafting popular accounts, registering spam accounts, and using obfuscation tricks to hide the real scam address in the bot contracts. Moreover, from the scam videos we have collected over 800 malicious bot contracts with source code and extracted 354 scam addresses. By further expanding the scam addresses with a similar contract matching technique, we have obtained a total of 1,697 scam addresses. Through tracing the transactions of all scam addresses on the Ethereum mainnet and Binance Smart Chain, we reveal that over 25,000 victims have fallen prey to this scam, resulting in a financial loss of up to 15 million USD. Overall, our work sheds light on the dissemination tactics and censorship evasion strategies adopted in the arbitrage bot scam, as well as on the scale and impact of such a scam on online social networks and blockchain platforms, emphasizing the urgent need for effective detection and prevention mechanisms against such fraudulent activity.
The ability to create decentralized applications without the authority of a single entity has attracted numerous developers to build applications using blockchain technology. However, ensuring the correctness of such applications poses significant challenges, as it can result in financial losses or, even worse, a loss of user trust. Testing smart contracts introduces a unique set of challenges due to the additional restrictions and costs imposed by blockchain platforms during test case execution. Therefore, it remains uncertain whether testing techniques developed for traditional software can effectively be adapted to smart contracts. In this study, we propose a multi-objective test selection technique for smart contracts that aims to balance three objectives: time, coverage, and gas usage. We evaluated our approach using a comprehensive selection of real-world smart contracts and compared the results with various test selection methods employed in traditional software systems. Statistical analysis of our experiments, which utilized benchmark Solidity smart contract case studies, demonstrates that our approach significantly reduces the testing cost while still maintaining acceptable fault detection capabilities. This is in comparison to random search, mono-objective search, and the traditional re-testing method that does not employ heuristic search.
Constantinos Patsakis, Eugenia Politou, Efthimios Alepis, Julio Hernández-Castro
Abstract The fast pace of blockchain technology and cryptocurrencies’ evolution makes people vulnerable to financial fraud and provides a relatively straightforward monetisation mechanism for cybercriminals, in particular ransomware groups which exploit crypto’s pseudo-anonymity properties. At the same time, regulatory efforts for addressing crimes related to crypto assets are emerging worldwide. In this work, we shed light on the current state of practice of ransomware monetisation to provide evidence of their payment traceability, explore future trends, and—above all—showcase that over-regulating cryptocurrencies is not the best way to mitigate their risks. For that purpose, first, we provide an overview of the legislative initiatives currently taken by the USA, the EU, and the OECD to regulate cryptocurrencies, showing that strict laws and the divergences between the regulatory regimes can hardly efficiently regulate the global phenomenon of cryptocurrency, which transcends borders and states. Next, we focus on illicit payments in bitcoin to ransomware groups, illustrating how these payments are siphoned off and how criminals cash out the ransom, often leaving traceable evidence behind. To this end, we leverage a publicly available dataset and a set of state-of-the-art blockchain analysis tools to identify payment patterns, trends, and transaction trails, which are provided in an anonymised form. Our work reveals that a significant amount of illicit bitcoin transactions can be easily traced, and consequently, many cyber crimes like ransomware can actually be tracked down and investigated with existing tools and laws, thus providing fertile ground for better and fairer legislation on crypto.
Stefan Driessen, Dario Di Nucci, Damian A. Tamburri, W.-J. van den Heuvel
Smart contracts have rapidly gained popularity as self-contained pieces of code, especially those run on the Ethereum blockchain. On the one hand, smart contracts are immutable, have transparent workings, and execute autonomously. On the other hand, these qualities make it essential to properly test the behavior of a smart contract before deploying it. In this paper, we introduce SolAR, a tool and approach for Solidity Automated Test Suite GeneRation. SolAR allows smart contract developers to generate test suites for Solidity smart contracts optimized automatically for branch coverage using either a state-of-the-art genetic algorithm or a fuzzing approach. It enables a novel way to handle blockchain operations—or ChainOps—from a pipeline perspective, entailing a larger-scale as well as more manageable and maintainable service continuity.
