Symbolic detection has been widely used to detect vulnerabilities in smart contracts. Unfortunately, as reported, existing symbolic tools cost too much time, since they need to execute all paths to detect vulnerabilities. Thus, their accuracy is limited by time. To tackle this problem, in this paper, we propose Park, the first general framework of parallel-fork symbolic execution for smart contracts. The main idea is to use multiple processes during symbolic execution, leveraging multiple CPU cores to enhance efficiency. Firstly, we propose a fork-operation based dynamic forking algorithm to achieve parallel symbolic contract execution. Secondly, to address the SMT performance loss problem in parallelization, we propose an adaptive processes restriction and adjustment algorithm. Thirdly, we design a shared-memory based global variable reconstruction method to collect and rebuild the global variables from different processes. We implement Park as a plug-in and apply it to two popular symbolic execution tools for smart contracts: Oyente and Mythril. The experimental results with third-party datasets show that Park-Oyente and Park-Mythril can provide up to 6.84x and 7.06x speedup compared to original tools, respectively.
WebAssembly (Wasm) smart contracts have shown growing popularity across blockchains (e.g., EOSIO) recently. Similar to Ethereum smart contracts, Wasm smart contracts suffer from various attacks exploiting their vulnerabilities. Even worse, few developers released the source code of their Wasm smart contracts for security review, raising the bar for uncovering vulnerable contracts. Although a few approaches have been proposed to detect vulnerable Wasm smart contracts, they have several major limitations, e.g., low code coverage, low accuracy and lack of scalability, unable to produce exploit payloads, etc. To fill the gap, in this paper, we design and develop WASAI, a new concolic fuzzer for uncovering vulnerabilities in Wasm smart contract after tackling several challenging issues. We conduct extensive experiments to evaluate WASAI, and the results show that it outperforms the state-of-the-art methods. For example, it achieves 2x code coverage than the baselines and surpasses them in detection accuracy, with an F1-measure of 99.2%. Moreover, WASAI can handle complicated contracts (e.g., contracts with obfuscation and sophisticated verification). Applying WASAI to 991 deployed smart contracts in the wild, we find that over 70% of smart contracts are vulnerable. By the time of this study, over 300 vulnerable contracts have not been patched and are still operating on the EOSIO Mainnet. One fake EOS vulnerability reported to the EOSIO ecosystem was recently assigned a CVE identifier (CVE-2022-27134).
The execution of smart contracts on the Ethereum blockchain consumes gas paid for by users submitting contracts' invocation requests. A contract execution proceeds as long as the users dedicate enough gas, within the limit set by Ethereum. If insufficient gas is provided, the contract execution halts and changes made during execution get reverted. Unfortunately, contracts may contain code patterns that increase execution cost, causing the contracts to run out of gas. These patterns can be manipulated by malicious attackers to induce unwanted behavior in the targeted victim contracts, e.g., Denial-of-Service (DoS) attacks. We call these gas-related vulnerabilities. We propose eTainter, a static analyzer for detecting gas-related vulnerabilities based on taint tracking in the bytecode of smart contracts. We evaluate eTainter by comparing it with the prior work, MadMax, on a dataset of annotated contracts. The results show that eTainter outperforms MadMax in both precision and recall, and that eTainter has a precision of 90% based on manual inspection. We also use eTainter to perform large-scale analysis of 60,612 real-world contracts on the Ethereum blockchain. We find that gas-related vulnerabilities exist in 2,763 of these contracts, and that eTainter analyzes a contract in eight seconds, on average.
K Dinesh Kumar, Venkata Rathnam T., Venkata Ramana R., Malla Sudhakara · 5 authors
Internet of things (IoT) technology plays a vital role in the current technologies because IoT develops a network by integrating different kinds of objects and sensors to create the communication among objects directly without human interaction. With the presence of internet of things technology in our daily comes smart thinking and various advantages. At the same time, secure systems have been a most important concern for the protection of information systems and networks. However, adopting traditional security management systems in the internet of things leads several issues due to the limited privacy and policies like privacy standards, protocol stacks, and authentication rules. Usually, IoT devices has limited network capacities, storage, and computing processors. So they are having more chances to attacks. Data security, privacy, and reliability are three main challenges in the IoT security domain. To address the solutions for the above issues, IoT technology has to provide advanced privacy and policies in this large incoming data source. Blockchain is one of the trending technologies in the privacy management to provide the security. So this chapter is focused on the blockchain technologies which can be able to solve several IoT security issues. This review mainly focused on the state-of-the-art IoT security issues and vulnerabilities by existing review works in the IoT security domains. The taxonomy is presented about security issues in the view of communication, architecture, and applications. Also presented are the challenges of IoT security management systems. The main aim of this chapter is to describe the importance of blockchain technology in IoT security systems. Finally, it highlights the future directions of blockchain technology roles in IoT systems, which can be helpful for further improvements.
