A malware propagation prediction model based on representation learning and graph convolutional networks

Li, Tun; Liu, Yanbing; Liu, Qilie; Xu, Wei; Xiao, Yunpeng; Hong, Liu

doi:10.1016/j.dcan.2022.05.015

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…The need for Android mobile phone security has increased in light of recent breakthroughs in malware family variety (Bala et al, 2021; He et al, 2022; Mercaldo & Santone, 2022; Qiu et al, 2022; Shrivastava & Kumar, 2021), and these issues are just as prevalent in numerous other security domains (Alyamani, 2023; Li et al, 2022; Luo et al, 2021). Users are in greater danger, which increases the demand for security researchers and anti‐virus companies (Maray et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A precise method of identifying Android application family

Li,

Lu,

Wang

et al. 2023

Expert Systems

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Implementing the necessary countermeasures to detect the growing and highly destructive family of malware is an urgent obligation. The proliferation and diversity of malware make these problems more challenging. For beginners, it is arduous to attain crucial features for multi‐class family classification and extract valuable information from the obtained features. Another issue is that building a classification model that effectively absorbs multi‐class samples and adapts to various features is challenging. This work indicates a precise identification method for Android application families (ANDF) to tackle these issues. It perceptively analyzes the features that multi‐class families can utilize to identify members and further excavates the relationship between implicit information and the severity of those distinctions. A more appropriate classification model is developed for the heterogeneous file formats, and a more beneficial feature with a diverse array of heterogeneous information is chosen as the replacement representation of the sample. It is capable of upgrading learning ability and mastering the multi‐modal traits of the family malware. The application of ANDF to real data sets yields effective classification results. It is capable of 0.9800 in f1‐macro and has a classification accuracy of 98.61%. It performs, respectively, 0.0088 points better than the two‐feature comparison classification model and 0.0872 points better than the single‐feature comparison classification model. The kappa coefficient can also exceed 0.9830, which is at least 0.1044 higher than other contrasting classifiers and is 0.0105 greater than that of the contrasted model containing two features, which is 0.1046 larger than the classifier with a contrasting single feature.

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Section: Introductionmentioning

confidence: 99%

A precise method of identifying Android application family

Li,

Lu,

Wang

et al. 2023

Expert Systems

View full text Add to dashboard Cite

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“…In this sense, [10] uses a Bayesian structural time series (BSTS) model to study the data-driven dynamics of malware propagation in cyberspace. In [16], a malware propagation prediction model based on representation learning and GCNs is proposed, such that the proposed model predicts whether potential nodes will be infected by malware. As far as we know, the use of statistical techniques as a complement to the qualitative and numerical studies mentioned above has not been thoroughly explored.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Malware Propagation on Wireless Sensor Networks: A New Approach Using Queueing Theory and HJ-Biplot with a SIRS Model

Frutos-Bernal,

Rodríguez-Rosa,

Anciones-Polo

et al. 2023

Mathematics

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Most research on malware focuses mainly on its detection, without paying attention to its propagation trends. However, modeling the spread of malware is an important research problem because it allows us to predict how malware will evolve and to take steps to prevent its propagation, hence the interest in analyzing this spread from a statistical point of view. This work proposes a malware propagation prediction methodology based on multivariate statistical techniques such as HJ-Biplot in combination with closed queuing networks. Datasets generated using individual-based SIRS models are used to validate the proposed methodology, although any other model could have been chosen to test its validity. Experimental results show that the proposed model can effectively predict and classify malware and discover the influence of different model parameters on the malware propagation situation.

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ARdetector: android ransomware detection framework

Li,

Shi,

et al. 2023

J Supercomput

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A malware propagation prediction model based on representation learning and graph convolutional networks

Cited by 7 publications

References 23 publications

A precise method of identifying Android application family

A precise method of identifying Android application family

Analyzing Malware Propagation on Wireless Sensor Networks: A New Approach Using Queueing Theory and HJ-Biplot with a SIRS Model

ARdetector: android ransomware detection framework

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