Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917745156685824 |
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| author | Khoa, Tran Viet Son, Do Hai Nguyen, Chi-Hieu Hoang, Dinh Thai Nguyen, Diep N. Quynh, Tran Thi Thuy Hoang, Trong-Minh Ha, Nguyen Viet Dutkiewicz, Eryk Alsheikh, Abu Trung, Nguyen Linh |
| author_facet | Khoa, Tran Viet Son, Do Hai Nguyen, Chi-Hieu Hoang, Dinh Thai Nguyen, Diep N. Quynh, Tran Thi Thuy Hoang, Trong-Minh Ha, Nguyen Viet Dutkiewicz, Eryk Alsheikh, Abu Trung, Nguyen Linh |
| contents | With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts by analyzing transaction features. Our framework exhibits the capability to classify various types of blockchain attacks, including intricate attacks at the machine code level (e.g., injecting malicious codes to withdraw coins from users unlawfully), which typically necessitate significant time and security expertise to detect. To achieve that, the proposed framework incorporates a unique tool that transforms transaction features into visual representations, facilitating efficient analysis and classification of low-level machine codes. Furthermore, we propose an advanced collaborative learning model to enable real-time detection of diverse attack types at distributed mining nodes. Our model can efficiently detect attacks in smart contracts and transactions for blockchain systems without the need to gather all data from mining nodes into a centralized server. In order to evaluate the performance of our proposed framework, we deploy a pilot system based on a private Ethereum network and conduct multiple attack scenarios to generate a novel dataset. To the best of our knowledge, our dataset is the most comprehensive and diverse collection of transactions and smart contracts synthesized in a laboratory for cyberattack detection in blockchain systems. Our framework achieves a detection accuracy of approximately 94% through extensive simulations and 91% in real-time experiments with a throughput of over 2,150 transactions per second. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_15804 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts Khoa, Tran Viet Son, Do Hai Nguyen, Chi-Hieu Hoang, Dinh Thai Nguyen, Diep N. Quynh, Tran Thi Thuy Hoang, Trong-Minh Ha, Nguyen Viet Dutkiewicz, Eryk Alsheikh, Abu Trung, Nguyen Linh Cryptography and Security Distributed, Parallel, and Cluster Computing With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts by analyzing transaction features. Our framework exhibits the capability to classify various types of blockchain attacks, including intricate attacks at the machine code level (e.g., injecting malicious codes to withdraw coins from users unlawfully), which typically necessitate significant time and security expertise to detect. To achieve that, the proposed framework incorporates a unique tool that transforms transaction features into visual representations, facilitating efficient analysis and classification of low-level machine codes. Furthermore, we propose an advanced collaborative learning model to enable real-time detection of diverse attack types at distributed mining nodes. Our model can efficiently detect attacks in smart contracts and transactions for blockchain systems without the need to gather all data from mining nodes into a centralized server. In order to evaluate the performance of our proposed framework, we deploy a pilot system based on a private Ethereum network and conduct multiple attack scenarios to generate a novel dataset. To the best of our knowledge, our dataset is the most comprehensive and diverse collection of transactions and smart contracts synthesized in a laboratory for cyberattack detection in blockchain systems. Our framework achieves a detection accuracy of approximately 94% through extensive simulations and 91% in real-time experiments with a throughput of over 2,150 transactions per second. |
| title | Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2308.15804 |