Xintong Ling, Pengcheng Chen, Yuwei Le, Jiaheng Wang · 5 authors
Clients, service providers (SPs), and manufacturers lay the foundation for 6G networking, and thus the direct relationships between them are abstracted as the trust triangle of telecommunications. As one component among three, manufacturers are facing considerable criticism from SPs and clients regarding cybersecurity and privacy, which compromises the whole trust triangle. Therefore, we aim to develop a block-chain-based framework to rebuild and maintain the trust triangle in this study. The introduction of blockchain sheds light on building a decentralized digital forensics model for the trust triangle. After analyzing the potential threats and vulnerabilities, we first design the Blockchain-based Network Hawkeye Function (BNHF) to provide an open and privacy-preserving way for clients watching SPs, SPs monitoring manufacturers, and manufacturers proving themselves trustworthy. We offer a practical BNHF solution compatible with 5G and envision its evolution for 6G. Furthermore, we propose two BNHF-aided mechanisms in clients' interests to help construct a complete trust triangle. Comprehensive test results under experimental networking setups illustrate the effectiveness of the proposed framework.
Anamika Singh, Md. Akkas Ali, B. Balamurugan, Vandana Sharma
Ransomware is a malware practice which cyber criminals usually inject through phishing practices to make money as their priority. With the high rise in the use of the internet amid the COVID phase, the cyber world is also reaching its peak as well. The attacker is also getting smarter with technology. We in this paper are taking blockchain technology as a weapon to fight against the terror of ransomware attacks, as this is the most notorious and devastating attack of all, and there is no doubt that it is going to be with us in the future too. Detection of ransomware attacks before they infect and decode the data is a complex thing. Several algorithms exist to detect attacks at their early stages, but the lack of information about the pre and post behavior similarities is proving to be an obstacle to accurately observing and detecting ransomware at its very first stage rather than making sense of paying the ransom and even not being sure of getting back the confidentialities. With the use of blockchain technology, we focus on making a record of the pre-encryption and post-encryption behaviour of ransomware attacks so that it is not going to be complex to track the nature, similarities, and behaviour of ransomware attacks. With inaccurate availability of data about pre and post behaviour of attacks and also weak design of detection models, both have a negative impact on selecting the features and similarities of the attack and thus developing a design model for the same. The paper is focusing on one of the most challenging variations of ransomware attacks, i.e., the crypto ransomware. Many researches before proposed solutions as performing regular backup of files but this measure has a significant overhead too as key backup schemes results in high computational cost as well. Thus, with the use of blockchain for gathering and maintaining records, we expect to be prepared for every unexpected attack.
Song Huang, Zhen Yang, Changyou Zheng, Yan Wang · 7 authors
In the process of crowdsourced testing service, the intellectual property of crowdsourced testing has been faced with problems such as code plagiarism, difficulties in confirming rights and unreliability of data. Blockchain is a decentralized, tamper-proof distributed ledger, which can help solve current problems. This paper proposes an intellectual property right confirmation system oriented to crowdsourced testing services, combined with blockchain, IPFS (Interplanetary file system), digital signature, code similarity detection to realize the confirmation of crowdsourced testing intellectual property. The performance test shows that the system can meet the requirements of normal crowdsourcing business as well as high concurrency situations.
