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.
In the context of combating crime, government institutions in several countries have instituted units specialized in investigation and intelligence activities to act in different areas and expertise. However, due to the considerable complexity and specificity of these activities, as well as a significant concern with security and other related aspects, there are challenges regarding the location and adoption of approaches aimed at applying process automation in the context of these units. Motivated by this scenario, this work presents an approach that adopts concepts of process automation in order to assist researchers and professionals interested in studies and practices aimed at simplification and/or automation of processes in the context of intelligence and investigation units. Exploring the main characteristics of blockchain technology, this paper also presents an overview of different application trends of blockchain technology and proposes the use of this technology as a support mechanism in the management, storage, and sharing of generated digital assets. On the other hand, to analyze the feasibility of applying the approach, a survey was carried out with specialists from specialized units and a real case scenario experience of use was performed. Results show evidence of the feasibility of use and suitability of the approach for the given context, and that it helps interested parties regarding the application of process automation in the scenario of intelligence and investigation units.
Open access
Blockchain Technology Applications and Security
Advanced Malware Detection Techniques
Physical Unclonable Functions (PUFs) and Hardware Security
Christof Ferreira Torres, Antonio Ken Iannillo, Arthur Gervais, Radu State
Smart contracts are Turing-complete programs that are executed across a blockchain network. Unlike traditional programs, once deployed they cannot be modified. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In recent years, smart contracts suffered major exploits, costing millions of dollars, due to programming errors. As a result, a variety of tools for detecting bugs has been proposed. However, majority of these tools often yield many false positives due to over-approximation or poor code coverage due to complex path constraints. Fuzzing or fuzz testing is a popular and effective software testing technique. However, traditional fuzzers tend to be more effective towards finding shallow bugs and less effective in finding bugs that lie deeper in the execution. In this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary fuzzing with constraint solving in order to execute more code and find more bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts of a smart contract, while constraint solving is used to generate inputs which satisfy complex conditions that prevent the evolutionary fuzzing from exploring deeper paths. Moreover, we use data dependency analysis to efficiently generate sequences of transactions, that create specific contract states in which bugs may be hidden. We evaluate the effectiveness of our fuzzing strategy, by comparing CONFUZZIUS with state-of-the-art symbolic execution tools and fuzzers. Our evaluation shows that our hybrid fuzzing approach produces significantly better results than state-of-the-art symbolic execution tools and fuzzers.
Christof Ferreira Torres, Antonio Ken Iannillo, Arthur Gervais, Radu State
<div> <div> <p>Smart contracts are Turing-complete programs that are executed across a blockchain. Unlike traditional programs, once deployed, they cannot be modified. As smart contracts carry more value, they become more of an exciting target for attackers. Over the last years, they suffered from exploits costing millions of dollars due to simple programming mistakes. As a result, a variety of tools for detecting bugs have been proposed. Most of these tools rely on symbolic execution, which may yield false positives due to over-approximation. Recently, many fuzzers have been proposed to detect bugs in smart contracts. However, these tend to be more effective in finding shallow bugs and less effective in finding bugs that lie deep in the execution, therefore achieving low code coverage and many false negatives. An alternative that has proven to achieve good results in traditional programs is hybrid fuzzing, a combination of symbolic execution and fuzzing. In this work, we study hybrid fuzzing on smart contracts and present ConFuzzius, the first hybrid fuzzer for smart contracts. ConFuzzius uses evolutionary fuzzing to exercise shallow parts of a smart contract and constraint solving to generate inputs that satisfy complex conditions that prevent evolutionary fuzzing from exploring deeper parts. Moreover, ConFuzzius leverages dynamic data dependency analysis to efficiently generate sequences of transactions that are more likely to result in contract states in which bugs may be hidden. We evaluate the effectiveness of ConFuzzius by comparing it with state-of-the-art symbolic execution tools and fuzzers for smart contracts. Our evaluation on a curated dataset of 128 contracts and a dataset of 21K real-world contracts shows that our hybrid approach detects more bugs than state-of-the-art tools (up to 23%) and that it outperforms existing tools in terms of code coverage (up to 69%). We also demonstrate that data dependency analysis can boost bug detection up to 18%.</p> </div> </div>
Security attacks targeting smart contracts have been on the rise, which have led to financial loss and erosion of trust. Therefore, it is important to enable developers to discover security vulnerabilities in smart contracts before deployment. A number of static analysis tools have been developed for finding security bugs in smart contracts. However, despite the numerous bug-finding tools, there is no systematic approach to evaluate the proposed tools and gauge their effectiveness. This paper proposes SolidiFI, an automated and systematic approach for evaluating smart contracts’ static analysis tools. SolidiFI is based on injecting bugs (i.e., code defects) into all potential locations in a smart contract to introduce targeted security vulnerabilities. SolidiFI then checks the generated buggy contract using the static analysis tools, and identifies the bugs that the tools are unable to detect (false-negatives) along with identifying the bugs reported as false-positives. SolidiFI is used to evaluate six widely-used static analysis tools, namely, Oyente, Securify, Mythril, SmartCheck, Manticore and Slither, using a set of 50 contracts injected by 9369 distinct bugs. It finds several instances of bugs that are not detected by the evaluated tools despite their claims of being able to detect such bugs, and all the tools report many false positives.
