Traditional techniques for smart contract vulnerability detection rely on fixed expert criteria to discover vulnerabilities, which are less generalizable, scalable, and accurate. Deep learning algorithms help to address these issues, but most fail to encode true expert knowledge and remain interpretable. In this paper, we present a smart contract vulnerability detection mechanism that operates in phases with graph neural networks and expert patterns in deep learning to mutually address the deficiencies of the two detection approaches and improve smart contract vulnerability detection capabilities. Experiments show that our vulnerability detection mechanism outperforms the original deep learning model by an average of 6 points in detecting vulnerabilities and that the second stage of the checking mechanism can also block contract transactions containing dangerous actions at the Ethernet Virtual Machine (EVM) level and generate error reports for submission. This strategy helps to construct more stable smart contracts and to create a secure environment for smart contracts.
DeFi, a decentralized financial service based on blockchain, not only provides innovative financial services, but also poses various risks, such as the Terra Luna crash. Therefore, anomaly detection in DeFi is necessary to ensure the safety and reliability of the DeFi ecosystem. However, this is very difficult because of the complex protocol, interaction among smart contracts, and high market volatility. In this study, we propose a novel method to effectively detect anomalies in DeFi. To the best of our knowledge, this is the first study that utilizes deep learning to detect anomalies in DeFi. We propose a deep learning model, anomaly VAE-Transformer, which combines the variational autoencoder to extract local information in the short term, and the transformer, to identify dependencies between data in the long term. Based on a deep understanding of DeFi protocols, the proposed model collects and analyzes various on-chain data of Olympus DAO, a representative DeFi protocol, for extracting features suitable for anomaly detection. Then, we demonstrate the superiority of the proposed model by analyzing four anomaly cases detected successfully by the proposed model in Olympus DAO. A malicious attack attempt and structural changes in DeFi protocols can be identified quickly using the proposed method; this is expected to help protect the assets of DeFi users and improve the safety, reliability, and transparency of the DeFi market. The dataset and codes are available athttps://github.com/fialle/Anomaly-VAE-Transformer
Numerous abnormal transactions have been exposed as a result of targeted attacks on Ethereum, such as the Ethereum Decentralized Autonomous Organization attack. Exploiting vulnerabilities in smart contracts, malicious users can pursue their own illicit objectives through abnormal transactions. Consequently, identifying these malevolent users, implicated in fraudulent activities and their attribution, becomes exceedingly complex. Cryptocurrency transactions used for malicious purposes, employing pseudo-anonymous accounts to send and receive ransom payments and accumulating funds under various identities, further highlight the need to control and detect these abnormal transactions for maintaining a high level of security within the Ethereum network. Although existing Intrusion Detection Systems (IDSs) help mitigate abnormal transaction occurrences, their performance necessitates improvement. To address this issue, this study presents a novel approach, named Abnormal Transactions Detection Using a Semi-Supervised Generative Adversarial Network (ATD-SGAN), which efficiently detects abnormal attacks within the Ethereum network. ATD-SGAN leverages a semi-supervised generative adversarial network for this purpose. The results demonstrate that ATD-SGAN significantly enhances the performance of state-of-the-art IDSs. It achieves an increase in detection accuracy from 3.78% to 11.05% and reduces the false alarm rate from 42.29% to 0.15%. Moreover, ATD-SGAN notably improves the F1-measure, ranging from 10.39% to 3.79%, compared to the current IDSs.
Abstract Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit.
The issue of creating an information security system is very relevant in the world today. One of the urgent tasks is to solve the issues of effective protection of information from both external and internal threats through the creation and implementation of information security management systems in automated systems of enterprises, which, among other things, requires the formalization of the task of protecting information for its subsequent implementation by software and other means. Now there are security analysis systems, for example, that examine the security elements settings of workstations and servers operating systems, analyze the network topology, look for unprotected network connections, examine the settings of firewalls. The disadvantage of these systems is that they are not suitable for monitoring large volumes of network traffic. The solution to this problem is the use of monitoring tools capable of analyzing large amounts of data in real time. Therefore, a significant place in the article is given to the review of developments based on artificial intelligence technologies, namely multi-agent systems, review of information security models, threat risk assessment in automated systems.
