Tokens have proliferated across blockchains in terms of number, market capitalisation and utility. Some tokens are tokenised versions of existing tokens -- known variously as wrapped tokens, fractional tokens, or shares. The repeated application of this process creates matryoshkian tokens of arbitrary depth. We perform an empirical analysis of token composition on the Ethereum blockchain. We introduce a graph that represents the tokenisation of tokens by other tokens, and we show that the graph contains non-trivial topological structure. We relate properties of the graph, e.g., connected components and cyclic structure, to the tokenisation process. For example, we identify the longest directed path and its corresponding sequence of tokens, and we visualise the connected components relating to a stablecoin and an NFT protocol. Our goal is to explore and visualise what has been wrought with tokens, rather than add yet another brick to the edifice.
The Internet of Things (IoT) has been deployed in a vast range of applications with exponential increases in data size and complexity. Existing forensic techniques are not effective for the accuracy and detection rate of security issues in IoT forensics. Cyber forensic comprises huge volume constraints that are processing huge volumes of data in the Information and Communication Technology (ICT) comprised of IoT devices and platforms. Trust blockchain is effective technology those are utilized to assess the tamper-proof records in all transaction in the IoT environment. With the implementation of trust blockchain the record and transaction are processed with a distributed ledger that is managed by the network nodes. The challenge associated with the trust blockchain in IoT forensics is cost and security. To achieve significant cost-effectiveness organizations, need to evaluate the risks and benefits associated with IoT forensics in the trust blockchain technology. In this paper, developed a Block Chain Enabled Cyber-Physical system with distributed storage. The developed Blockchain model is termed as Integrated Hadoop Blockchain Forensic Machin Learning (IHBF-ML). The IHBF-ML model uses the Hadoop Distributed File System (HDFS) with cyberspace to improve security. Within the IHBF-ML model, IoT data communication is established with the smart contract. The smart contract-based blockchain process uses the Machine Learning model integrated with Cat Boost classification model for anomaly detection. Cost in IoT forensic is minimized with the parallel processing of the data through MapReduce Framework for the traffic translation, extraction, and analysis of the dynamic feature traffic from the IoT environment. The experimental analysis stated that constructed IHBF-ML model reduces the cost by ~25% than the other conventional blockchain Ethereum and EOS.
The domain of Digital Forensics for the Industrial Internet of Things (IIoT) and the proposed use of a Distributed Digital Ledger (DDL), has for the most part been theoretical in nature within the current literature. The work in this paper explores the practical feasibility of using DDL technology for Digital Forensics in the IIOT context. We detail a new methodology for testing the performance of writing to and reading from a DDL in an IIOT environment, and present findings on the overhead associated with storing and retrieving IIoT transactions in a DDL. We conclude that while it is possible to build and use a DDL for storing IIoT transactions, there are limitations to the number of sensors that can be supported by a single implementation and the time it takes to retrieve transactions may be too high to be practical for Digital Forensics.
The use of log files in digital forensics highlights the importance of ensuring their data integrity for auditing purposes. However, traditional centralized audit log systems face challenges in maintaining data integrity due to log injection attacks and single-point failures. Although blockchain technology can accurately process and replicate log files, existing blockchain-based audit log systems still suffer from security and reliability issues due to their weak threat models and limited scalability. To address these concerns, we propose a blockchain-based audit log system that ensures data integrity under a general threat model where a part of the nodes, including loggers and auditors, are untrusted. First, our proposed system resists collusion attacks by incorporating multiple nodes for system processes and utilizing smart contracts to enforce consensus algorithms. Second, to save blockchain storage space, we design an efficient log integrity proof method, which generates a sub-Non-Fungible Token (sub-NFT) for each log file and keeps it on the blockchain as integrity proof. The single-point failure problem is resolved by outsourcing log files to a distributed file system. To evaluate the proposed system, we implement a prototype based on Hyperledger Fabric. Experimental results show that our proof generation method can reduce storage space usage in comparison to other blockchain-based audit log systems, saving approximately 50% of space in Hyperledger Fabric. The security analysis proves that our system can ensure log file data integrity under the proposed threat model.