Lei Wu, Siwei Wu, Yajin Zhou, Runhuai Li · 8 authors
As one of the representative blockchain platforms, Ethereum has attracted lots of attacks. Due to the existed financial loss, there is a pressing need to perform timely investigation and detect more attack instances. Though multiple systems have been proposed, they suffer from the scalability issue due to the following reasons. First, the tight coupling between malicious contract detection and blockchain data importing makes them infeasible to repeatedly detect different attacks. Second, the coarse-grained archive data makes them inefficient to replay transactions. Third, the separation between malicious contract detection and runtime state recovery consumes lots of storage. In this paper, we present the design of a scalable attack detection framework on Ethereum. It overcomes the scalability issue by saving the Ethereum state into a database and providing an efficient way to locate suspicious transactions. The saved state is fine-grained to support the replay of arbitrary transactions. The state is well-designed to avoid saving unnecessary state to optimize the storage consumption. We implement a prototype named EthScope and solve three technical challenges, i.e., incomplete Ethereum state, scalability, and extensibility. The performance evaluation shows that our system can solve the scalability issue, i.e., efficiently performing a large-scale analysis on billions of transactions, and a speedup of around 2,300x when replaying transactions. It also has lower storage consumption compared with existing systems. The result with three different types of information as inputs shows that our system can help an analyst understand attack behaviors and further detect more attacks. To engage the community, we will release our system and the dataset of detected attacks.
Lei Wu, Siwei Wu, Yajin Zhou, Runhuai Li · 8 authors
As one of the representative blockchain platforms, Ethereum has attracted
lots of attacks. Due to the potential financial loss, there is a pressing need
to detect malicious smart contracts and understand their behaviors. Though
there exist multiple systems for smart contract analysis, they cannot
efficiently analyze a large number of transactions and re-execute smart
contracts to introspect malicious behaviors. In this paper, we urge for a transaction-centric security analytics framework
for Ethereum, which provides an efficient way to quickly locate suspicious ones
from a large number of transactions and extensible way to detect malicious
smart contracts with analyst-provided scripts. We present the system design in
the paper, which solves three technical challenges, i.e., incomplete states,
scalability and extensibility. We have implemented a prototype system named
EthScope to solve these challenges. In particular, the first component Data
Aggregator collects and recovers critical blockchain states. The second
component Replay Engine is able to {replay} arbitrary and a large number of
transactions. The third component Instrumentation Framework exposes interfaces
for an analyst to dynamically instrument smart contracts and introspect the
execution of suspicious transactions. The comprehensive evaluation with six
types of attacks demonstrated the effectiveness of our system. The performance
evaluation shows that our system can perform a large-scale analysis on
suspicious transactions (more than 8 million ones) and has a speed up of around
2,300x compared with the JSTracer provided by Go-Ethereum. To engage the
community, we will release our system and a dataset of detected attacks on
https://github.com/zjuicsr/ethscope.
The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts ( SCs ) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and summarize the key reasons. Their feedback shows that 66.67% of the developers will deploy an updated contract to the Ethereum after destructing the old contract. According to this information, we propose a method to find the self-destructed contracts (also called predecessor contracts) and their updated version (successor contracts) by computing the code similarity. By analyzing the difference between the predecessor contracts and their successor contracts, we found five reasons that led to the death of the contracts; two of them (i.e., Unmatched ERC20 Token and Limits of Permission ) might affect the life span of contracts. We developed a tool named LifeScope to detect these problems. LifeScope reports 0 false positives or negatives in detecting Unmatched ERC20 Token . In terms of Limits of Permission , LifeScope achieves 77.89% of F-measure and 0.8673 of AUC in average. According to the feedback of developers who exclude selfdestruct functions, we propose suggestions to help developers use selfdestruct functions in Ethereum smart contracts better.