 The functional architecture of the information security management system based on a multi-agent system has been proposed to search in real time for information security optimal solutions through the selection of such coalitions of protection mechanisms agents that will allow to build the optimal protection of the automated system according to the selected criteria. The model with complete overlapping of threats has been substantiated and adopted as a basis, which allows to analyze the overall situation and choose strategically important decisions directly during the organization of information security. The essence of of multi-agent systems functioning that implement a decentralized control system based on the work of autonomous agents that can be implemented programmatically has been revealed. The role of threat agents, resource agents, agents of protection mechanisms and their functional purpose have been defined. The problem of searching a set of protection mechanisms agents coalition for the current state of the automated system as a problem of optimal search by the criterion of protection cost, taking into account the value of information, has been generalized. Due to the modularity of the multi-agent system, the further work will be aimed at detailing its components and perfection.
Ran Guo, Weijie Chen, Lejun Zhang, Guopeng Wang · 5 authors
Blockchain technology is currently evolving rapidly, and smart contracts are the hallmark of the second generation of blockchains. Currently, smart contracts are gradually being used in power system networks to build a decentralized energy system. Security is very important to power systems and attacks launched against smart contract vulnerabilities occur frequently, seriously affecting the development of the smart contract ecosystem. Current smart contract vulnerability detection tools suffer from low correct rates and high false positive rates, which cannot meet current needs. Therefore, we propose a smart contract vulnerability detection system based on the Siamese network in this paper. We improved the original Siamese network model to perform smart contract vulnerability detection by comparing the similarity of two sub networks with the same structure and shared parameters. We also demonstrate, through extensive experiments, that the model has better vulnerability detection performance and lower false alarm rate compared with previous research results.
Cybersecurity is a major challenge in today’s era despite being equipped with multiple technologies, the world is lacking behind due to security issues. The domains like industries, manufacturing plants, healthcare organizations, educational institutes, etc are at the edge of shifting entirely to digitalization. This gives us immense improvement in existing processes but on the other hand, digitalized information is prone to hackers. Blockchain Technology is widely used for creating cryptocurrency as well as the research community is also utilizing this concept for numerous other applications. Supply chain, industry 4.0, defense services, agriculture industry, and many other fields exploit blockchain technology’s benefits. In this research paper, the recent work in the direction of boosting cybersecurity in different fields with blockchain technology is discussed, and also the future directions are outlined by depicting the research gap in that specific research area. Blockchain Technology is the master coin to provide reliable, transparent, and authorized access to information-sharing domains and it can be explored further for expanding the security of the system.
We study security-latency bounds for Nakamoto consensus, i.e., how secure a block is after it becomes k-deep in the chain. We improve the state-of-the-art bounds by analyzing the race between adversarial and honest chains in three different phases. We find the probability distribution of the growth of the adversarial chains under models similar to those in Guo and Ren (2022) when a target block becomes k-deep in the chain. We analyze certain properties of this race to model each phase with random walks that provide tighter bounds than the existing results. Combining all three phases provides novel upper and lower bounds for blockchains with small$\lambda \Delta $.
The security of crypto wallets is a major concern in light of the recent prevalence of thefts. Aiming at the problem that there is no complete and reliable security detection model for Android-based crypto wallets, this study provides an evaluation framework based on the standard Android application security detection and unique security assessment of crypto wallets. The framework presents an attack-based detection approach, which identifies potential wallet security issues by simulating attacks and exploiting vulnerabilities. Ten popular Android crypto wallets are evaluated and compared to validate the framework’s practicability and accuracy. The test results demonstrate that the framework can accurately reflect the performance security of wallets. Additionally, the study proposes the corresponding actions to address the identified common security threats in crypto wallets.
