Abstract We report our experience in the formal verification of the reference implementation of the Beacon Chain. The Beacon Chain is the backbone component of the new Proof-of-Stake Ethereum 2.0 network: it is in charge of tracking information about the validators , their stakes , their attestations (votes) and if some validators are found to be dishonest, to slash them (they lose some of their stakes). The Beacon Chain is mission-critical and any bug in it could compromise the whole network. The Beacon Chain reference implementation developed by the Ethereum Foundation is written in Python, and provides a detailed operational description of the state machine each Beacon Chain’s network participant (node) must implement. We have formally specified and verified the absence of runtime errors in (a large and critical part of) the Beacon Chain reference implementation using the verification-friendly language Dafny. During the course of this work, we have uncovered several issues, proposed verified fixes. We have also synthesised functional correctness specifications that enable us to provide guarantees beyond runtime errors. Our software artefact with the code and proofs in Dafny is available at https://github.com/ConsenSys/eth2.0-dafny .
Detection of fabricated or manipulated audio content to prevent, e.g., distribution of forgeries in digital media, is crucial, especially in political and reputational contexts. Better tools for protecting the integrity of media creation are desired. Within the paradigm of the Internet of Audio Things(IoAuT), we discuss the ability of the IoAuT network to verify the authenticity of original audio using distributed ledger technology. By storing audio recordings in combination with associated recording-specific metadata obtained by the IoAuT capturing device, this architecture enables secure distribution of original audio footage, authentication of unknown audio content, and referencing of original audio material in future derivative works. By developing a proof-of-concept system, the feasibility of the proposed architecture is evaluated and discussed.
Edward Henry Young, Christos Chrysoulas, Nikolaos Pitropakis, Pavlos Papadopoulos · 5 authors
Little or no research has been directed to analysis and researching forensic analysis of the Bitcoin mixing or ‘tumbling’ service themselves. This work is intended to examine effective tooling and methodology for recovering forensic artifacts from two privacy focused mixing services namely Obscuro which uses the secure enclave on intel chips to provide enhanced confidentiality and Wasabi wallet which uses CoinJoin to mix and obfuscate crypto currencies. These wallets were set up on VMs and then several forensic tools used to examine these VM images for relevant forensic artifacts. These forensic tools were able to recover a broad range of forensic artifacts and found both network forensics and logging files to be a useful source of artifacts to deanonymize these mixing services.
Alexandre Pinheiro, Edna Dias Canedo, Robson de Oliveira Albuquerque, Rafael T. de Sousa
The management practicality and economy offered by the various technological solutions based on cloud computing have attracted many organizations, which have chosen to migrate services to the cloud, despite the numerous challenges arising from this migration. Cloud storage services are emerging as a relevant solution to meet the legal requirements of maintaining custody of electronic documents for long periods. However, the possibility of losses and the consequent financial damage require the permanent monitoring of this information. In a previous work named "Monitoring File Integrity Using Blockchain and Smart Contracts", the authors proposed an architecture based on blockchain, smart contract, and computational trust technologies that allows the periodic monitoring of the integrity of files stored in the cloud. However, the experiments carried out in the initial studies that validated the architecture included only small- and medium-sized files. As such, this paper presents a validation of the architecture to determine its effectiveness and efficiency when storing large files for long periods. The article provides an improved and detailed description of the proposed processes, followed by a security analysis of the architecture. The results of both the validation experiments and the implemented defense mechanism analysis confirm the security and the efficiency of the architecture in identifying corrupted files, regardless of file size and storage time.
Evidence management is crucial in the field of forensic science. Evidences obtained from a crime scene are important in solving the case and delivering justice to the parties involved. Hence, protecting these evidences from any form of alteration is of utmost important. Chain of Custody is the process which maintains the integrity of evidence. 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. Blockchains 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. Hyperledger Fabric is a consortium blockchain framework created by the Linux foundation and is mainly used for enterprise use. Based on the concept of Hyperledger Fabric, present study aimed to create a framework and further propose an algorithm to implement Blockchain Technology to digitalize forensic evidence management system and maintain Chain of Custody.
