Blockchain Papers

Sagar S. Rane, Arati M. Dixit

No abstract is available for this record.

Joseph A. Ricci, Ibrahim Baggili, Frank Breitinger

The current state of the art in digital forensics has primarily focused on the acquisition of data from cloud storage. Here, we present a new challenge in digital forensics: blockchain-based distributed cloud storage, using STORJ as a technology example.

Yijun Yu, Danny Barthaud, Blaine Price, Arosha K. Bandara · 6 authors

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly expected to operate in spaces populated by humans while avoiding injury to people or damaging property. However, incidents and accidents can, and increasingly do, happen. Traditional investigations of aircraft incidents require on-board flight data recorders (FDRs); however, these physical FDRs only work if the drone can be recovered. A further complication is that physical FDRs are too heavy to mount on light drones, hence not suitable for forensic digital investigations of drone flights. In this paper, we propose a self-adaptive software architecture, LiveBox, to make drones both forensic-ready and regulation compliant. We studied the feasibility of using distributed technologies for implementing the LiveBox reference architecture. In particular, we found that updates and queries of drone flight data and constraints can be treated as transactions using decentralised ledger technology (DLT), rather than a generic time-series database, to satisfy forensic tamper-proof requirements. However, DLTs such as Ethereum, have limits on throughput (i.e. transactions-per-second), making it harder to achieve regulation-compliance at runtime. To overcome this limitation, we present a self-adaptive reporting algorithm to dynamically reduce the precision of flight data without sacrificing the accuracy of runtime verification. Using a real-life scenario of drone delivery, we show that our proposed algorithm achieves a 46% reduction in bandwidth without losing accuracy in satisfying both tamper-proof and regulation-compliant requirements.

David Billard

This paper proposes the use of a blockchainbased structure in order to store evidences in a digital forensics investigation. The traditional chain of evidence is augmented with properties of immutability and traceability, thanks to a cryptographic process. The blockchain is constructed by forensics experts by adding evidences through the process. Since the blockchain is immutable, it can be shared among the different parties involved in a prosecution in order to review the chain of evidence and build their case. Furthermore, the blockchain structure can be applied to other forensics fields, like drugs, firearms, NDA. This blockchain is called a Digital Evidence Inventory (DEI) and is part of a wider framework encompassing a Forensics Confidence Rating (FCR) structure, in order to give experts the ability to rate the level of confidence for each evidence and a Global Digital Timeline (GDT) to order evidence through time. The whole framework is called ‘Aldiana’.

Adrien Ghosn, James R. Larus, Edouard Bugnion

Trusted Execution Environments (TEEs), such as Intel SGX enclaves, use hardware to ensure the confidentiality and integrity of operations on sensitive data. While the technology is available on many processors, the complexity of its programming model and its performance overhead have limited adoption. TEEs provide a new and valuable hardware functionality that has no obvious analogue in programming languages, which means that developers must manually partition their application into trusted and untrusted components. This paper describes an approach that fully integrates trusted execution into a language. We extend the Go language to allow a programmer to execute a goroutine within an enclave, to use low-overhead channels to communicate between the trusted and untrusted environments, and to rely on a compiler to automatically extract the secure code and data. Our prototype compiler and runtime, GOTEE, is a backward-compatible fork of the Go compiler. The evaluation shows that our compiler-driven code and data partitioning efficiently executes both microbenchmarks and applications. On the former, GOTEE achieves a 5.2×throughput and a 2.3× latency improvement over the Intel SGX SDK. Our case studies, a Go ssh server, the Go tls package, and a secured keystore inspired by the go-ethereum project, demonstrate that minor source-code modifications suffice to provide confidentiality and integrity guarantees with only moderate performance overheads.

Weihong Huang, Renfa Li, Jianbo Xu, Yin Huang · 5 authors

The rapid advancement in internet of things (IoTs) has created new demands for intellectual property (IP) protection. Existing zero-knowledge-based IP watermark detection schemes have low performance in real-time detection. To address this issue, we propose a position fuzzification-based IP watermark detection scheme mainly aiming to reduce the rounds of inquiry. Firstly, a random sequence is produced by chaos system to scramble all the resource positions of an IP design. Then watermark positions are fuzzified to make the scramble algorithm irreversible. A verifier can achieve zero-knowledge proof of IP ownership by one round of inquiry. Experiments show that the proposed scheme can greatly reduce computational complexity during blind IP watermark detection and enhance performance in real-time watermark detection.