Abstract A smart contract (SC) is a programme stored in the Ethereum blockchain by a contract‐creation transaction. SC developers deploy an instance of the SC and attempt to execute it in exchange for a fee, paid in Ethereum coins (Ether). If the computation needed for their execution turns out to be larger than the effort proposed by the developer (i.e., the gasLimit ), their client instantiation will not be completed successfully. In this paper, we examine SCs from 11 Ethereum blockchain‐oriented software projects hosted on GitHub.com , and we evaluate the resources needed for their deployment (i.e., the gasUsed ). For each of these contracts, we also extract a suite of object‐oriented metrics, to evaluate their structural characteristics. Our results show a statistically significant correlation between some of the object‐oriented (OO) metrics and the resources consumed on the Ethereum blockchain network when deploying SCs. This result has a direct impact on how Ethereum developers engage with a SC: evaluating its structural characteristics, they will be able to produce a better estimate of the resources needed to deploy it. Other results show specific source code metrics to be prioritised based on application domains when the projects are clustered based on common themes.
Self-sovereign identity (SSI) is considered to be a "killer application" of blockchain. However, there is a lack of systematic architecture designs for blockchain-based SSI systems to support methodical development. An aspect of such gap is demonstrated in current solutions, which are considered coarse grained and may increase data security risks. In this paper, we first identify the lifecycles of three major SSI objects (i.e., key, identifier, and credential) and present fine-grained design patterns critical for application development. These patterns are associated with particular state transitions, providing a systematic view of system interactions and serving as a guidance for effective use of these patterns. Further, we present an SSI platform architecture, which advocates the notion of Design-Pattern-as-a-Service. Each design pattern serves as an API by wrapping the respective pattern code to ease application development and improve scalability and security. We implement a prototype and evaluate it on feasibility and scalability.
Ricardo Neisse, José L. Hernández-Ramos, Sara N. Matheu, Gianmarco Baldini · 8 authors
Cybersecurity certification is a core notion to support the mitigation of cybersecurity risks of Information and Communication Technologies (ICT). At the European Union (EU) level, the Cybersecurity Act establishes a common cybersecurity certification framework supporting the coexistence of different certification schemes across Member States. However, its realization needs to be sustained by technical approaches to enable ICT stakeholders from different sectors or countries to exchange cybersecurity information and evaluate the up-to-date security level of an ICT system throughout their lifecycle. Toward this end, we propose a blockchain-based platform using a novel interledger design, where ledgers associated with ICT artifacts, cybersecurity certificates, and vulnerabilities are interconnected. The main purpose is to leverage the advantages of blockchain in terms of distributed trust, transparency, and accountability, while at the same time coping with scalability, performance, and interoperability requirements. We analyze the impact of our platform in the current EU legislation and provide insights for its deployment.
On the blockchain, cryptocurrencies play a role similar to cash, while cryptographic tokens are a universal tool for handling rights and assets. Software wallets interact with blockchains in general and with smart contracts (on-chain programs) in particular. Some wallets are realized (partly) as smart contracts with the intent to increase trust and security by being transparent and by offering features like daily limits, approvals, multiple signatures, and recovery mechanisms. Ethereum is the most prominent platform for both, tokens and smart contracts, and thus also for wallet contracts. We discuss several methods for identifying wallet contracts in a semi-automatic manner by looking at the deployed bytecodes and their interaction patterns. Furthermore, we differentiate characteristics of wallets in use, and group them into six types.
Anton Permenev, Dimitar Dimitrov, Petar Tsankov, Dana Drachsler-Cohen · 5 authors
We present VerX, the first automated verifier able to prove functional properties of Ethereum smart contracts. VerX addresses an important problem as all real-world contracts must satisfy custom functional specifications.VerX is based on a careful combination of three techniques, enabling it to automatically verify temporal properties of infinite- state smart contracts: (i) reduction of temporal property verification to reachability checking, (ii) a new symbolic execution engine for the Ethereum Virtual Machine that is precise and efficient for a practical fragment of Ethereum contracts, and (iii) delayed predicate abstraction which uses symbolic execution during transactions and abstraction at transaction boundaries.Our extensive experimental evaluation on 83 temporal properties and 12 real-world projects, including popular crowdsales and libraries, demonstrates that VerX is practically effective.