S Mohammed Ishaq, Arpitha K M, Himanshu Choubey, Mohammed Azeemulla · 5 authors
In an increasingly computerized universe, the secure administration & trading of crypto files and documents has become a critical concern. In this estimate seeks to label this issue beside creating decentralized implementation (App) that uses distributed larger technology and deep neural network models to enable secure and efficient digital asset management, with an emphasis on NFTs. The App's features include secure wallet network, NFT picture production, stamp out, a sale out, and account handling. The App's backend is built on the Goerli development network with reliability intelligent contracts, while IPFS and ReactJS/Ethers are utilized for scattered storage and client development, individually. Furthermore, the Open AI Api is used to create unique NFT picture depends on customer input. This design showcases the actual application of distributed larger technology and deep neural network models for creating Apps for assured and scattered crypto asset management. Universal, the project adds to the continuing study on blockchain-based solutions for secure digital asset management, while also emphasizing the power of distributed larger technology and deep neural network models to change the way we handle and trade crypto assets.
C. Anitha, R. Priyadharshini, E. Sivajothi, G Kumaran · 6 authors
Modern criminal investigations rely on forensic evidence management, which necessitates careful handling, safe storage, and precise documenting of the chain of custody. Problems including data manipulation, illegal access, and a lack of transparency plague conventional evidence management systems. In response to these concerns, this study introduces a novel approach to forensic evidence management that makes use of blockchain technology. Nowadays, data plays a crucial role in every stage of the work process. Since data might change, it should be secure. In a diverse organization, we shall handle data capacity and portrayal. Data that is vital to a particular organization might be the target of an attack. As the prevalence of cybercrime rises, malicious actors are taking increasingly cunning actions to alter such data. Whatever the case may be, it is significantly impacting the expected scientific confirmation of provenance. Since forensic evidence moves through many stages during scientific examination, it is supposed to be preserved in this manner. With this approach, a report is created and then passes through various levels of delegation, such as a pathology lab, a forensic lab, the police, and so on. To create a clear framework with immutable measurable confirmations, blockchain technology innovation is the best choice. The efficiency of the proposed scheme is evaluated by cross-validation with the conventional security model called Secured Forensic Evidence Handler (SFEH). This paper introduced a novel algorithm called Heterogeneous Key Generation for Forensic Data Safety (HKGFDS) to maintain forensic details in a safer manner. By demonstrating how blockchain technology has the ability to revolutionize forensic evidence management methods, this research adds to what is already known. Improved and more trustworthy criminal investigations are possible outcomes of this new line of inquiry and its use in practical forensic contexts. But wider acceptance can’t happen unless scalability and interoperability issues are resolved.
The openness and transparency of Ethereum transaction data make it easy to be exploited by any entities, executing malicious attacks. The sandwich attack manipulates the Automated Market Maker (AMM) mechanism, profiting from manipulating the market price through front or after-running transactions. To identify and prevent sandwich attacks, we propose a cascade classification framework GasTrace. GasTrace analyzes various transaction features to detect malicious accounts, notably through the analysis and modeling of Gas features. In the initial classification, we utilize the Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel to generate the predicted probabilities of accounts, further constructing a detailed transaction network. Subsequently, the behavior features are captured by the Graph Attention Network (GAT) technique in the second classification. Through cascade classification, GasTrace can analyze and classify the sandwich attacks. Our experimental results demonstrate that GasTrace achieves a remarkable detection and generation capability, performing an accuracy of 96.73% and an F1 score of 95.71% for identifying sandwich attack accounts.