Constantinos Pouyioukka, Thanassis Giannetsos, Weizhi Meng

No abstract is available for this record.

Mayra Samaniego, Ralph Deters

No abstract is available for this record.

Mikerah Quintyne-Collins

No abstract is available for this record.

Mehran Pourvahab, Gholamhossein Ekbatanifard

Cloud forensics is an intelligent evolution of digital forensics that defends against cyber-crimes. However, centralized evidence collection and preservation minimizes the reliability of digital evidence. To resolve this severe problem, this paper proposes a novel digital forensic architecture using fast-growing Software-Defined Networking (SDN) and Blockchain technology for Infrastructure-as-a-Service (IaaS) cloud. In this proposed forensic architecture, the evidence is collected and preserved in the blockchain that is distributed among multiple peers. To protect the system from unauthorized users, Secure Ring Verification based Authentication (SRVA) scheme is proposed. To strengthen the cloud environment, secret keys are generated optimally by using Harmony Search Optimization (HSO) algorithm. All data are encrypted based on the sensitivity level and stored in the cloud server. For encryption, Sensitivity Aware Deep Elliptic Curve Cryptography (SA-DECC) algorithm is presented. For every data stored in the cloud, a block is created in the SDN controller and the history of data is recorded as metadata. In each block, the Merkle hash tree is built by using Secure Hashing Algorithm-3 (SHA-3). Our system allows users to trace their data by deploying Fuzzy based Smart Contracts (FCS). Finally, evidence analysis is enabled by constructing Logical Graph of Evidence (LGoE) collected from the blockchain. Experiments are conducted in an integrated environment of java (for cloud and blockchain) and network simulator-3.26 (for SDN). The extensive analysis shows that proposed forensic architecture shows promising results in Response time, Evidence insertion time, Evidence verification time, Communication overhead, Hash computation time, Key generation time, Encryption time, Decryption time and total change rate.

Konrad Weiss, Julian Schütte

Ethereum smart contracts are executable programs, deployed on a peer-to-peer network and executed in a consensus-based fashion. Their bytecode is public, immutable and once deployed to the blockchain, cannot be patched anymore. As smart contracts may hold Ether worth of several million dollars, they are attractive targets for attackers and indeed some contracts have successfully been exploited in the recent past, resulting in tremendous financial losses. The correctness of smart contracts is thus of utmost importance. While first approaches on formal verification exist, they demand users to be well-versed in formal methods which are alien to many developers and are only able to analyze individual contracts, without considering their execution environment, i.e., calls to external contracts, sequences of transaction, and values from the actual blockchain storage. In this paper, we present Annotary, a concolic execution framework to analyze smart contracts for vulnerabilities, supported by annotations which developers write directly in the Solidity source code. In contrast to existing work, Annotary supports analysis of inter-transactional, inter-contract control flows and combines symbolic execution of EVM bytecode with a resolution of concrete values from the public Ethereum blockchain. While the analysis of Annotary tends to weight precision higher than soundness, we analyze inter-transactional call chains to eliminate false positives from unreachable states that traditional symbolic execution would not be able to handle. We present the annotation and analysis concepts of Annotary, explain its implementation on top of the Laser symbolic virtual machine, and demonstrate its usage as a plugin for the Sublime Text editor.

Leyi Shi, Yang Li, Tianxu Liu, Jia Liu · 6 authors

Honeypot technology can be applied to efficiently attract attackers and exhaust their resources. However, the traditional static honeypot is easy to be recognized by anti-honeypot technology. By contrast, most of the dynamic honeypots can simulate the real system in time, thus interacting with an intruder in disguise. In this paper, we employ the dynamic property of honeypot in four kinds of services of our system. However, this dynamic property shows up in a location and identification, indicating that genuine or fake services (honeypots) are changeable in different hosts. Thus, the dynamic property of our system differs from the dynamic honeypot aforementioned. Besides, we adopt the blockchain platform (Ethereum) to decentralize our system and store the port access data by delivering a private chain. To illustrate the effectiveness of our scheme in theory and practice, security analysis, eavesdropping attack, scanning attack, and DoS attack experiments are conducted. The results show that our scheme is valid in safeguarding against network attack.