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.
Ralph Holz, Diego Perino, Matteo Varvello, Johanna Amann · 9 authors
In late 2017, a sudden proliferation of malicious JavaScript was reported on the Web: browser-based mining exploited the CPU time of website visitors to mine the cryptocurrency Monero. Several studies measured the deployment of such code and developed defenses. However, previous work did not establish how many users were really exposed to the identified mining sites and whether there was a real risk given common user browsing behavior. In this paper, we present a retroactive analysis to close this research gap. We pool large-scale, longitudinal data from several vantage points, gathered during the prime time of illicit cryptomining, to measure the impact on web users. We leverage data from passive traffic monitoring of university networks and a large European ISP, with suspected mining sites identified in previous active scans. We corroborate our results with data from a browser extension with a large user base that tracks site visits. We also monitor open HTTP proxies and the Tor network for malicious injection of code. We find that the risk for most Web users was always very low, much lower than what deployment scans suggested. Any exposure period was also very brief. However, we also identify a previously unknown and exploited attack vector on mobile devices.
Tai D. Nguyen, Long Hoang Pham, Jun Sun, Yun Lin · 5 authors
Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and an efficient lightweight multi-objective adaptive strategy targeting those hard-to-cover branches. sFuzz has been applied to more than 4 thousand smart contracts and the experimental results show that (1) sFuzz is efficient, e.g., two orders of magnitude faster than state-of-the-art tools; (2) sFuzz is effective in achieving high code coverage and discovering vulnerabilities; and (3) the different fuzzing strategies in sFuzz complement each other.
Traditional IT security mechanisms are generally not well-suited for IoT devices, where processing and network connectivity should be kept at minimal. Consequently, IoT devices have been recently identified as an easy target for cyber-attacks, like for example on the Mirai botnet Distributed Denial of Service attacks in 2016, where various devices were hacked into and taken over. Different solutions have been developed aiming at guaranteeing the security at both the devices application layer and the network layers. Few succeeded to deliver the flexibility necessary for IoT devices. Even fewer have implemented an effective threats detection system, and just a handful have realised all the previous in a fully decentralised fashion, including this one. This Distributed Ledger Technology (DLT) attestation system is maintained and supported by most, or all, IoT devices because it is based on a light-weight DLT protocol. It comprises of a system for authorisation and authentication for the individual devices as well as includes an anomalies detection system based on smart contracts. A demonstration was built to support a Smart City use case. The objective is to guarantee, in a decentralised manner, the security of low computational power devices executing the sensing function and their connectivity, and therefore the correct functioning of the system. On the demonstrator, the system was ran using DLT supported by the sensors connectivity bridge (built using Raspberry Pi’s). The system proved to be rapid to develop, flexible with regards to systems changes and resilient to attacks to both individual IoT devices and to the DLT.
Andreas Penzkofer, Bartosz Kuśmierz, Angelo Capossele, William H. Sanders · 5 authors
In recent years several distributed ledger technologies based on directed acyclic graphs (DAGs) have appeared on the market. Similar to blockchain technologies, DAG-based systems aim to build an immutable ledger and are faced with security concerns regarding the irreversibility of the ledger state. However, due to their more complex nature and recent popularity, the study of adversarial actions has received little attention so far. In this paper we are concerned with a particular type of attack on the IOTA cryptocurrency, more specifically a Parasite Chain attack that attempts to revert the history stored in the DAG structure, also called the Tangle. \nIn order to improve the security of the Tangle, we present a detection mechanism for this type of attack. In this mechanism, we embrace the complexity of the DAG structure by sampling certain aspects of it, more particularly the distribution of the number of approvers. We initially describe models that predict the distribution that should be expected for a Tangle without any malicious actors. We then introduce metrics that compare this reference distribution with the measured distribution. Upon detection, measures can then be taken to render the attack unsuccessful. We show that due to a form of the Parasite Chain that is different from the main Tangle it is possible to detect certain types of malicious chains. We also show that although the attacker may change the structure of the Parasite Chain to avoid detection, this is done so at a significant cost since the attack is rendered less efficient.