Mrs. D. Thamizhisai, S. Bharathi, U. Bhuvaneshwaran, S. Mervin Immanuvel · 5 authors
The incorporation of blockchain technology into forensic investigations represents a significant advancement, tackling critical challenges within the legal and criminal justice systems. Central to this integration are smart contracts, which automate and secure essential aspects of investigations. These self-executing agreements operate based on predefined rules, ensuring integrity and transparency in tasks such as evidence tracking, chain of custody management, and access control. A key advantage lies in the substantial enhancement of data security. Blockchain's cryptographic principles and decentralized structure make it highly resistant to unauthorized access and tampering, crucial in maintaining evidence integrity. Additionally, blockchain's immutability ensures the reliability of information; once recorded, data becomes virtually unalterable, providing an indisputable ledger of events. In summary, this innovative integration streamlines operations, reduces errors and disputes, and strengthens the trustworthiness of forensic investigations by offering an unforgeable and transparent chain of custody and evidence history within the legal and criminal justice framework.
Lilita Infante, Roger A. Hallman, J. Ray Hays, Evelyn Cronnon · 5 authors
The increasing use of the Internet and cyber capabilities by criminals have made digital forensics a critical component of criminal investigations and prosecution. Criminal networks were early adopters of the cryptocurrency ecosystem; however, forensics capabilities for cryptocurrency-related investigations have been lacking. While investigators may utilize a number of commercially available digital forensics tools to search for cryptocurrency-related evidence, these tools are inadequate for the task. This paper presents Recovery CAT, a cryptocurrency-specific forensics tool for finding cryptographic artifacts or cryptographic material(s) such as seed phrases, addresses, private keys, wallet applications, hardware wallet usage, exchange domains, etc.
Yepeng Ding, Arthur Gervais, Roger Wattenhofer, Hiroyuki Satō
Decentralized finance (DeFi) is revolutionizing the traditional centralized finance paradigm with its attractive features such as high availability, transparency, and tamper-proofing. However, attacks targeting DeFi services have severely damaged the DeFi market, as evidenced by our investigation of 80 real-world DeFi incidents from 2017 to 2022. Existing methods, based on symbolic execution, model checking, semantic analysis, and fuzzing, fall short in identifying the most DeFi vulnerability types. To address the deficiency, we propose Context-Sensitive Concolic Verification (CSCV), a method of automating the DeFi vulnerability finding based on user-defined properties formulated in temporal logic. CSCV builds and optimizes contexts to guide verification processes that dynamically construct context-carrying transition systems in tandem with concolic executions. Furthermore, we demonstrate the effectiveness of CSCV through experiments on real-world DeFi services and qualitative comparison. The experiment results show that our CSCV prototype successfully detects 76.25% of the vulnerabilities from the investigated incidents with an average time of 253.06 seconds.
The blockchain ecosystem, particularly with the rise of Web3 and Non-Fungible Tokens (NFTs), has experienced a significant increase in users and applications. However, this expansion is challenged by the need to connect early adopters with a wider user base. A notable difficulty in this process is the complex interfaces of blockchain wallets, which can be daunting for those familiar with traditional payment methods. To address this issue, the category of "embedded wallets" has emerged as a promising solution. These wallets are seamlessly integrated into the front-end of decentralized applications (Dapps), simplifying the onboarding process for users and making access more widely available. However, our insights indicate that this simplification introduces a trade-off between ease of use and security. Embedded wallets lack transparency and auditability, leading to obscured transactions by the front end and a pronounced risk of fraud and phishing attacks. This paper proposes a new protocol to enhance the security of embedded wallets. Our VELLET protocol introduces a wallet verifier that can match the audit trail of embedded wallets on smart contracts, incorporating a process to verify authenticity and integrity. In the implementation architecture of the VELLET protocol, we suggest using the Text Record feature of the Ethereum Name Service (ENS), known as a decentralized domain name service, to serve as a repository for managing the audit trails of smart contracts. This approach has been demonstrated to reduce the necessity for new smart contract development and operational costs, proving cost-effective through a proof-of-concept. This protocol is a vital step in reducing security risks associated with embedded wallets, ensuring their convenience does not undermine user security and trust.