Rongxin Zheng, Cuiwen Ying, Jun Shao, Guiyi Wei · 9 authors

No abstract is available for this record.

Ashar Ahmad

In enterprise business applications, large volumes of data are generated daily, encoding business logic and transactions. Those applications are governed by various compliance requirements, making it essential to provide audit logs to store, track, and attribute data changes. In traditional audit log systems, logs are collected and stored in a centralized medium, making them prone to various forms of attacks and manipulations, including physical access and remote vulnerability exploitation attacks, and eventually allowing for unauthorized data modification, threatening the guarantees of audit logs. Moreover, such systems, and given their centralized nature, are characterized by a single point of failure. To harden the security of audit logs in enterprise business applications, in this work we explore the design space of blockchain-driven secure and transparent audit logs. We highlight the possibility of ensuring stronger security and functional properties by a generic blockchain system for audit logs, realize this generic design through BlockAudit, which addresses both security and functional requirements, optimize BlockAudit through multi-layered design in BlockTrail, and explore the design space further by assessing the functional and security properties the consensus algorithms through comprehensive evaluations. The first component of this work is BlockAudit, a design blueprint that enumerates structural, functional, and security requirements for blockchain-based audit logs. BlockAudit uses a consensus-driven approach to replicate audit logs across multiple application peers to prevent the single-point-of-failure. BlockAudit also uses the Practical Byzantine Fault Tolerance (PBFT) protocol to achieve consensus over the state of the audit log data. We evaluate the performance of BlockAudit using event-driven simulations, abstracted from IBM Hyperledger. Through the performance evaluation of BlockAudit, we pinpoint a need for high scalability and high throughput. We achieve those requirements by exploring various design optimizations to the flat structure of BlockAudit inspired by real-world application characteristics. Namely, enterprise business applications often operate across non-overlapping geographical hierarchies including cities, counties, states, and federations. Leveraging that, we applied a similar transformation to BlockAudit to fragment the flat blockchain system into layers of codependent hierarchies, capable of processing transactions in parallel. Our hierarchical design, called BlockTrail, reduced the storage and search complexity for blockchains substantially while increasing the throughput and scalability of the audit log system. We prototyped BlockTrail on a custom-built blockchain simulator and analyzed its performance under varying transactions and network sizes demonstrating its advantages over BlockAudit. A recurring limitation in both BlockAudit and BlockTrail is the use of the PBFT consensus protocol, which has high complexity and low scalability features. Moreover, the performance of our proposed designs was only evaluated in computer simulations, which sidestepped the complexities of the real-world blockchain system. To address those shortcomings, we created a generic cloud-based blockchain testbed capable of executing five well-known consensus algorithms including Proof-of-Work, Proof-of-Stake, Proof-of-Elapsed Time, Clique, and PBFT. For each consensus protocol, we instrumented our auditing system with various benchmarks to measure the latency, throughput, and scalability, highlighting the trade-off between the different protocols.

Pierre Reibel, Haaroon Yousaf, Sarah Meiklejohn

No abstract is available for this record.

Yun-Tae Kim, Dohyun Pak, JongHyup Lee

Smart contracts on blockchain systems implement business logic and directly handle important assets. Although smart contracts play these critical roles, it is hard for users interacting with the system to understand the real behavior of the deployed bytecodes of smart contracts. The quirks of smart contracts, such as code reuse and limited unique datasets, make it challenging to recognize the functional details of smart contracts. In this paper, we propose a new method for characterizing bytecode-only smart contracts by automatically assigning multiple attribute tags. Using a deep learning approach, our system, the ScanAT, extracts attribute tags from the source code and metadata of known smart contracts and trains their bytecode with the attribute tags. The ScanAT then infers attribute tags from the bytecode of smart contracts alone. Our experiments show that ScanAT can achieve 81% accuracy in predicting attribute tags, using convolutional neural networks and a customized autoencoder.

Bruno Bogaz Zarpel�ão, Rodrigo Sanches Miani, Muttukrishnan Rajarajan

No abstract is available for this record.

