An Intelligent Hybrid Model for the Construction of Expert Systems in Malware Detection

Souza, Paulo Vitor de Campos; Guimarães, Augusto Júnio; Rezende, Thiago Silva; Araújo, Vanessa Souza; Nascimento, Luiz Antonio Ferreira do; Batista, Lucas Oliveira

doi:10.1109/eais48028.2020.9122770

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…De C. Souza et al [34] developed a hybrid expert system in the identification of malware using the artificial intelligence and fuzzy system approach, as well as the linearly scaled hyperbolic tangent (LiSHT) activation function to increase the accuracy (97.82%) of fuzzy neural network outputs. The proposed model shows a significant improvement in runtime (27.28s for training and 0.01s for testing) compared to other hybrid models.…”

Section: Malware Detection Methodsmentioning

confidence: 99%

A Client/Server Malware Detection Model Based on Machine Learning for Android Devices

Fournier

Khoury

Pierre

2021

IoT

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The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for malware detection on Android devices to protect private and financial information, for the mobile applications of the ATISCOM project. This model is based on client/server architecture, to reduce the heavy computations on a mobile device by sending data from the mobile device to the server for remote processing (i.e., offloading) of the predictions. We then gradually optimized our proposed model for better classification of the newly installed applications on Android devices. We at first adopted Naive Bayes to build the model with 92.4486% accuracy, then the classification method that gave the best accuracy of 93.85% for stochastic gradient descent (SGD) with binary class (i.e., malware and benign), and finally the regression method with numerical values ranging from −100 to 100 to manage the uncertainty predictions. Therefore, our proposed model with random forest regression gives a good accuracy in terms of performance, with a good correlation coefficient, minimum computation time and the smallest number of errors for malware detection.

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Section: Malware Detection Methodsmentioning

confidence: 99%

A Client/Server Malware Detection Model Based on Machine Learning for Android Devices

Fournier

Khoury

Pierre

2021

IoT

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An autoML network traffic analyzer for cyber threat detection

Παπανικολάου

Alevizopoulos

Ilioudis

et al. 2023

Int. J. Inf. Secur.

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Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

Souza

Lughofer

2022

Evolving Systems

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Evolving fuzzy neural networks are models capable of solving complex problems in a wide variety of contexts. In general, the quality of the data evaluated by a model has a direct impact on the quality of the results. Some procedures can generate uncertainty during data collection, which can be identified by experts to choose more suitable forms of model training. This paper proposes the integration of expert input on labeling uncertainty into evolving fuzzy neural classifiers (EFNC) in an approach called EFNC-U. Uncertainty is considered in class label input provided by experts, who may not be entirely confident in their labeling or who may have limited experience with the application scenario for which the data is processed. Further, we aimed to create highly interpretable fuzzy classification rules to gain a better understanding of the process and thus to enable the user to elicit new knowledge from the model. To prove our technique, we performed binary pattern classification tests within two application scenarios, cyber invasion and fraud detection in auctions. By explicitly considering class label uncertainty in the update process of the EFNC-U, improved accuracy trend lines were achieved compared to fully (and blindly) updating the classifiers with uncertain data. Integration of (simulated) labeling uncertainty smaller than 20% led to similar accuracy trends as using the original streams (unaffected by uncertainty). This demonstrates the robustness of our approach up to this uncertainty level. Finally, interpretable rules were elicited for a particular application (auction fraud identification) with reduced (and thus readable) antecedent lengths and with certainty values in the consequent class labels. Additionally, an average expected uncertainty of the rules were elicited based on the uncertainty levels in those samples which formed the corresponding rules.

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An Intelligent Hybrid Model for the Construction of Expert Systems in Malware Detection

Cited by 7 publications

References 27 publications

A Client/Server Malware Detection Model Based on Machine Learning for Android Devices

A Client/Server Malware Detection Model Based on Machine Learning for Android Devices

An autoML network traffic analyzer for cyber threat detection

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

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