Due to the growing use of cloud services and the possible hazards involved with storing and processing sensitive data in remote locations, data security is a crucial concern in cloud computing. This literature review analyses the state of the art in data security research for cloud computing and emphasises the major discoveries, difficulties, and recommendations for the future. Encryption, access control, network security, physical security, security monitoring, data segregation, backup and disaster recovery, compliance, and safe development practises are just a few of the data security-related topics included in the assessment. The study notes that a problem is the size and quantity of the body of literature as well as the requirement to stay up with the continuously changing area. The need for thorough coverage, evaluation of source quality and reliability, and overcoming language and accessibility challenges are also mentioned as drawbacks. The review goes into further detail on issues including time limits, subjectivity in selection and interpretation, information overload, and integration and synthesis of findings. It is recommended that future research in data security for cloud computing put a particular emphasis on advanced encryption methods, secure data sharing and collaboration, privacy-preserving methods, cloud forensics, threat intelligence, machine learning, security assurance and auditing, blockchain and distributed ledger technologies, as well as user awareness and education. Data security in cloud computing may be further reinforced to secure sensitive data and guarantee the reliability of cloud services by solving these issues and making progress in the sector.
Digital forensic in Internet-of-Thing (IoT) paradigm is critical due to its heterogeneity and lack of transparency of evidence processing. Moreover, cross-border legalization makes a hindrance in such process pertaining to the cloud forensic issues. This urges a forensic framework for IoT which provides distributed computing, decentralization, and transparency of forensic investigation of digital evidences in cross-border perspectives. To this end, we propose a framework for IoT forensics that addresses the above mentioned issues. The proposed solution called Internet-of-Forensics (IoF) considers a blockchain tailored IoT framework for digital forensics. It provides a transparent view of the investigation process that involves all the stakeholders (e.g., heterogeneous devices, and cloud service providers) in a single framework. It uses blockchain-based case chain to deal with the investigation process including chain-of-custody and evidence chain. Consensus is used for consortium to solve the problems of cross-border legalization. This is also beneficial for a transparent and ease of forensic reference. The programmable lattice-based cryptographic primitives produce reduced complexities. It shows benefits for power-aware devices and puts an add-on to the novelty of the presented idea. IoF is generic; hence, it can be used by autonomous security operation centers, cyber-forensic investigators and manually initiated evidences under chain-of-custody for man-made crimes. Security services are assured as required by the framework. IoF is experimented and compared with the other state-of-the-art frameworks. The outcomes and analysis prove the efficiency of IoF concerning complexity, time consumption, memory and CPU utilization, gas consumption, and energy analysis.
With the increasing number of cybercrimes, the digital forensics team has no choice but to implement more robust and resilient evidence-handling mechanisms. The capturing of digital evidence, which is a tangible and probative piece of information that can be presented in court and used in trial, is very challenging due to its volatility and improper handling procedures. When computer systems get compromised, digital forensics comes into play to analyze, discover, extract, and preserve all relevant evidence. Therefore, it is imperative to maintain efficient evidence management to guarantee the credibility and admissibility of digital evidence in a court of law. A critical component of this process is to utilize an adequate chain of custody (CoC) approach to preserve the evidence in its original state from compromise and/or contamination. In this paper, a practical and secure CustodyBlock (CB) model using private blockchain protocol and smart contracts to support the control, transfer, analysis, and preservation monitoring is proposed. The smart contracts in CB are utilized to enhance the model automation process for better and more secure evidence preservation and handling. A further research direction in terms of implementing blockchain-based evidence management ecosystems, and the implications on other different areas, are discussed.