Cryptocurrencies like Bitcoin have become a popular weapon for illegal activities. They have the characteristics of decentralization and anonymity, which can effectively avoid the supervision of government departments. How to de-anonymize Bitcoin transactions is a crucial issue for regulatory and judicial investigation departments to supervise and combat crimes involving Bitcoin effectively. This paper aims to de-anonymize Bitcoin transactions and present a Bitcoin transaction traceability method based on Bitcoin network traffic analysis. According to the characteristics of the physical network that the Bitcoin network relies on, the Bitcoin network traffic is obtained at the physical convergence point of the local Bitcoin network. By analyzing the collected network traffic data, we realize the traceability of the input address of Bitcoin transactions and test the scheme in the distributed Bitcoin network environment. The experimental results show that this traceability mechanism is suitable for nodes connected to the Bitcoin network (except for VPN, Tor, etc.), and can obtain 47.5% recall rate and 70.4% precision rate, which are promising in practice.
Due to the surging popularity of various cryptocurrencies in recent years, a large number of browser extensions have been developed as portals to access relevant services, such as cryptocurrency exchanges and wallets. This has stimulated a wild growth of cryptocurrency themed malicious extensions that cause heavy financial losses to the users and legitimate service providers. They have shown their capability of evading the stringent vetting processes of the extension stores, highlighting a lack of understanding of this emerging type of malware in our community. In this work, we conduct the first systematic study to identify and characterize cryptocurrency-themed malicious extensions. We monitor seven official and third-party extension distribution venues for 18 months (December 2020 to June 2022) and have collected around 3600 unique cryptocurrency-themed extensions. Leveraging a hybrid analysis, we have identified 186 malicious extensions that belong to five categories. We then characterize those extensions from various perspectives including their distribution channels, life cycles, developers, illicit behaviors, and illegal gains. Our work unveils the status quo of the cryptocurrency-themed malicious extensions and reveals their disguises and programmatic features on which detection techniques can be based. Our work serves as a warning to extension users, and an appeal to extension store operators to enact dedicated countermeasures. To facilitate future research in this area, we release our dataset of the identified malicious extensions and open-source our analyzer.
Dec 1, 2022·2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
As an innovative technology of great significance, blockchain has been widely used in various walks of life. Meanwhile, scams have experienced rapid growth on the blockchain, the phishing scam is a classic fraud among them. Ethereum is the largest blockchain platform that supports smart contracts, and its ecosystem has been under serious threat due to phishing scams. Consequently, phishing scam detection is particularly critical for Ethereum to avoid economic loss. However, accounts on Ethereum are represented as strings without features, which brings great difficulties for detection. To combat this challenge, we propose an effective method based on Graph Convolutional Network (GCN) with Conditional Random Field (CRF) to detect phishing scams. Firstly, we process accounts and their neighbors with transaction records to build transaction graphs. Due to lack of portrait information, we adopt DeepWalk to provide initial features for each node. Then graph representations are learned through GCN with CRF. The extensive experiments show that the proposed model performs better than comparison methods on both precision and recall, which indicates that our method can effectively identify phishing scams on Ethereum.
Recently, the Internet of Things (IoT) has gained tremendous popularity in several realms such as smart cities, healthcare, industrial automation, etc. IoT networks are increasing rapidly, containing heterogeneous devices that offer easy and user-friendly services via the internet. With the big shift to IoT technology, the security of IoT networks has become a primary concern, especially with the lack of intrinsic security mechanisms regarding the limited capabilities of IoT devices. Therefore, many studies have been interested in enhancing the security of IoT networks. IoT networks need a scalable, decentralized, and adaptive defense system. Although the area of development provides advanced security solutions using AI and Blockchain, there is no systematic and comprehensive study talking about the convergence between AI and Blockchain to secure IoT networks. In this paper, we focus on reviewing and comparing recent studies that have been proposed for detecting cybersecurity attacks in IoT environments. This paper address three research questions and highlights the research gaps and future directions. This paper aims to increase the knowledge base for enhancing IoT security, recommend future research, and suggest directions for future research.
In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given.