In recent years, with the advent of the blockchain 2.0 era, the security problems of smart contracts have gradually emerged. The detection of contract vulnerabilities is currently a research hotspot in blockchain security [1]. Current research methods are mainly based on traditional software defect analysis methods for vulnerabilities detection, but the detection accuracy and false alarm rate are not satisfactory. In this paper, we propose a bidirectional long short-term memory neural network model (HAM-BiLSTM for short) with hierarchical attention mechanism, which takes the code segment and account information of a smart contract as input. It divides the input samples into three levels as documents: word level, sentence level and document level, and introduces attention mechanism in different levels. The aim is to detect reentrancy vulnerability more accurately and reduce the false alarm rate of the model as much as possible. The neural network model classifies smart contracts through softmax layers by learning feature information from training samples to determine the presence of reentrancy vulnerability. The experiments demonstrate that our proposed solution and model increase detection accuracy and reduce false alarm rate.
With the evolution of technology, blockchain a swiftly impending phenomenon i.e., "decentralized computing” is observed. The emergence of Smart Contracts (SC) has resulted in advancements in the application of blockchain technology. The Ethereum network’s computing capabilities and functionalities are founded on the basis of SC. A smart contract is a self-executing agreement between buyer and seller with the terms of the settlement between them, written directly as lines of code, existing across a distributed decentralized blockchain network. It is a decentralized software that runs on a blockchain autonomously, consistently, and publicly. Conversely, due to the complex semantics of fundamental domain-specific languages and their testability, constructing reliable and secure SC can be extremely difficult. SC might contain some vulnerabilities. Security vulnerabilities can originate from financial tribulations; there are a number of notorious events that specify blockchain SC could comprise numerous code-security vulnerabilities. Security and privacy of blockchain-based SC are very important, we must first identify their vulnerabilities before implementing them widely. Therefore, the purpose of this paper is to conduct a comprehensive experimental evaluation of two current security testing tools: Remix solidity static analysis plugin and Solium which are used for static analysis of SC. We have conducted an empirical analysis of SC for finding tangible and factual evidence, controlled by the scientific approach. The methodology’s first step is to gather all of the Ethereum SC and store them in a repository. The next step is to use the Remix solidity static analysis plugin and Solium to perform vulnerability assessments. The last step is to analyze the result of both tools and evaluate them on the basis of accuracy and effectiveness. The goal of this empirical analysis is to evaluate the two FOSS tools: Remix solidity static analysis plugin and Solium on the basis of accuracy and effectiveness. Some research questions were considered to reach the stated goal: What automated tools and frameworks are proposed in supporting the state-of-the-art empirical approach to SC vulnerability detection? How accurate are security analysis tools? And which tool has more accuracy rate? How effectively security analysis tools are detecting vulnerabilities in SC? And which is the most effective security analysis tool? We investigated the effectiveness and accuracy of security code analysis tools on Ethereum by testing them on a random sample of vulnerable contracts. The results indicate that the tools have significant discrepancies when it comes to certain security characteristics. In terms of effectiveness and accuracy, the Remix plugin outperformed and beat the other tool.
Abstract Data security and confidentiality are major goals now days due to the extensive use of the internet for data sharing. In modern era, most of the networks are compromised by intruders to grab access to private, confidential, and highly secured data. An intrusion detection system (IDS) is widely used to secure the network from getting compromised by intruders. Most of the IDS share the signatures of the novel attacks detected by anomaly approach for improving the detection rate and processing time. Security of signature shared by nodes is becoming a considerable problem. This paper presents a novel framework blockchain based hybrid intrusion detection system (BC-HyIDS), which uses the blockchain framework for exchanging signatures from one node to the other in distributed IDS. BC-HyIDS works in three phases where it uses both detection methods and blockchain in the third phase to provide security to data transferred through the network. This system makes use of a cryptosystem to encrypt the data stored in blocks to improve security one level higher. Hyperledger fabric v2.0 and Hyperledger sawtooth is used to implement system. Blockchain framework is created as a prototype using distributed ledger technology which helps in securing signature exchange. Performance of BC-HyIDS is evaluated in terms of accuracy, detection rate, and false alarm rate. From results, it is observed that a 2.8% increase in accuracy, 4.3% increase in detection rate, and a reduction of 2.6% in FAR is achieved. Blockchain performance is evaluated using Hyperledger fabric v2.0 and Hyperledger sawtooth on throughput, processing time, and average latency. BC-HyIDS shows improved performance when used with blockchain.
