Exploring Automatic Malware Detection Through Deep Learning Models

Jaafar M. Alghazo; David M. Feinauer; Sherif E. Abdelhamid

doi:10.1109/CSCE60160.2023.00050

Exploring Automatic Malware Detection Through Deep Learning Models

Jaafar M. Alghazo, David M. Feinauer, Sherif E. Abdelhamid

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Malware is continually being developed and reinvented by malicious users. Deep Learning opens doors for real-time detection and classification of malware. In this paper, we explore the use of deep learning models (GoogleNet and ResNet50) for the detection and classification of malware based on the Malimg dataset. In addition, we explore modifying deep learning models and present a modified ResNet50 model to study its effect on classification accuracy. Finally, we explore the use of ensemble methods, applying three independent ResNet50 models to study the effect of ensemble models on classification accuracy. GoogleNet achieved the highest test accuracy among the three with 94.82%, however, when applying the ensemble method, the test accuracy reached 97.86%.

Original language	English (US)
Title of host publication	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	287-294
Number of pages	8
ISBN (Electronic)	9798350327595
DOIs	https://doi.org/10.1109/CSCE60160.2023.00050
State	Published - 2023
Externally published	Yes
Event	2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 - Las Vegas, United States Duration: Jul 24 2023 → Jul 27 2023

Publication series

Name	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

Conference

Conference	2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Country/Territory	United States
City	Las Vegas
Period	7/24/23 → 7/27/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Cybersecurity
Deep Learning
GoogleNet
Malimg
Malware Classification
ResNet50

Publisher link

10.1109/CSCE60160.2023.00050

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Alghazo, J. M., Feinauer, D. M., & Abdelhamid, S. E. (2023). Exploring Automatic Malware Detection Through Deep Learning Models. In Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (pp. 287-294). (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCE60160.2023.00050

Exploring Automatic Malware Detection Through Deep Learning Models. / Alghazo, Jaafar M.; Feinauer, David M.; Abdelhamid, Sherif E.
Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 287-294 (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Alghazo, JM, Feinauer, DM & Abdelhamid, SE 2023, Exploring Automatic Malware Detection Through Deep Learning Models. in Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 287-294, 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023, Las Vegas, United States, 7/24/23. https://doi.org/10.1109/CSCE60160.2023.00050

Alghazo JM, Feinauer DM, Abdelhamid SE. Exploring Automatic Malware Detection Through Deep Learning Models. In Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 287-294. (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023). doi: 10.1109/CSCE60160.2023.00050

Alghazo, Jaafar M. ; Feinauer, David M. ; Abdelhamid, Sherif E. / Exploring Automatic Malware Detection Through Deep Learning Models. Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 287-294 (Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023).

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abstract = "Malware is continually being developed and reinvented by malicious users. Deep Learning opens doors for real-time detection and classification of malware. In this paper, we explore the use of deep learning models (GoogleNet and ResNet50) for the detection and classification of malware based on the Malimg dataset. In addition, we explore modifying deep learning models and present a modified ResNet50 model to study its effect on classification accuracy. Finally, we explore the use of ensemble methods, applying three independent ResNet50 models to study the effect of ensemble models on classification accuracy. GoogleNet achieved the highest test accuracy among the three with 94.82%, however, when applying the ensemble method, the test accuracy reached 97.86%.",

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AB - Malware is continually being developed and reinvented by malicious users. Deep Learning opens doors for real-time detection and classification of malware. In this paper, we explore the use of deep learning models (GoogleNet and ResNet50) for the detection and classification of malware based on the Malimg dataset. In addition, we explore modifying deep learning models and present a modified ResNet50 model to study its effect on classification accuracy. Finally, we explore the use of ensemble methods, applying three independent ResNet50 models to study the effect of ensemble models on classification accuracy. GoogleNet achieved the highest test accuracy among the three with 94.82%, however, when applying the ensemble method, the test accuracy reached 97.86%.

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Exploring Automatic Malware Detection Through Deep Learning Models

Abstract

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