Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Kamišalić, Aida; Kramberger, Renata; Fister, Iztok

doi:10.3390/app11177987

Cited by 24 publications

(13 citation statements)

References 172 publications

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“…According to P. Report, from 2015 to 2017, 80 A-share listed companies and 143 companies in China revealed financial fraud [9]. There are 108 companies, and in 2019 there was Pharmaceutical's financial fraud case, which ballooned to $ 20.644 billion from 2016 to 2018, operating profit of RMB 3, and operating profit of RMB 2.072 billion [10]. In the environment of information asymmetry between investors and managers and incomplete external supervision system of listed companies, how to deal with and prevent financial fraud of listed companies is a hot issue of public concern [11].…”

Section: Introductionmentioning

confidence: 99%

Application Analysis of Block Technology Based on Block Chain Computer Model in the False Identification of the Catalog List of the Financial Department of Multinational Companies

2022

Advances in Multimedia

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The financial data of some listed companies is not transparent enough, especially in financial-related management and program planning. In addition to legal and ethical constraints, with the continuous upgrading of blockchain technology, it is possible to explore the use of blockchain technology to lower the threshold for a certain number of investors to participate in corporate financial governance. Jointly supervising the capital flow of listed companies can ensure the safety of funds and improve the efficiency of capital use, thereby solving the problem of financial fraud of listed companies to a certain extent. The four core features of blockchain technology include decentralized distributed ledgers, consensus mechanisms, asymmetric encryption, and smart contracts. Exploring the feasibility and embedding path of chain technology in the capital supervision of listed companies will help to solve this problem. The integrated application of blockchain technology plays an important role in new technology innovation and industrial transformation. According to the unique technical characteristics of blockchain technology such as decentralization, nontampering, and traceability, combined with its application scenarios in the accounting field, it is hoped to use blockchain technology to solve problems such as information asymmetry and easy tampering of financial amounts from the source. Therefore, effective analysis can reduce the occurrence of financial fraud and fraud in listed companies, and blockchain technology has become a new way to prevent financial fraud in listed companies. This paper analyzes the classification and basic characteristics of blockchain, and studies the access mechanism and voting rules of candidate nodes based on the private chain architecture of the Raft distributed protocol to ensure the authenticity and real-time of financial information of listed companies and financial decisions. Improving the difficulty of financial fraud can provide good social and economic benefits.

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Section: Introductionmentioning

confidence: 99%

Application Analysis of Block Technology Based on Block Chain Computer Model in the False Identification of the Catalog List of the Financial Department of Multinational Companies

2022

Advances in Multimedia

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“…The stateless and intangible nature of virtual currencies further complicates regulatory and legal frameworks, requiring active efforts from many countries to address the associated challenges (Leuprecht et al, 2022). Moreover, the use of machine learning techniques and blockchain technology has been explored for anomaly detection and tracking illicit financial flows in virtual currency transactions (Liu et al, 2023;Kamišalić et al, 2021).…”

Section: Virtual Currencies In Online Transactionsmentioning

confidence: 99%

The Nexus of Cybercrime and Money Laundering: A Conceptual Paper

Azero,

Kay Abdullah,

Zakaria

et al. 2024

AFR

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The nexus between cybercrime and money laundering poses significant challenges that require a holistic understanding and strengthened global efforts to address. Cybercrimes provide financial motivation for criminals to engage in illicit activities. Techniques like cryptocurrency laundering and exploiting online payment systems allow criminals to disguise illegal proceeds. Investigating these modern financial crimes faces hurdles from rapid technological advancements, encryption, international jurisdiction issues, and lack of standardized information sharing between agencies and sectors. Gaps exist in current anti- money laundering legal frameworks in properly criminalizing emerging cyber threats and prosecuting related offenses effectively, as seen in the Malaysian context. Reforms are needed to enhance enforcement mechanisms, risk assessment practices, and the role of regulatory authorities like the Financial Intelligence Unit. A comprehensive approach combining updated laws, improved inter-agency coordination, public-private collaboration, and adaptive strategies is crucial to counter the evolving nexus between cybercrime and money laundering in today's digital landscape.

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“…The groundbreaking concepts of GANs have powered this tool into becoming a new frontier in the cyber security domain where several factors hailing from varied threat detection, vulnerability analysis, and adversarial robustness can be discovered. Understanding GAN's capacities, functions and its potential for utilizing cybersecurity is the main priority of this research which can contribute to the improvement of Security Science in the context of the modern threats [8]. Deep Learning models such as GANs are used to detect anomalies because they can provide nearly realistic synthetic data to be used to model small deviations and noises that could signify malicious activity in the traffic or system behavior.…”

Section: Firmware Attacksmentioning

confidence: 99%

Smart Network Forensics with Generative Adversarial Networks Leveraging Blockchain for Anomaly Detection and Immutable Audit Trails

Yan Lei

2024

pst

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Analyzing the specificity of the cybersecurity domain, the problem of ensuring the security and integrity of smart networks is multifaceted. This research explores the complexity of smart network forensics and seeks to meet theses challenges through different approaches. First, to establish the subject of the investigation, the context is described, which includes factors such as ever-fluctuating network traffic and increasing threat types. Further, a thorough analysis of the literature and research work available in the field of network forensics, anomaly detection methodologies, generative adversarial networks, and blockchain technology bring new perspectives and information to the discussion. From this perspective, the proposed methodology contributes towards devising a novel concept. Drawing on the prospects of using GANs for detecting anomalies, this research investigates how GANs can be employed to add synthetic data to training sets and improve the efficiency of smart networks in detecting anomalies. Similarly, Blockchain becomes a valuable asset in creating unalterable audit trails, and providing accountability and recoverability of any examined evidence. By incorporating these state-of-the-art approaches into the proposed work, this research aims at enhancing the reliability of smart network forensics to advance more effective cybersecurity awareness and threat analysis in complex and ever-evolving networks. DOI: https://doi.org/10.52783/pst.432

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Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

Cited by 24 publications

References 172 publications

Application Analysis of Block Technology Based on Block Chain Computer Model in the False Identification of the Catalog List of the Financial Department of Multinational Companies

Application Analysis of Block Technology Based on Block Chain Computer Model in the False Identification of the Catalog List of the Financial Department of Multinational Companies

The Nexus of Cybercrime and Money Laundering: A Conceptual Paper

Smart Network Forensics with Generative Adversarial Networks Leveraging Blockchain for Anomaly Detection and Immutable Audit Trails

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