Archana B, Adithya Baragi S, K. N. Anusha, Jeevan Basri B S · 5 authors
Evidence management is crucial in the field of forensic science. Evidence obtained from a crime scene is important in solving the case and delivering justice to the victim involved. Hence, protecting the integrity of the evidence throughout the process is of prime importance. Chain of Custody (CoC) is the process which maintains the integrity of the evidence using Blockchain Technology. Inability to maintain the chain of custody will make the evidence inadmissible in court, eventually leading to the case dismissal. Digitalization of forensic evidence management system is a need of time as it is an environment friendly model. Blockchain are digitally distributed ledgers of transactions signed cryptographically in chronological order that are sorted into blocks and is completely open to anyone in the blockchain network. Present study aims to create a framework and further propose an algorithm to implement blockchain technology to digitalize forensic evidence management system and maintain Chain of Custody
Abstract: As a consequence of mass unemployment being the byproduct of COVID-19, people around the world discovered investment in cryptocurrency as a means to tackle their declining financial condition. Subsequently, the prominence of Ethereum as a platform for crypto transactions also gave rise to fraudulent transactions. The need to detect these frauds exists even today. This study proposes a token-based approach to detect fraud in Ethereum transactions incorporating the ERC20 standard, by employing machine learning techniques. After cleaning and preprocessing of the dataset, the transaction data was fed to Random Forest (RF), AdaBoost, Extra Trees (ET), Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) classifiers in search of the most suitable model for fraud detection. Meticulous evaluation revealed that RF, ET and XGB classifiers yielded the highest accuracy of 95%. The proposed token-based approach hence presents a novel and efficient solution for fraud detection, with room for improvement and scalability.
P. Maragathavalli, Aravindhar RS, R Keerthana, M. Harini · 5 authors
Cybercrime gives challenges to law enforcement agencies to secure digital evidence and maintain its integrity. Blockchain known for its decentralized and immutable nature, provides a secure ledger to record digital evidence transactions restricting unauthorized access. This project proposes a framework for digital forensic evidence management, contributing to the enhancement of security and reliability in digital forensic practices through the utilization of Ethereum Blockchain technology and Advanced Encryption Standard (AES) encryption. Through a systematic review, various studies, methodologies, and implementations employing Blockchain to safeguard digital evidence are explored. Blockchain, known for its decentralized and immutable nature, provides a secure ledger to record digital evidence transactions, restricting unauthorized access. Advanced Encryption Standard (AES) algorithm ensures that the data stored on the blockchain remains tamper-resistant and secure. In the blockchain ecosystem, Proof of Stake (POS) plays a critical role by facilitating transaction validation and block creation. It distinguishes itself by selecting validators based on the amount of cryptocurrency they 'stake' or pledge as collateral, offering an energy-efficient and environmentally sustainable alternative to the traditional method.
In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations.
Cryptocurrency tracker is an online platform that provides a userfriendly experience. Users get a simple and userfriendly experien ce through the user interface. Users can sign into their account with Gmail or a mobile number for easy access to their account. U sers can track prices of different cryptocurrencies and view currency charts. Using this user interface, users can find prices and ot her relevant information about cryptocurrencies. The app helps users to create watchlists and we can track prices. We can set alerts for cryptocurrency prices. We can customize notifications and help understand new cryptocurrency trends. Users can easily find various cryptocurrencies and track future crypt currency trends. It helps users invest in new popular cryptocurrencies that will be more useful to them in the future. Overall, the Cryptocurrency Tracker web app is a valuable tool for anyone looking to invest, trade, or just keep an eye on the cryptocurrency market. It provides realtime data and insights that can help users make informed investment decisions and stay abrea st of the latest industry trends and developments.
Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.