Suliman Aladhadh, Huda Alwabli, Tarek Moulahi, Muneerah Al Asqah
Recently, blockchain technology has appeared as a powerful decentralized tool for data integrity protection. The use of smart contracts in blockchain helped to provide a secure environment for developing peer-to-peer applications. Blockchain has been used by the research community as a tool for protection against attacks. The blockchain itself can be the objective of many cyberthreats. In the literature, there are few research works aimed to protect the blockchain against cyberthreats adopting, in most cases, statistical schemes based on smart contracts and causing deployment and runtime overheads. Although, the power of machine learning tools there is insufficient use of these techniques to protect blockchain against attacks. For that reason, we aim, in this paper, to propose a new framework called BChainGuard for cyberthreat detection in blockchain. Our framework’s main goal is to distinguish between normal and abnormal behavior of the traffic linked to the blockchain network. In BChainGuard, the execution of the classification technique will be local. Next, we embed only the decision function as a smart contract. The experimental result shows encouraging results with an accuracy of detection of around 95% using SVM and 98.02% using MLP with a low runtime and overhead in terms of consumed gas.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.
Cryptocurrencies, such as Bitcoin and Ethereum, are becoming increasingly prevalent mainly due to their anonymity, decentralization, transparency, and security. However, the completely public ledger makes the trace and analysis of each account possible as long as the identity behind the public address is revealed. Theoretically, social networks could make that happen when addresses are posted on social network platforms using accounts containing personal information. To verify such a possibility, we have collected public data from two major platforms, i.e. Twitter and Reddit, aiming to find potential privacy leakage behind the ETH public address. In the end, an easy-to-use retrieval application is also built for a better illustration.
This paper presents an inter-domain transport net-work slice management with Service Level Agreements (SLA) using the ETSI TeraFlowSDN (TFS) controller. Different instances of the TFS controller are deployed for each involved domain. The communication between the TFS instances is supported by a Distributed Ledger Technology (DLT)-based database. The different TFS instances upload the abstracted view of their topologies and retrieve that from remote peers. When the end- to-end SLA of the transport network slice is violated, the slice is reconfigured avoiding the domain that originated the violation.
In current IPv6 networks, the increasing number of network devices also boosts the widespread DDoS attacks. Meanwhile, Intrusion Detection System (IDS) is evolved from the individual defense pattern to a distributed and collaborative mode, and Cooperative IDS (CIDS) becomes the mainstream technique. How to improve the overall defense capability through the coordination of information becomes worth studying. In this paper, we propose a DDoS blacklist mechanism with smart contract for IPv6-SAVI (Source Address Validation Improvements) network. In SAVI environment, DDoS source information detected by IDS is considered to be credible. Based on this observation, we design a dynamic update strategy for the reputation of trusted addresses based on the detection results and form a blacklist. Furthermore, we combine CIDS deployment with blockchain to design a blacklist sharing strategy based on smart contract, so that the individual IDS distributed on the chain can realize safe and reliable sharing and updating of the blacklist. Finally, extensive experiments evaluate the performance of our mechanism in terms of latency, overhead, reputation change accuracy, etc., which demonstrates that the blacklist can provide DDoS traffic filtering reference to improve the DDoS mitigation capability.
For data privacy, system reliability, and security, Blockchain technologies have become more popular in recent years. Despite its usefulness, the blockchain is vulnerable to cyber assaults; for example, in January 2019 a 51% attack on Ethereum Classic successfully exposed flaws in the platform's security. From a statistical point of view, attacks represent a highly unusual occurrence that deviates significantly from the norm. Blockchain attack detection may benefit from Deep Learning, a field of study whose aim is to discover insights, patterns, and anomalies within massive data repositories. In this work, we define an trusted two way intrusion detection system based on a Hierarchical weighed fuzzy algorithm and self-organized stacked network (SOSN) deep learning model, that is trained exploiting aggregate information extracted by monitoring blockchain activities. Here initially the smart contract handles the node authentication. The purpose of authenticating the node is to ensure that only specific nodes can submit and retrieve the information. We implement Hierarchical weighed fuzzy algorithm to evaluate the trust ability of the transaction nodes. Then the transaction verification step ensures that all malicious transactions or activities on the submitted transaction by self-organized stacked network deep learning model. The whole experimentation was carried out under matlab environment. Extensive experimental results confirm that our suggested detection method has better performance over important indicators such as Precision, Recall, F-Score, overhead.