Jieh‐Haur Chen, Mu‐Chun Su, Vidya Trisandini Azzizi, Ting-Kwei Wang · 5 authors
Technological developments have made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using the interactive concept of natural language processing technology. A comprehensive literature review and expert interviews associated with techniques dealing with natural languages suggests the proposed system containing the Progressive Scale Expansion Network (PSENet), Convolutional Recurrent Neural Network (CRNN), and Bi-directional Recurrent Neutral Networks Convolutional Recurrent Neural Network (BRNN-CNN) toolboxes to extract the key words for construction projects contracts. The results show that a fully automatic platform facilitating contract management is achieved. For academic domains, the Contract Keyword Detection (CKD) mechanism integrating PSENet, CRNN, and BRNN-CNN approaches to cope with real-time massive document flows is novel in the construction industry. For practice, the proposed approach brings significant reduction for manpower and human error, an alternative for settling down misunderstanding or disputes due to real-time and precise communication, and a solution for efficient documentary management. It connects all contract stakeholders proficiently.
Since digital forensics becomes more and more popular, more and more attention has been paid to the originality and validity of data, and data preservation technology emerges as the times require. However, the current data preservation models and technologies are only the combination of cryptography technology, and there is a risk of being attacked and cracked. And in the process of data preservation, human participation is also needed, which may lead to data tampering. To solve problems given, this paper presents a data preservation model based on blockchain and multidimensional hash. With the decentralization and smart contract characteristics of blockchain, data can be automatically preserved without human participation to form a branch chain of custody in the unit of case, and blockchain has good antiattack performance, which is the so‐called 51% attack. Meanwhile, in order to solve the problem of data confusion and hard to query caused by the excessive number of cases, hash, cryptography, and timestamps are used to form a serialized main chain of custody. Because of the confliction problem of hash and judicial trial needs to absolutely guarantee the authenticity and validity of data, multidimensional hash is used to replace regular hash. In this way, the data preservation becomes an automatic, nonhuman‐interventional process. Experiments have been carried out to show the security and effectiveness of the proposed model.
Due to businesses’ growing use of IoT services in their day-to-day operations and the increased use of smart devices, digital forensic investigations involving such systems will need increasingly sophisticated digital evidence collection and processing. The majority of IoT systems are composed of disparate software and hardware components, which may pose security and privacy concerns. Recently, blockchain technology was presented as one of the options for achieving IoT security via the use of an immutable ledger, a decentralized architecture, and strong cryptographic primitives. Integrating blockchain platforms with IoT-based applications, on the other hand, poses a number of difficulties owing to the trustworthiness, integrity, and real-time responsiveness of IoT data. However, certain IoT devices may be incompatible with existing blockchain-based IoT forensic methods for additional digital evidence processing owing to their usage of conventional hash. A critical feature of cryptographic hash functions is that even if just one bit of the input is altered, the output acts pseudo-randomly, making it impossible to identify identical files. However, in the field of computer forensics, it is essential to locate comparable files (e.g., various versions of a file); therefore, we need a hash function that preserves similarity. It is getting more difficult to establish how forensic investigators might utilize traces from such devices. To effectively deal with IoT digital forensics applications, this article presents an improved blockchain-based IoT digital forensics architecture that use fuzzy hash to construct the Blockchain’s Merkle tree in addition to the conventional hash for authentication. Fuzzy hashing enables the identification of potentially damning documents that might otherwise remain undiscovered using conventional hashing techniques. By comparing blocks/files to all nodes in the blockchain network using fuzzy hash similarity, the digital forensics investigator will be able to verify their authenticity. To support the proof of concept, we simulated the suggested model.