In recent years we have seen a great deal of attention to the topic of security analysis in smart contracts, especially those developed for the Ethereum blockchain. As a result, there seems to be an evergrowing demand for secure smart contracts to protect what could potentially be worth billions. In this paper, we introduce Etherolic as a robust, scalable and efficient fuzzing tool based on concolic testing for performing security analysis on smart contracts. Our approach works based on a successful combination of dynamic taint tracking (DTA) and concolic testing that allows users to analyze the bytecode of smart contracts being run on the Ethereum Virtual Machine. Moreover, Etherolic is not only able to identify a wide spectrum of recent vulnerabilities in smart contracts, but also it generates exploits to trigger unknown errors in the code. In order to demonstrate the usefulness of our approach, we evaluated Etherolic on a crafted benchmark suite, comprising several real-world and synthetic smart contracts along with 98 safety features. The result of our preliminary evaluation reveals 204 security violations in the benchmarks.
Michael Coblenz, Jonathan Aldrich, Brad A. Myers, Joshua Sunshine
Some blockchain programs (smart contracts) have included serious security vulnerabilities. Obsidian is a new typestate-oriented programming language that uses a strong type system to rule out some of these vulnerabilities. Although Obsidian was designed to promote usability to make it as easy as possible to write programs, strong type systems can cause a language to be difficult to use. In particular, ownership, typestate, and assets, which Obsidian uses to provide safety guarantees, have not seen broad adoption together in popular languages and result in significant usability challenges. We performed an empirical study with 20 participants comparing Obsidian to Solidity, which is the language most commonly used for writing smart contracts today. We observed that Obsidian participants were able to successfully complete more of the programming tasks than the Solidity participants. We also found that the Solidity participants commonly inserted asset-related bugs, which Obsidian detects at compile time.
Ajay K. Gogineni, S. Swayamjyoti, Devadatta Sahoo, Κ. K. Sahu · 5 authors
Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. These tools require more search time as the invocation depth increases. Since the number of smart contracts is increasing exponentially, it is difficult to analyze the contracts using these traditional tools. Recently a machine learning technique called Long Short Term Memory (LSTM) has been used for binary classification, i.e., to predict whether a smart contract is vulnerable or not. This technique requires nearly constant search time as the invocation depth increases. In the present article, we have shown a multi-class classification, where we classify a smart contract in Suicidal, Prodigal, Greedy, or Normal categories. We used Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), which is a variant of LSTM, to perform classification. We reduced the class imbalance (a large number of normal contracts as compared to other categories) by considering only the distinct opcode combination for normal contracts. We have achieved a weighted average Fbeta score of 90.0%. Hence, such techniques can be used to analyze a large number of smart contracts and help to improve the security of these contracts.
The convergence of fifth-generation (5G) communication and the Internet-of-Things (IoT) has dramatically increased the diversity and complexity of the network. This change diversifies the attacker’s attack vectors, increasing the impact and damage of cyber threats. Cyber threat intelligence (CTI) technology is a proof-based security system which responds to these advanced cyber threats proactively by analyzing and sharing security-related data. However, the performance of CTI systems can be significantly compromised by creating and disseminating improper security policies if an attacker intentionally injects malicious data into the system. In this paper, we propose a blockchain-based CTI framework that improves confidence in the source and content of the data and can quickly detect and eliminate inaccurate data for resistance to a Sybil attack. The proposed framework collects CTI by a procedure validated through smart contracts and stores information about the metainformation of data in a blockchain network. The proposed system ensures the validity and reliability of CTI data by ensuring traceability to the data source and proposes a system model that can efficiently operate and manage CTI data in compliance with the de facto standard. We present the simulation results to prove the effectiveness and Sybil-resistance of the proposed framework in terms of reliability and cost to attackers.
Vyper has been proposed as a new high-level language for Ethereum smart contract development due to numerous security vulnerabilities and attacks witnessed on contracts written in Solidity since the system's inception. Vyper aims to address these vulnerabilities by providing a language that focuses on simplicity, auditability and security. We present a survey where we study how well-known and commonly-encountered vulnerabilities in Solidity feature in Vyper's development environment. We analyze all such vulnerabilities individually and classify them into five groups based on their status in Vyper. To the best of our knowledge, our survey is the first attempt to study security vulnerabilities in Vyper.