From Data Behavior to Code Analysis: A Multimodal Study on Security and Privacy Challenges in Blockchain-Based DApp

• URL: http://arxiv.org/abs/2504.11860v1
• Date: Wed, 16 Apr 2025 08:30:43 GMT
• Title: From Data Behavior to Code Analysis: A Multimodal Study on Security and Privacy Challenges in Blockchain-Based DApp
• Authors: Haoyang Sun, Yishun Wang, Xiaoqi Li,
• Abstract summary: The recent proliferation of blockchain-based decentralized applications (DApp) has catalyzed transformative advancements in distributed systems.<n>This study initiates with a systematic analysis of behavioral patterns derived from empirical DApp datasets.<n>The principal security vulnerabilities in vulnerability-based smart contracts developed via Solidity are then critically examined.
• Score: 1.6081378516701994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent proliferation of blockchain-based decentralized applications (DApp) has catalyzed transformative advancements in distributed systems, with extensive deployments observed across financial, entertainment, media, and cybersecurity domains. These trustless architectures, characterized by their decentralized nature and elimination of third-party intermediaries, have garnered substantial institutional attention. Consequently, the escalating security challenges confronting DApp demand rigorous scholarly investigation. This study initiates with a systematic analysis of behavioral patterns derived from empirical DApp datasets, establishing foundational insights for subsequent methodological developments. The principal security vulnerabilities in Ethereum-based smart contracts developed via Solidity are then critically examined. Specifically, reentrancy vulnerability attacks are addressed by formally representing contract logic using highly expressive code fragments. This enables precise source code-level detection via bidirectional long short-term memory networks with attention mechanisms (BLSTM-ATT). Regarding privacy preservation challenges, contemporary solutions are evaluated through dual analytical lenses: identity privacy preservation and transaction anonymity enhancement, while proposing future research trajectories in cryptographic obfuscation techniques.

Related papers

• WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems [23.03944479383518]
  We propose WeiDetect, a two-phase, server-side defense mechanism for FL-based NIDS that detects malicious participants.<n>We conducted experiments to evaluate the effectiveness of our approach in diverse attack settings.<n>Our findings highlight that WeiDetect outperforms state-of-the-art defense approaches.
  arXiv  Detail & Related papers  (2025-04-06T05:31:24Z)
• Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
  Money laundering is a financial crime that obscures the origin of illicit funds. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations. This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
  arXiv  Detail & Related papers  (2025-03-13T05:19:44Z)
• Autonomous Identity-Based Threat Segmentation in Zero Trust Architectures [4.169915659794567]
  Zero Trust Architectures (ZTA) fundamentally redefine network security by adopting a "trust nothing, verify everything" approach. This research applies the proposed AI-driven, autonomous, identity-based threat segmentation in ZTA.
  arXiv  Detail & Related papers  (2025-01-10T15:35:02Z)
• Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey [37.07938402225207]
  Non- Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices to handle streaming data from a joint non-stationary environment.<n>This survey focuses on the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices.
  arXiv  Detail & Related papers  (2024-12-18T13:33:28Z)
• CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection [41.94295877935867]
  We introduce a benchmark to assess the ability of Large Language Models to autonomously identify vulnerabilities in new cryptographic protocols. We created a dataset of novel, flawed, communication protocols and designed a method to automatically verify the vulnerabilities found by the AI agents.
  arXiv  Detail & Related papers  (2024-11-20T14:16:55Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses [50.921333548391345]
  Federated Learning is a privacy preserving decentralized machine learning paradigm. Recent research has revealed that private ground truth data can be recovered through a gradient technique known as Deep Leakage. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-05T11:42:26Z)
• Towards Secure and Private AI: A Framework for Decentralized Inference [14.526663289437584]
  Large multimodal foundational models present challenges in scalability, reliability, and potential misuse.<n>Decentralized systems offer a solution by distributing workload and mitigating central points of failure.<n>We address these challenges with a comprehensive framework designed for responsible AI development.
  arXiv  Detail & Related papers  (2024-07-28T05:09:17Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security [1.2999518604217852]
  Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks. Previous studies predominantly focused on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices. This paper introduces the Federated Learning-based Intrusion Detection System (FL-IDS), addressing challenges encountered by centralized systems in FANETs.
  arXiv  Detail & Related papers  (2023-12-07T08:50:25Z)
• Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services [0.0]
  Data Distribution Service (DDS) is an innovative approach towards communication in ICS/IoT infrastructure and robotics. Traditional intrusion detection systems (IDS) do not detect any anomalies in the publish/subscribe method. This report presents an experimental work on simulation and application of Deep Learning for their detection.
  arXiv  Detail & Related papers  (2021-06-12T12:53:38Z)
• Blockchained Federated Learning for Threat Defense [0.0]
  This research paper introduces the development of an intelligent Threat Defense system, employing Federated Learning. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods.
  arXiv  Detail & Related papers  (2021-02-25T09:16:48Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

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.