Open access
Digital and Cyber Forensics
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
It has become a truism that the speed of technological progress leaves law and policy scrambling to keep up. But in addition to creating new challenges, technological advances also enable new improvements to issues at the intersection of law and technology. In this thesis, I develop new cryptographic tools for informing and improving our law and policy, including specific technical innovations and analysis of the limits of possible interventions. First, I present a cryptographic analysis of a legal question concerning the limits of the Fifth Amendment: can courts legally compel people to decrypt their devices? Our cryptographic analysis is useful not only for answering this specific question about encrypted devices, but also for analyzing questions about the wider legal doctrine. The second part of this thesis turns to algorithmic fairness. With the rise of automated decision-making, greater attention has been paid to statistical notions of fairness and equity. In this part of the work, I demonstrate technical limits of those notions and examine a relaxation of those notions; these analyses should inform legal or policy interventions. Finally, the third section of this thesis describes several methods for improving zero-knowledge proofs of knowledge, which allow a prover to convince a verifier of some property without revealing anything beyond the fact of the prover's knowledge. The methods in this work yield a concrete proof size reduction of two plausibly post-quantum styles of proof with transparent setup that can be made non-interactive via the Fiat-Shamir transform: "MPC-in-the-head," which is a linear-size proof that is fast, low-memory, and has few assumptions, and "Ligero," a sublinear-size proof achieving a balance between proof size and prover runtime. We will describe areas where zero-knowledge proofs in general can provide new, currently-untapped functionalities for resolving legal disputes, proving adherence to a policy, executing contracts, and enabling the sale of information without giving it away.
Open access
Cryptography and Data Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
Abdullah Ayub Khan, Mueen Uddin, Aftab Ahmed Shaikh, Asif Ali Laghari · 5 authors
Due to globalization and worldwide connectivity, multimedia data exchange has increased significantly over the Internet in the last decade. The life cycle of multimedia content is also getting more multifaceted as more people are accessing, sharing, modifying and re-using multimedia information. This poses serious challenges for the multimedia industry to provide integrity, reliability and trustworthiness for multimedia investigations against the growing cybersecurity threats. This paper bridges this gap by enabling a secure and transparent digital forensic investigations process using blockchain technology. MF-Ledger a Blockchain Hyperledger sawtooth-enabled novel, secure and efficient digital forensic investigation architecture is proposed where participating stakeholders create a private network to exchange and agree on different investigation activities before being stored on the blockchain ledger. We have created digital contracts (smart contracts) and implemented them using sequence diagrams to handle the stakeholders' secure interaction in the investigation process. The proposed architectural solution delivers robust information integrity, prevention, and preservation mechanism to permanently and immutably store the evidence (chain of custody) in a private permissioned encrypted blockchain ledger.
Open access
Blockchain Technology Applications and Security
Digital and Cyber Forensics
Advanced Steganography and Watermarking Techniques
With the advancing technologies become tools for illicit activities and avoid detection, digital forensics is necessary for law enforcement in modern criminal investigations. In order to maintain the integrity and authenticity of the evidence, a chain of custody is essential for the successful prosecution of criminals in court. However, it is a great challenge to manage the preservation and collection of digital evidence because of its fragile and volatile in nature. Blockchain has been proposed as a promising and reliable technology to provide immutability and traceability of digital content, however, the applications of blockchain to the law enforcement agencies (LEAs) requires special attention to the security issue. In this research, we proposed a blockchain of custody framework to facilitate the security and transparency of digital evidence in criminal investigation process. The framework is implement on Ethereum smart contract to support authenticity and integrity of digital evidence in preliminary investigation, case management and court phases. We also propose the role of investigator to leverage access control in evidence creation, transferring and modification. The corresponding actions with judicial process is simulated using private ethereum blockchain. The experimental results indicate that the proposed framework can prevent digital evidence been tampered or contaminated and assure its legal defensibility with rigorous privilege management. Moreover, by successfully synchronizing digital evidence transactions to multiple nodes ensures the accountability of evidence data for every involved law enforcement agency.
Luca Caviglione, Michał Choraś, Igino Corona, Artur Janicki · 7 authors
Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the “classical” crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim’s machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird’s eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection.
Nathan Martindale, Scott Stewart, Mark Adams, Greg Westphal
In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.