<p>The <a href="https://sites.google.com/99cryptowallet.com/ledgerwalletextension/"><strong>Ledger Wallet Extension</strong></a> is a multi-chain web browser extension developed to offer secure and straightforward access to Web3 through the Ledger hardware wallet, letting you connect with dApps (decentralized applications) directly and simply without importing your Ledger account into the software wallet. Read the guide to understand the ins and outs of setting up and utilizing the Ledger Wallet Extension.</p>
Raifa Akkaoui, Alexandru Ştefanov, Peter Pálenský, Dick Epema
The solar industry in residential areas has been witnessing an astonishing growth worldwide. At the heart of this transformation, affecting the edge of the electricity grid, reside smart inverters (SIs). These IoT-enabled devices aim to introduce a certain degree of intelligence to conventional inverters by integrating various grid support capabilities (e.g., voltage and frequency control). However, with the remarkable automation of these devices come enormous security risks. Thus, rising rates of vulnerabilities have increased the necessity for designing resilient, auditable, and secure SIs’ firmware over the air (FOTA) amendment schemes suitable for this heterogeneous SIs-based ecosystem. In this regard, we propose leveraging blockchain as an innovative technology to guarantee these cybersecurity requirements. In this article, we present the design of a distributed FOTA scheme, namely, RASSIFAB, governing the process of amending SIs’ firmware within residential areas in an immutable and scalable manner. The scheme was implemented on a blockchain test network to assess its functionalities and performance. We also carried out a security evaluation to determine whether RASSIFAB is resistant to various identified threats. The obtained results confirm that the scheme is efficient and sound. They also indicate that RASSIFAB ensures reliable and authentic firmware amendments even with malicious insiders, differentiating our framework from the existing ones.
With the widespread adoption of blockchain platforms across various decentralized applications, the smart contract’s vulnerabilities are continuously growing and evolving. Consequently, a failure to optimize conventional vulnerability analysis methods results in unforeseen effects caused by overlooked classes of vulnerabilities. Current methods have difficulty dealing with multifaceted intrusions, which calls for more robust approaches. Therefore, overdependence on environment-defined parameters in the contract execution logic binds the contract to the manipulation of such parameters and is perceived as a security vulnerability. Several vulnerability analysis tools have been identified as insufficient to effectively identify certain types of vulnerability. In this paper, we perform a domain-specific evaluation of state-of-the-art vulnerability detection tools on smart contracts. A domain can be defined as a particular area of knowledge, expertise, or industry. We use a perspective specific to the area of energy contracts to draw logical and language-dependent features to advance the structural and procedural comprehension of these contracts. The goal is to reach a greater degree of abstraction and navigate the complexities of decentralized applications by determining their domains. In particular, we analyze code embedding of energy smart contracts and characterize their vulnerabilities in transactive energy systems. We conclude that energy contracts can be affected by a relatively large number of defects. It also appears that the detection accuracy of the tools varies depending on the domain. This suggests that security flaws may be domain-specific. As a result, in some domains, many vulnerabilities can be overlooked by existing analytical tools. Additionally, the overall impact of a specific vulnerability can differ significantly between domains, making its mitigation a priority subject to business logic. As a result, more effort should be directed towards the reliable and accurate detection of existing and new types of vulnerability from a domain-specific point of view.