Smart manufacturing systems grow based on multiple demands to predict equipment reliability and quality. To this end, many machine learning techniques are examined. Data security and management are other issues that are important for the industry. The integrated blockchain and cyber-physical systems have been used to protect system transactions from hacking the data during transmission to overcome the above problems. In the private blockchain platform, the blockchain system was implemented. The quality control system was evaluated based on non-linear techniques that are complex and demonstrate the truly positive quality control rate of this system. Similarly, the prediction aspect of fault diagnosis was assessed based on hybrid prediction techniques.
With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people's lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.
In recent years, mobile edge computing (MEC) has become a research hotspot in academia. The Internet of Things (IoT) is an excellent way to build the infrastructure required for a MEC environment. Its rich digital tracking repository can provide insights into people's daily activities at home and elsewhere. Meanwhile, due to the open connectivity of the Internet of things devices, they can easily become the target of network attacks and be used by criminals as criminal tools. As a result, civil and criminal cases have increased year by year. This article conducts in-depth research on IoT forensics. By comparing its difference with traditional digital forensics (DF), the definition of IoT forensics is given. We have systematically sorted out the research results since the concept of IoT forensics was proposed in 2013 and proposed a generalized IoT forensics model. By studying blockchain technology and introducing it into the IoT forensics framework, a blockchain-based IoT forensics architecture is further proposed. Further, an alliance chain IoT forensics system is proposed. From the perspective of the data provider and the data visitor, the process of evidence storage and forensics of the IoT system is discussed. Finally, taking Unmanned Aerial Vehicle (UAV) forensics as an example, we give an experiment of IoT forensics analysis.
Hyperledger Fabric is another development of blockchain technology after Ethereum, which is more suitable as an operating platform for smart contracts. However, the testing technology of Hyperledger Fabric smart contracts (also known as chaincode) is not yet mature currently. Based on this, this paper studies the vulnerability detection of Golang chaincodes. Firstly, we summarize 17 kinds of Golang chaincode vulnerabilities by investigating existing research. Secondly, taking the high accuracy of dynamic detection and the high efficiency of static detection into consideration, we propose a chaincode vulnerability detection framework that combines the dynamic symbolic execution and the static abstract syntax tree analysis technology. We also implement a supporting-tool that can detect the above 15 types of vulnerabilities. Finally, we test the tool by 15 chaincodes collected from GitHub and unknown vulnerabilities were detected in 13 projects. The precision turned out to be 91% after manual inspection. In order to verify the recall rate, we manually inject 30 vulnerabilities into the collected chaincodes and all of them are detected. The evaluation results show the accuracy of the proposed vulnerability detection method for Hyperledger Fabric smart contracts.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Ensuring determinism is mandatory when writing blockchain software. When determinism is not met it can lead to serious implications in the blockchain network while compromising the software development, release, and patching processes. In the industrial context, it is widespread to adopt general-purpose languages, such as Go, for developing blockchain solutions. However, it is not surprising that non-deterministic behaviors may arise, being these programming languages not originally designed for blockchain purposes. In this paper, we present an experience report on ensuring determinism in blockchain software with GoLiSA, a static analyzer based on abstract interpretation for Go applications, in an industrial context. In particular, we ran GoLiSA on Commercio.network, a blockchain-based solution for exchanging electronic documents in a legally binding way. Thanks to GoLiSA, non-trivial bugs got detected and the analysis performed made it possible to identify the critical points where to apply the fixes.
Ben Weintraub, Christof Ferreira Torres, Cristina Nita-Rotaru, Radu State
The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, where savvy participants game transaction orderings within a block for profit. One popular solution to address such behavior is Flashbots, a private pool with infrastructure and design goals aimed at eliminating the negative externalities associated with MEV. While Flashbots has established laudable goals to address MEV behavior, no evidence has been provided to show that these goals are achieved in practice. In this paper, we measure the popularity of Flashbots and evaluate if it is meeting its chartered goals. We find that (1) Flashbots miners account for over 99.9% of the hashing power in the Ethereum network, (2) powerful miners are making more than $2\times$ what they were making prior to using Flashbots, while non-miners' slice of the pie has shrunk commensurately, (3) mining is just as centralized as it was prior to Flashbots with more than 90% of Flashbots blocks coming from just two miners, and (4) while more than 80% of MEV extraction in Ethereum is happening through Flashbots, 13.2% is coming from other private pools.
