Cryptocurrency malware detection in real-world environment: Based on multi-results stacking learning

Zheng, Rui; Wang, Qiuyun; Lin, Zhuopang; Jiang, Zhengwei; Fu, Jianming; Peng, Guojun

doi:10.1016/j.asoc.2022.109044

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…are valid at the same time, it proves that the results required by both parties are correct and equal, or the protocol is terminated; (10) Alice decodes the points W 1i , W 2i to obtain points d 1i , d 1i ; Bob decodes the points…”

Section: Protocol Startmentioning

confidence: 84%

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Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology

Liu,

Wang,

Luo

et al. 2023

Electronics

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With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.

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Section: Protocol Startmentioning

confidence: 84%

“…In general, deep learning has achieved good results in text-similarity-matching tasks [4][5][6]. Natural language processing is important in deep learning as a vector-based approach to solve the problem of text similarity [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology

Liu,

Wang,

Luo

et al. 2023

Electronics

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“…The frequency distribution of malware opcodes appears to deviate greatly from that of non-malicious software as rarer opcodes appear to explain greater variance in frequency than common opcodes [21]. Although the opcode feature can be beneficial for malware classification, it is limited by its incapability to define the invocation connections between instructions [26].…”

Section: Static Analysismentioning

confidence: 99%

Data-Driven Malware Detection for 6G Networks: A Survey From the Perspective of Continuous Learning and Explainability via Visualisation

Uysal

Yoo

Taha

2023

IEEE Open J. Veh. Technol.

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5G is inherently prone to security vulnerabilities. We witness that many today's networks contain 5G security flaws due to their reliance on the existing 4G network core. A lack of security standards for 5G IoT devices means network breaches and malware threats might run uncontrolled. The future 6G network is predicted to be implemented with artificial intelligencedriven communication via machine learning, enhanced edge computing, post-quantum cryptography and so forth. With the activation of edge computing, the computing power available at supercomputing servers is to be integrated directly into the devices at the entry point of a network in a distributed manner (e.g., antennas, routers, IoT sensors, etc). This feature brings an equal quality of service everywhere including remote regions (a.k.a service everywhere) which will trigger an exponential growth of associated applications. In this intricate environment, malware attacks are becoming more challenging to detect. This paper thus reviews the theoretical and experimental data-driven malware detection literature, in the large-scale data-intensive field, relating to: (1) continuous learning, including new concepts in multi-domain to multi-target learning and the challenges associated with unseen/unknown data, imbalance data and data scarcity, and (2) new explainability via visualisation concepts with a multi-labelling approach which allows identifying malware by their recipes while improving the interpretability of its decision process.

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Variants of Crypto-Jacking Attacks and Their Detection Techniques

Praise

Xavier

Jose

et al. 2023

Communications in Computer and Information Science

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Cryptocurrency malware detection in real-world environment: Based on multi-results stacking learning

Cited by 6 publications

References 15 publications

Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology

Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology

Data-Driven Malware Detection for 6G Networks: A Survey From the Perspective of Continuous Learning and Explainability via Visualisation

Variants of Crypto-Jacking Attacks and Their Detection Techniques

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