Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection

Rana, Md. Shohel; Gudla, Charan; Sung, Andrew H.

doi:10.1007/978-3-030-22868-2_34

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…The authors tested their system on a simulated network and showed it can detect malware effectively [5]. Rana et al (2019) evaluated the performance of machine-learning models for Android malware detection on the Ethereum blockchain. The authors showed that their system can achieve high accuracy while maintaining low computational overhead.…”

Section: Literature Surveymentioning

confidence: 99%

“…The authors showed that their system can achieve high accuracy while maintaining low computational overhead. [6] Anita and Vijayalakshmi (2019) surveyed various security attacks on blockchain and discussed the potential impact of these attacks on different blockchain applications, including malware detection. [7] Fuji et al ( 2020) proposed a blockchain-based malware detection method that uses shared signatures of suspected malware files.…”

Section: Literature Surveymentioning

confidence: 99%

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Decentralized Malware Attacks Detection using Blockchain

Sheela¹,

Shalini²,

Harsha³

et al. 2023

ITM Web Conf.

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This research introduces an approach to detect malware attacks using blockchain technology that integrates signature-based and behavioralbased methods. The proposed system uses a decentralized blockchain network to share and store malware signatures and behavioral patterns. This enables faster and more efficient detection of new malware files. The signature-based method involves storing the signatures in the blockchain and the sharing of the signature of malware files among the user nodes of the p2p blockchain network, while the behavioral-based approach analyzes the behavior and actions of files in a separate virtualized environment to identify suspicious patterns. This system addresses the limitations of conventional signature-based methods, which can be evaded by polymorphic malware, and behavioral-based methods, which may generate false positives. The results of the evaluation indicate that the proposed system achieves high detection rates while maintaining low false positives. Overall, the proposed system offers an effective and efficient approach to malware detection by utilizing the strengths of both signature-based and behavioral-based methods and utilizing the security and transparency benefits of blockchain technology.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Decentralized Malware Attacks Detection using Blockchain

Sheela¹,

Shalini²,

Harsha³

et al. 2023

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…Rana et al [21] investigated various machine-learning models in a consortium blockchain network for a specific dataset. The decentralized network provides transparency, enhances security, and reduces the expense of managing all crucial data by eliminating intermediaries.…”

Section: Comprehensive Reviewmentioning

confidence: 99%

Blockchain-Based Data Breach Detection: Approaches, Challenges, and Future Directions

Ansar,

Ahmed,

Helfert

et al. 2023

Mathematics

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In cybersecurity, personal data breaches have become one of the significant issues. This fact indicates that data breaches require unique detection systems, techniques, and solutions, which necessitate the potential to facilitate precise and quick data breach detection. Various research works on data breach detection and related areas in dealing with this problem have been proposed. Several survey studies have been conducted to comprehend insider data breaches better. However, these works did not examine techniques related to blockchain and innovative smart contract technologies to detect data breaches. In this survey, we examine blockchain-based data breach detection mechanisms developed so far to deal with data breach detection. We compare blockchain-based data breach detection techniques based on type, platform, smart contracts, consensus algorithm language/tool, and evaluation measures. We also present a taxonomy of contemporary data breach types. We conclude our study by outlining existing methodologies’ issues, offering ideas for overcoming those challenges, and pointing the way forward.

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“…Rana et al 11 developed a consortium blockchain to evaluate various machine learning models on the DREBIN dataset. In this paper, a reward is o®ered using smart contracts as an incentive to the competitors for their work by allowing them to submit solutions through training with selected machine learning models in a secure and trustworthy manner.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Rana

Sung

2020

Vietnam J. Comp. Sci.

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Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.

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Evaluating Machine Learning Models on the Ethereum Blockchain for Android Malware Detection

Cited by 13 publications

References 12 publications

Decentralized Malware Attacks Detection using Blockchain

Decentralized Malware Attacks Detection using Blockchain

Blockchain-Based Data Breach Detection: Approaches, Challenges, and Future Directions

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

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