In recent years, Ethereum, which is a leading application for realizing blockchain services, has received much attention for its usability and functionality. Ethereum executes smart contracts and arbitrary programmable calculations, in addition to cryptocurrency trading. However, cyberattacks target misconfigured Ethereum clients with application programming interface (API) enabled, specifically JSON-RPC. Herein, we propose EtherWatch, a framework to detect and analyze malicious and/or suspicious Ethereum accounts using three data sources (a honeypot, an internet-wide scanner, and a blockchain explorer). The honeypot, named Etherpot, leverages a proxy server placed between a real Ethereum client and the internet. It modifies client responses to attract attackers, identifies malicious accounts, and analyzes their behaviors. Using scan results from Shodan, we also detect suspicious Ethereum accounts registered on multiple nodes. Finally, we utilize Etherscan, a well-known blockchain explorer, to track and analyze the activities of the detected accounts. During six weeks of observations, we discovered 538 hosts attempting to call JSON-RPC of our honeypots using 41 types of methods, including a type of unreported attack in the wild. Specifically, we observed account hijacking, mining, and smart contract attacks. We detected 16 malicious accounts using the honeypots and 64 suspicious accounts from the Shodan scan results, with five overlapping accounts. Finally, from Etherscan, we collected records of activities related to the detected accounts, including transactions of 21.50 ETH and mining of 22.61 ETH (equivalent to 39,494 US$ and 41,533 US$, respectively, as of June 9, 2023).
Τα πρόσφατα επιτεύγματα στην τεχνολογία της πληροφορίας, καθώς επίσης και η αυξανόμενη χρήση των ψηφιακών υπηρεσιών στην καθημερινότητα, έχουν δημιουργήσει ένα μεγάλο αριθμό από έξυπνες και αλληλένδετες συσκευές για διάφορα οικοσυστήματα του Διαδίκτυο των Πραγμάτων (ΔτΠ), όπως το έξυπνο σπίτι, τις μεταφορές, την υγειονομική περίθαλψη, κ.α. Η μετάδοση της κρίσιμης πληροφορίας στο Διαδίκτυο, χωρίς την ανάγκη της ανθρώπινης παρέμβασης, παρέχει ανεξαρτησία και ανέσεις στους χρήστες των συσκευών του ΔτΠ, αλλά επίσης κάνει το οικοσύστημα αυτό ευάλωτο σε έναν αυξανόμενο αριθμό από παράνομες ενέργειες καθώς δημιουργεί έναν ολόκληρο νέο κόσμο από ευκαιρίες σε επιτιθέμενους. Μια λύση βασιζόμενη σε Συστήματα Ανίχνευσης Εισβολών (ΣΑΕ) θα μπορούσε να αυξήσει την ανθεκτικότητα ενός δικτύου ενάντια σε επιθέσεις, καθώς μπορεί να ανιχνεύσει κακόβουλη δραστηριότητα, να παρακολουθήσει τις συσκευές του ΔτΠ υπό επίθεση, αλλά επίσης και να καταγράψει τις κακόβουλες ενέργειες με ειδοποιήσεις ασφαλείας. Για την ανίχνευση και αντιμετώπιση την περίπλοκων και μεγάλης-κλίμακας επιθέσεων σε συσκευές του ΔτΠ, μπορεί να δημιουργηθεί ένα Συνεργατικό Συστήμα Ανίχνευσης Εισβολών (ΣΣΑΕ) στο οποίο θα ανταλλάσσονται