Deep Smart Contract Intent Detection

• URL: http://arxiv.org/abs/2211.10724v3
• Date: Thu, 26 Dec 2024 13:10:25 GMT
• Title: Deep Smart Contract Intent Detection
• Authors: Youwei Huang, Sen Fang, Jianwen Li, Jiachun Tao, Bin Hu, Tao Zhang,
• Abstract summary: textscSmartIntentNN is a deep learning model designed to automatically detect development intents in smart contracts. We trained and evaluated textscSmartIntentNN on a dataset containing over 40,000 real-world smart contracts.
• Score: 5.642524477190184
• License:
• Abstract: In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detecting developers' intents in smart contracts has become a more pressing issue, as malicious intents have caused substantial financial losses. Unfortunately, existing research lacks effective methods for detecting development intents in smart contracts. To address this gap, we propose \textsc{SmartIntentNN} (Smart Contract Intent Neural Network), a deep learning model designed to automatically detect development intents in smart contracts. \textsc{SmartIntentNN} leverages a pre-trained sentence encoder to generate contextual representations of smart contracts, employs a K-means clustering model to identify and highlight prominent intent features, and utilizes a bidirectional LSTM-based deep neural network for multi-label classification. We trained and evaluated \textsc{SmartIntentNN} on a dataset containing over 40,000 real-world smart contracts, employing self-comparison baselines in our experimental setup. The results show that \textsc{SmartIntentNN} achieves an F1-score of 0.8633 in identifying intents across 10 distinct categories, outperforming all baselines and addressing the gap in smart contract detection by incorporating intent analysis.

Related papers

• Exact Certification of (Graph) Neural Networks Against Label Poisoning [50.87615167799367]
  Machine learning models are vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. We introduce an exact certification method, deriving both sample-wise and collective certificates. Our work presents the first exact certificate to a poisoning attack ever derived for neural networks, which could be of independent interest.
  arXiv  Detail & Related papers  (2024-11-30T17:05:12Z)
• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection [8.121484960948303]
  We propose Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a contrastive learning (CL) model to capture the fine-grained correlation information among contracts. We show that Clear achieves optimal performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.
  arXiv  Detail & Related papers  (2024-04-27T09:13:25Z)
• Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
  In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars. Various tools have been developed to detect and mitigate vulnerabilities in smart contracts. This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
  arXiv  Detail & Related papers  (2023-12-27T11:26:26Z)
• HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model [1.16095700765361]
  Existing analysis techniques are capable of identifying a large number of smart contract security flaws, but they rely too much on rigid criteria established by specialists. We propose HyMo as a multi-modal hybrid deep learning model, which intelligently considers various input representations to consider multimodality. We show that our hybrid HyMo model has excellent smart contract vulnerability detection performance.
  arXiv  Detail & Related papers  (2023-04-25T19:16:21Z)
• An Automated Vulnerability Detection Framework for Smart Contracts [18.758795474791427]
  We propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract. Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result.
  arXiv  Detail & Related papers  (2023-01-20T23:16:04Z)
• SmartIntentNN: Towards Smart Contract Intent Detection [5.9789082082171525]
  We introduce textscSmartIntentNN (Smart Contract Intent Neural Network), a deep learning-based tool designed to automate the detection of developers' intent in smart contracts. Our approach integrates a Universal Sentence for contextual representation of smart contract code, and employs a K-means clustering algorithm to highlight intent-related code features. Evaluations on 10,000 real-world smart contracts demonstrate that textscSmartIntentNN surpasses all baselines, achieving an F1-score of 0.8633.
  arXiv  Detail & Related papers  (2022-11-24T15:36:35Z)
• Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach [60.67748036747221]
  Implicit neural networks offer competitive performance and reduced memory consumption. They can remain brittle with respect to input adversarial perturbations. This paper proposes a theoretical and computational framework for robustness verification of implicit neural networks.
  arXiv  Detail & Related papers  (2021-12-10T03:08:55Z)
• Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion [48.744359070088166]
  Conventional smart contract vulnerability detection methods heavily rely on fixed expert rules. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. We develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features.
  arXiv  Detail & Related papers  (2021-06-17T07:12:13Z)
• A Bytecode-based Approach for Smart Contract Classification [10.483992071557195]
  The number of smart contracts deployed on blockchain platforms is growing exponentially, which makes it difficult for users to find desired services by manual screening. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems.
  arXiv  Detail & Related papers  (2021-05-31T03:00:29Z)
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
  Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
  arXiv  Detail & Related papers  (2021-03-23T15:04:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.