G Hall, M Mansi, I. Makrant

Block-chain world is very dynamic and there is need for strong governance and underlying technology architecture to be robust to face challenges. This paper considers Ethereum, a leading block chain. We deep dive into the nature of this block chain, wherein for software upgrades forks are performed. They types of forks and impact is discussed. A specific Ethereum hack led to a hard fork and focus is provided on understanding the hack and overcoming it from a novel approach. The current model has been unable to handle multiple Ethereum attacks. Thus the current approach is compared against a novel approach providing a security and scaling solution. Here the architecture draws upon combining block-chain layers into operating system level. The approach can have tremendous benefits to block chain world and improve the way decentralized application teams perform. The benefits of the novel architecture is discussed. The approach helps safe guard block chain projects, making them safer and chain agnostic.

Pavel V. Sukharev, Dmitry S. Silnov

Cryptocurrency mining is a process of calculation of some hard-computational task. Ethereum cryptocurrency mining sets a task of determining a special block header value which is corresponds to requirements of cryptocurrency system. While doing computational jobs there are software computational errors which will lead to wrong mining task solution. To ensure mining correctness, the mining results should be validated. This paper describes reliable mining result validation methodology, which is not affecting mining performance.

Stephan Zöllner, Kim‐Kwang Raymond Choo, Nhien‐An Le‐Khac

Bitcoin is popular not only with consumers, but also with cybercriminals (e.g., in ransomware and online extortion, and commercial online child exploitation). Given the potential of Bitcoin to be involved in a criminal investigation, the need to have an up-to-date and in-depth understanding on the forensic acquisition and analysis of Bitcoins is crucial. However, there has been limited forensic research of Bitcoin in the literature. The general focus of existing research is on postmortem analysis of specific locations (e.g. wallets on mobile devices), rather than a forensic approach that combines live data forensics and postmortem analysis to facilitate the identification, acquisition, and analysis of forensic traces relating to the use of Bitcoins on a system. Hence, the latter is the focus of this paper where we present an open source tool for live forensic and postmortem analysing automatically. Using this open source tool, we describe a list of target artifacts that can be obtained from a forensic investigation of popular Bitcoin clients and Web Wallets on different web browsers installed on Windows 7 and Windows 10 platforms.

Alexander Hefele, Ulrich Gallersdörfer, Florian Matthes

No abstract is available for this record.

Quoc-Bao Nguyen, Anh-Quynh Nguyen, Van Hoa Nguyen, Thanh Nguyen-Le · 5 authors

No abstract is available for this record.

Pinchen Cui, Julie Dixon, Ujjwal Guin, Daniel DiMase

The complexity of the electronics supply chain has grown significantly due to the expansion of globalization in the 21st century. Electronic parts are now manufactured, distributed, and sold globally. Ensuring the security and integrity of the supply chain has become extremely challenging due to the widespread infiltration of untrusted hardware, specifically, counterfeit and cloned parts. Especially, the provenance of microelectronics and commercial off-the-shelf (COTS) parts becomes prohibitively difficult to track and calls for immediate solutions. In this paper, we present a non-destructive way of ensuring the traceability of electronic parts in the supply chain. We have implemented a blockchain-based framework, which helps to track and trace every chip while they are circulating in the supply chain. The proposed framework is built upon a permissioned blockchain. Hyperledger is used for implementing this framework. A detailed analysis is carried out to present the feasibility of our proposed approach.

Anastasia Mavridou, Áron Lászka, Emmanouela Stachtiari, Abhishek Dubey

The adoption of blockchain based distributed ledgers is growing fast due to their ability to provide reliability, integrity, and auditability without trusted entities. One of the key capabilities of these emerging platforms is the ability to create self-enforcing smart contracts. However, the development of smart contracts has proven to be error-prone in practice, and as a result, contracts deployed on public platforms are often riddled with security vulnerabilities. This issue is exacerbated by the design of these platforms, which forbids updating contract code and rolling back malicious transactions. In light of this, it is crucial to ensure that a smart contract is secure before deploying it and trusting it with significant amounts of cryptocurrency. To this end, we introduce the VeriSolid framework for the formal verification of contracts that are specified using a transition-system based model with rigorous operational semantics. Our model-based approach allows developers to reason about and verify contract behavior at a high level of abstraction. VeriSolid allows the generation of Solidity code from the verified models, which enables the correct-by-design development of smart contracts.