In this study, firstly, a dataset of 10,476 annotated vulnerable ERC-20 standard token smart contracts (belonging to a set of 33 common smart contract vulnerabilities) has been collected from a publicly available repository. Secondly, using the SolMet smart contract metrics measurement tool, the object-oriented software attributes (i.e., metrics) from each smart contract's source code has been extracted. Lastly, using the source code metrics and the vulnerability annotations (i.e., labels) as the input in supervised machine learning (classification) algorithms, the accuracy of each individual algorithm is evaluated against the accuracy of an ensemble classifier (namely voting). The model accuracies demonstrate the feasibility of identifying and prioritising smart contracts for further inspection prior to deployment to the blockchain network. The ensemble classifier performed better (accuracy = 0.79) compared to each classifier when used individually.
Ethereum smart contracts may incur security vulnerabilities. Fuzzing is an industry-standard practice to detect them in improving the dependability of programs. Existing fuzz testing techniques for Ethereum smart contracts are insensitive to whether consecutive seeds of the same function are used for fuzzing the smart contract under test. Nonetheless, smart contracts are often designed to have collaborations among different functions for business activity to complete. We wonder whether this mismatch will make fuzzing techniques less effective than they should be. In this paper, to the best of our knowledge, we present the first work to show that security vulnerability detection can be significantly more effective in smart contract fuzzing if the entry functions of recent past test cases can be distinct. The empirical results show that the performance boost can be as large as 10.4% by simply enabling any test case not invoking the same entry functions as a few recent past test cases. The empirical result also shows that the cost-effectiveness also increases by up to 21.9%.
EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.
Ethereum, a blockchain-based platform, provides a suitable environment for coding and executing smart contracts among trustless parties. However, smart contracts are exposed to inherent security risks due to manipulating digital units of value on a public decentralized blockchain. Even worse, vulnerabilities can hardly be fixed as the contracts are stored on the blockchain. Hence, there is a significant need to write secure and well-performing contracts and ensure their high reliability. To overcome these challenges, we propose an automated static framework SolChecker, for vulnerability detection on Solidity smart contracts. It works by analyzing information extracted from constructed abstract syntax tree of Solidity smart contracts, and developers can use the analyzed results to design detectors for specific vulnerabilities according to their needs. In this paper, we present an overview of SolChecker, provide the detailed design of each component, and evaluate the capabilities of the designed detectors on 47,037 real-world contracts. Furthermore, we clarify the effect of each extracted information by performing a comparative experiment on 500 contracts sampled from the dataset. Our tool reflects the current state of knowledge on Solidity vulnerabilities.
Saroj Gopali, Zulfiqar Ali Khan, Bipin Chhetri, Bimal Karki · 5 authors
Various decentralized applications have deployed millions of smart contracts (SCs) on the Blockchain networks. SCs enable programmable transactions involving the transfer of monetary assets between peers on a Blockchain network without any need to a central authority. However, similar to any software program, SCs may contain security issues. Software se-curity engineers and researchers have already uncovered several Ethereum BlockChain and SC vulnerabilities. Still, researchers continuously discover many more security flaws in deployed SCs. Indeed, the popularity of SCs attracts adversaries to launch new attack vectors. Thus, efficient vulnerability detection is necessary. This paper lists broad known vulnerabilities in SCs and classifies them based on the multi-class categories such as Suicidal, Prodigal, Greedy, and Normal SCs. The paper adopts artificial recurrent neural network architecture such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) used in deep learning to identify and then classify vulnerable Scs.
A smart contract that is deployed to a blockchain system like Ethereum is, under reasonable circumstances, expected to be immutable and tamper-proof. This is both a feature (promoting integrity and transparency) and a bug (preventing security patches and feature updates). Modern smart contracts use software tricks to enable upgradeability, raising the research questions of how upgradeability is achieved and who is authorized to make changes. In this paper, we summarize and evaluate six upgradeability patterns. We develop a measurement framework for finding how many upgradeable contracts are on Ethereum that use certain prominent upgrade patters. We find 1.4 million proxy contracts which 8,225 of them are unique upgradeable proxy contracts. We also measure how they implement access control over their upgradeability: about 50% are controlled by a single Externally Owned Address (EOA), and about 14% are controlled by multi-signature wallets in which a limited number of persons can change the whole logic of the contract.