Blockchain-Based Malware Detection Method Using Shared Signatures of Suspected Malware Files

Fuji, Ryusei; Usuzaki, Shotaro; Aburada, Kentaro; Yamaba, Hisaaki; Katayama, Tetsuro; Park, Mirang; Shiratori, Norio; Okazaki, Naonobu

doi:10.1007/978-3-030-29029-0_28

Cited by 12 publications

(13 citation statements)

References 6 publications

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“…With the aim of preventing the attacks affecting users' privacy, many works investigated the use of blockchain technologies [130], [131], [132], [133]. In essence, the blockchain ensures secure and reliable storing and sharing of signatures as well as a framework for using them for detection duties.…”

Section: E Bio-inspired and Other Detection Methodsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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Section: E Bio-inspired and Other Detection Methodsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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“…Ryusei Fuji et al 25 developed blockchain‐assisted malware discovery technique which utilized shared signatures of alleged malware files. This technique shared signatures of alleged files amongst the users thereby allows it to quickly respond to maximize the suspicious threats.…”

Section: Motivationsmentioning

confidence: 99%

“…The aforementioned strategies help to determine unknown malware which could not be determined by signature‐based strategies. Heuristic strategies are strategies which determine malware using machine learning and data mining strategies 25 …”

Section: Introductionmentioning

confidence: 99%

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Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN

Nalinipriya

Maram

Priya

et al. 2022

Concurrency and Computation

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The emergence of networking systems and quick deployment of applications cause huge increase in cybercrimes which involves various applications like phishing, hacking, and malware propagation. However, the Ransomware techniques utilize certain device which may lead to undesirable properties which might shrink the paying-victim pool. This paper devises a new method, namely Water Moth Flame optimization (WMFO) and deep recurrent neural network (Deep RNN) for determining Ransomware. Here, Deep RNN training is done with WMFO, and is developed by combining Moth Flame optimization (MFO) and Water wave optimization (WWO). Moreover, features are mined with opcodes and by finding term frequency-inverse document frequency (TF-IDF) amongst individual features. Moreover, Probabilistic Principal Component Analysis (PPCA) is adapted to choose significant features. These features are adapted in Deep RNN for classification, wherein the proposed WMFO is employed to produce optimum weights. The WMFO offered enhanced performance with elevated accuracy of 95.025%, sensitivity of 95%, and specificity of 96%.

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“…Ryusei et al [37] proposed a blockchain-based malware file detection method. Their proposal seeks to exploit certain blockchain functionalities, specifically the immutability of stored data and the robustness of consensus management.…”

Section: Related Studies-attack Detection Techniquesmentioning

confidence: 99%

Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

et al. 2021

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With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

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Blockchain-Based Malware Detection Method Using Shared Signatures of Suspected Malware Files

Cited by 12 publications

References 6 publications

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN

Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

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