Omi Akter, Arnisha Akther, Md. Ashraf Uddin, Md. Manowarul Islam
With the advancement in digital forensics, digital forensics has been evolved in Cloud computing.A common process of digital forensics mainly includes five steps: defining problem scenario, collection of the related data, investigation of the crime scenes, analysis of evidences and case documentation.The conduction of digital forensics in cloud results in several challenges, security, and privacy issues.In this paper, several digital forensics approaches in the context of IoT and cloud have been presented.The review focused on zone-based approach for IoT digital forensics where the forensics process is divided into three zones.Digital forensics in cloud provides the facilities of large data storage, computational capabilities and identification of criminal activities required for investigating forensics.We have presented a brief study on several issues and challenges raised in each phase of Cloud forensics process.The solution approaches as well as advancement prospects of cloud forensics have been described in the light of Blockchain technology.These studies will broaden the way to new researchers for better understanding and devising new ideas for combating the challenges.
The financial crime landscape is evolving along with the digitization in financial services. In this context, laws and regulations cannot efficiently cope with a fast-moving industry such as finance, which translates in late adoption of measures and legal voids, providing a fruitful landscape for malicious actors. In parallel, blockchain technology and its promising features such as immutability, verifiability, and authentication, enhance the opportunities of financial forensics. In this paper, we focus on an embezzlement scheme and we provide a forensic-by-design methodology for its investigation. In addition, the feasibility and adaptability of our approach can be extended and embrace digital investigations on other types of schemes. We provide a functional implementation based on smart contracts and we integrate standardised forensic flows and chain of custody preservation mechanisms. Finally, we discuss the benefits and challenges of the symbiotic relationship between blockchain and financial investigations, along with future research directions.
Tyler Thomas, Mathew Piscitelli, Ilya Shavrov, Ibrahim Baggili
We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the Ledger and Trezor hardware wallet clients. The framework can be generalized for use in future memory forensics work.
Thomas K. Dasaklis, Fran Casino, Constantinos Patsakis
As the digitization of information-intensive processes gains momentum in nowadays, the concern is growing about how to deal with the ever-growing problem of cybercrime. To this end, law enforcement officials and security firms use sophisticated digital forensics techniques for analyzing and investigating cybercrimes. However, multi-jurisdictional mandates, interoperability issues, the massive amount of evidence gathered (multimedia, text etc.) and multiple stakeholders involved (law enforcement agencies, security firms etc.) are just a few among the various challenges that hinder the adoption and implementation of sound digital forensics schemes. Blockchain technology has been recently proposed as a viable solution for developing robust digital forensics mechanisms. In this paper, we provide an overview and classification of the available blockchain-based digital forensic tools, and we further describe their main features. We also offer a thorough analysis of the various benefits and challenges of the symbiotic relationship between blockchain technology and the current digital forensics approaches, as proposed in the available literature. Based on the findings, we identify various research gaps, and we suggest future research directions that are expected to be of significant value both for academics and practitioners in the field of digital forensics.
Suat Mercan, Mumin Cebe, Ege Tekiner, Kemal Akkaya · 6 authors
IoT devices have been adopted widely in the last decade which enabled collection of various data from different environments. The collected data is crucial in certain applications where IoT devices generate data for critical infrastructure or systems whose failure may result in catastrophic results. Specifically, for such critical applications, data storage poses challenges since the data may be compromised during the storage and the integrity might be violated without being noticed. In such cases, integrity and data provenance are required in order to be able to detect the source of any incident and prove it in legal cases if there is a dispute with the involved parties. To address these issues, blockchain provides excellent opportunities since it can protect the integrity of the data thanks to its distributed structure. However, it comes with certain costs as storing huge amount of data in a public blockchain will come with significant transaction fees. In this paper, we propose a highly cost effective and reliable digital forensics framework by exploiting multiple inexpensive blockchain networks as a temporary storage before the data is committed to Ethereum. To reduce Ethereum costs,we utilize Merkle trees which hierarchically stores hashes of the collected event data from IoT devices. We evaluated the approach on popular blockchains such as EOS, Stellar, and Ethereum by presenting a cost and security analysis. The results indicate that we can achieve significant cost savings without compromising the integrity of the data.