οι ειδοποιήσεις ασφαλείας μεταξύ των ΣΑΕ κόμβων. Παρόλα αυτά, για την επίτευξη υψηλού επιπέδου ασφαλείας σε ένα ΣΣΑΕ θα πρέπει να υπάρχει αμοιβαία εμπιστοσύνη στο ΣΣΑΕ δίκτυο για την λήψη πληροφοριών μόνο από αξιόπιστους κόμβους, καθώς κάποιοι εξ’ αυτών μπορεί να γίνουν κακόβουλοι, με σκοπό να μειώσουν το επίπεδο ασφαλείας του δικτύου. Συνεπώς, είναι απαραίτητο κάθε ΣΣΑΕ κόμβος να παρακολουθεί συνεχόμενα την συμπεριφορά των υπολοίπων και να υπολογίζει έναν βαθμό αξιοπιστίας σύμφωνα με τη συμπεριφορά τους. Εκτός όμως από τους συνεργατικούς κόμβους, ένας ΣΣΑΕ κόμβος πρέπει επίσης να παρακολουθεί και την εισερχόμενη κίνηση στο δίκτυο και να αξιολογεί την αξιοπιστία των συσκευών του ΔτΠ. Παρόλα αυτά, ακόμα και με την ανάπτυξη ενός ΣΣΑΕ, είναι εφικτό να συμβούν επιθέσεις στο οικοσύστημα του ΔτΠ, με την ανίχνευσή τους να απαιτεί σφοδρή ανάλυση δεδομένων και υπολογιστική νοημοσύνη, καθώς οι περισσότερες εξ’ αυτών μπορεί να είναι εκλεπτυσμένες και δόλιες, κάτι που σημαίνει ότι, εφόσον εκτελεστούν, μπορούν να τροποποιήσουν την οποιαδήποτε πληροφορία που θα μπορούσε να χρησιμοποιηθεί ως ψηφιακό αποδεικτικό στοιχείο. Με αυτόν τον αυξανόμενο αριθμό από επιθέσεις, ένας νέος επιστημονικός χώρος, που ονομάζεται “Εγκληματολογία του ΔτΠ” δημιουργήθηκε ως ένας κλάδος της Εγκληματολογίας με επίκεντρο την διερεύνηση των κυβερνο-επιθέσεων σε ένα οικοσύστημα του ΔτΠ. Κληρονομώντας τους περιορισμούς της ψηφιακής Εγκληματολογίας η πιστοποίηση και η ακεραιότητα των ψηφιακών αποδεικτικών στοιχείων, καθώς επίσης και η διατήρηση της προστασίας των προσωπικών δεδομένων αποτελούν κάποιους από τους κύριους παράγοντες που επηρεάζουν τη διαδικασία της διερεύνησης των ψηφιακών αποδεικτικών στοιχείων του ΔτΠ. Οι μηχανισμοί κατανεμημένων μητρώων έχουν πρόσφατα αναπτυχθεί με προφανή εφαρμογή το ΔτΠ, ενισχύοντας σημαντικά την ασφάλεια των κατανεμημένων δικτύων παρέχοντας καινοτόμες λύσεις, οι οποίες μπορούν να διατηρήσουν την προστασία των προσωπικών δεδομένων ενός χρήστη μιας έξυπνης συσκευής. Συνεπώς, οι κύριες ιδιότητες των μηχανισμών κατανεμημένων μητρώων, όπως η διαλειτουργικότητα, η αποκέντρωση και η ασφάλεια μπορούν να προσφέρουν ευεργετικά χαρακτηριστικά σε κάθε μια από τις προαναφερθείς περιοχές ενδιαφέροντος. Προς αυτή την κατεύθυνση, η τεχνολογία κατανεμημένων μητρώων δύναται να ενσωματωθεί σε ένα ΣΣΑΕ και να αποφέρει επιπρόσθετη ασφάλεια. Ακόμα και στην περίπτωση που ένα οικοσύστημα του ΔτΠ είναι υπό επίθεση, τα ψηφιακά αποδεικτικά στοιχεία που την αφορούν, να μπορούν να αποθηκευτούν κατανεμημένα και με ασφαλή τρόπο. Επομένως, τα ευεργετικά χαρακτηριστικά των μηχανισμών κατανεμημένων μητρώων συσχετίζονται άμεσα με μια ψηφιακή έρευνα αποδεικτικών στοιχείων, η οποία είναι εφικτό να γίνει με διαφανή τρόπο, καθώς το χρονολογικό ιστορικό χειρισμού των αποδεικτικών στοιχείων καταγράφεται. Μια αρχιτεκτονική, η οποία θα εξάγει ψηφιακά αποδεικτικά στοιχεία από το οικοσύστημα του ΔτΠ, με