IOTA is a new public chain system specifically designed for the Internet of Things (IoT), which provides strong support for the high concurrency, scalability, and zero handling fees of the IoT. The distributed ledger of IOTA, called the tangle, adopts a Directed Acyclic Graph (DAG) structure. However, compared to the single-chain architecture, the tangle is more complex and highly vulnerable to security threats. The existing transaction verification methods still cannot simultaneously meet the need for accelerating approval speed and improving security to resist illegal transactions, such as lazy tips and permanent tips. In this work, we propose TDTS, a tip-selection algorithm based on time division to improve the efficiency of transaction verification. The main idea of the algorithm is to quickly determine two tips of an incoming transaction that need to be confirmed by sorting tip values in a time slot. It shortens the transaction verification time and reduces the number of lazy tips and permanent tips. A comprehensive theoretical analysis confirmed the effectiveness of our proposed algorithm. Based on 1000 IOTA nodes, the evaluations showed that TDTS can select tips quickly like URTS and resist lazy tips like MCMC.
Long He, Xiangfu Zhao, Yichen Wang, Jiahui Yang · 5 authors
Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others.
The Internet of Things (IoT) is the most extensively utilized technology nowadays that is simple and has the advantage of replacing the data with other devices by employing cloud or wireless networks. However, cyber-threats and cyber-attacks significantly affect smart applications on these IoT platforms. The effects of these intrusions lead to economic and physical damage. The conventional IoT security approaches are unable to handle the current security problems since the threats and attacks are continuously evolving. In this background, employing Artificial Intelligence (AI) knowledge, particularly Machine Learning (ML) and Deep Learning (DL) solutions, remains the key to delivering a dynamically improved and modern security system for next-generation IoT systems. Therefore, the current manuscript designs the Honey Badger Algorithm with an Optimal Hybrid Deep Belief Network (HBA-OHDBN) technique for cyberattack detection in a blockchain (BC)-assisted IoT environment. The purpose of the proposed HBA-OHDBN algorithm lies in its accurate recognition and classification of cyberattacks in the BC-assisted IoT platform. In the proposed HBA-OHDBN technique, feature selection using the HBA is implemented to choose an optimal set of features. For intrusion detection, the HBA-OHDBN technique applies the HDBN model. In order to adjust the hyperparameter values of the HDBN model, the Dung Beetle Optimization (DBO) algorithm is utilized. Moreover, BC technology is also applied to improve network security. The performance of the HBA-OHDBN algorithm was validated using the benchmark NSLKDD dataset. The extensive results indicate that the HBA-OHDBN model outperforms recent models, with a maximum accuracy of 99.21%.
The rapid expansion of the Internet of Things (IoT) on a global scale has facilitated the convergence of revolutionary technologies such as artificial intelligence (AI), blockchain, and cloud computing. The integration of these technologies has paved the way for the development of intricate infrastructures, such as smart homes, smart cities, and smart industries, that are capable of delivering advanced solutions and enhancing human living standards. Nevertheless, IoT devices, while providing effective connectivity and convenience, often rely on traditional network interfaces that can be vulnerable to exploitation by adversaries. If not properly secured and updated, these legacy communication protocols and interfaces can expose potential vulnerabilities that attackers may exploit to gain unauthorized access, disrupt operations, or compromise sensitive data. To overcome the security challenges associated with smart home systems, we have devised a robust framework that leverages the capabilities of both AI and blockchain technology. The proposed framework employs a standard dataset for smart home systems, from which we first eliminated the anomalies using an isolation forest (IF) algorithm using random partitioning, path length, anomaly score calculation, and thresholding stages. Next, the dataset is utilized for training classification algorithms, such as K-nearest neighbors (KNN), support vector machine (SVM), linear discriminate analysis (LDA), and quadratic discriminant analysis (QDA) to classify the attack and non-attack data of the smart home system. Further, an interplanetary file system (IPFS) is utilized to store classified data (non-attack data) from classification algorithms to confront data-manipulation attacks. The IPFS acts as an onsite storage system, securely storing non-attack data, and its computed hash is forwarded to the blockchain’s immutable ledger. We evaluated the proposed framework with different performance parameters. These include training accuracy (99.53%) by the KNN classification algorithm and 99.27% by IF for anomaly detection. Further, we used the validation curve, lift curve, execution cost of blockchain transactions, and scalability (86.23%) to showcase the effectiveness of the proposed framework.