τη βοήθεια των μηχανισμών των κατανεμημένων μητρώων, όπου στη βάση της θα έχει αναπτυχθεί ένα ΣΣΑΕ, καθώς επίσης και ένα σύστημα διαχείρισης εμπιστοσύνης, θα μπορούσε να αντιμετωπίσει πολλές επιθέσεις σε συσκευές του ΔτΠ και να μειώσει τους παράγοντες που επηρεάζουν την διερεύνηση ψηφιακών αποδεικτικών στοιχείων. Η παρούσα διδακτορική διατριβή αρχικά προτείνει ένα πλαίσιο ανάλυσης καταλληλότητας των μηχανισμών κατανεμημένων μητρώων στο οικοσύστημα του ΔτΠ, με εφαρμογή σε ένα μεγάλο πλήθος από αλγόριθμους συναίνεσης και πλατφόρμες κατανεμημένων μητρώων, ώστε να οριστεί η ικανότητά τους να αντιμετωπίσουν τις πιο κρίσιμες προκλήσεις του ΔτΠ, με καίρια περιοχή αναφοράς το έξυπνο σπίτι. Βασικές αρχιτεκτονικές πτυχές των μηχανισμών κατανεμημένων μητρώων, όπως το λογισμικό της πλατφόρμας και οι ρυθμίσεις του δικτύου, οι αλγόριθμοι συναίνεσης, καθώς επίσης και η ασφάλεια των έξυπνων συμβολαίων, εξετάζονται σε αυτό το πλαίσιο σχετικά με την ικανότητά τους να αντιμετωπίσουν ένα μεγάλο πλήθος από κοινές απειλές του ΔτΠ και των μηχανισμών κατανεμημένων μητρώων, να προσφέρουν βελτιωμένες δυνατότητες προστασίας προσωπικών δεδομένων, και να εξασφαλίσουν επαρκή επίπεδα επιδόσεων όταν επεξεργάζονται μεγάλο όγκο δεδομένων. Έπειτα, προτείνεται μια ολιστική διαδικασία για την διερεύνηση ψηφιακών αποδεικτικών στοιχείων τα οποία εξάγονται από το ΔτΠ, έχοντας ένα σημείο αναφοράς έναντι του οποίου πρότυπα ενσωμάτωσης της τεχνολογίας κατανεμημένων μητρώων και βέλτιστες πρακτικές έχουν ταυτοποιηθεί ώστε να δημιουργηθεί μια νέα, ευρέως αποδεκτή και επεκτάσιμη αρχιτεκτονική. Η τεχνολογία κατανεμημένων μητρώων έχει ενσωματωθεί με την προτεινόμενη διαδικασία εξαγωγής ψηφιακών αποδεικτικών στοιχείων από το ΔτΠ, για να αντιμετωπίσει τις παραπάνω προκλήσεις και να αξιολογήσει την συνολική προτεινόμενη λύση δίνοντας ιδιαίτερη έμφαση σε βελτιώσεις και σε απόδοση. Η προτεινόμενη πλατφόρμα, η οποία βασίζεται στην τεχνολογία MEC, έχει υλοποιηθεί σε έναν μηχανισμό κατανεμημένων μητρώων, ο οποίος ονομάζεται Hyperledger Fabric, και σε ένα εικονικό περιβάλλον, παρέχοντας ένα ρεαλιστικό οικοσύστημα έξυπνου σπιτιού. Μια ενδελεχής υλοποίηση διεξήχθη, με πραγματικές κυβερνο-επιθέσεις για τη δημιουργία ψηφιακών αποδεικτικών στοιχείων σε υψηλούς ρυθμούς, ώστε να δοκιμαστεί η αντοχή της πλατφόρμας κατανεμημένων μητρώων σε υψηλό φορτίο. Ένα νέο σύστημα διαχείρισης εμπιστοσύνης προτάθηκε για την προστασία της ακεραιότητας της πληροφορίας που ανταλλάσσεται μεταξύ των ΣΣΑΕ κόμβων. Η μοντελοποίηση της αξιοπιστίας των κόμβων επιτρέπει την στάθμιση διαφορετικής βαρύτητας στην πιο πρόσφατη συμπεριφορά τους, ώστε να γίνεται αναπροσαρμογή του μοντέλου αξιοπιστίας σύμφωνα με την κάθε αλλαγή της. Τα πειραματικά αποτελέσματα δείχνουν ότι η προτεινόμενη πλατφόρμα μπορεί να παρέχει υψηλή απόδοση, υπερβολικά χαμηλή καθυστέρηση, και μηδέν ποσοστά σφαλμάτων κατά τη λειτουργία του μηχανισμού κατανεμημένων μητρώων.