Malware Prediction Using LSTM Networks

Saba Iqbal; Abrar Ullah; Shiemaa Adlan; Ahmad Ryad Soobhany

doi:10.1007/978-981-16-7618-5_51

Malware Prediction Using LSTM Networks

Saba Iqbal^*, Abrar Ullah, Shiemaa Adlan, Ahmad Ryad Soobhany

^*Corresponding author for this work

School of Mathematical & Computer Sciences

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

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Abstract

With a recent increase in the use of the Internet, there has been a rise in malware attacks. Malware attacks can lead to stealing confidential data or make the target a source of further attacks. The detection of malware has been posing a unique challenge. Malware analysis is the study of malicious code to prevent cyber-attacks and vulnerability assessment. This article aims for classification of malware using a deep learning model to obtain an accurate and efficient performance. The system proposed in this study extracts a number of features and trains the long short-term memory (LSTM) model. The study utilises hyper-parameter tuning to improve the accuracy and efficiency of the LSTM model. The findings revealed 99.65% accuracy using sigmoid function that outperforms other activation function. This work can be helpful in malware detection to improve security posture.

Original language	English
Title of host publication	Proceedings of International Conference on Information Technology and Applications. ICITA 2021
Editors	Abrar Ullah, Steve Gill, Álvaro Rocha, Sajid Anwar
Publisher	Springer
Pages	583-604
Number of pages	22
ISBN (Electronic)	9789811676185
ISBN (Print)	9789811676178
DOIs	https://doi.org/10.1007/978-981-16-7618-5_51
Publication status	Published - 21 Apr 2022
Event	15th International Conference on Information Technology and Applications 2021 - Dubai, United Arab Emirates Duration: 13 Nov 2021 → 14 Nov 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	350
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	15th International Conference on Information Technology and Applications 2021
Abbreviated title	ICITA 2021
Country/Territory	United Arab Emirates
City	Dubai
Period	13/11/21 → 14/11/21

Keywords

Deep learning
LSTM
Malware
Neural networks
Security

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

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10.1007/978-981-16-7618-5_51

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Cite this

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title = "Malware Prediction Using LSTM Networks",

abstract = "With a recent increase in the use of the Internet, there has been a rise in malware attacks. Malware attacks can lead to stealing confidential data or make the target a source of further attacks. The detection of malware has been posing a unique challenge. Malware analysis is the study of malicious code to prevent cyber-attacks and vulnerability assessment. This article aims for classification of malware using a deep learning model to obtain an accurate and efficient performance. The system proposed in this study extracts a number of features and trains the long short-term memory (LSTM) model. The study utilises hyper-parameter tuning to improve the accuracy and efficiency of the LSTM model. The findings revealed 99.65% accuracy using sigmoid function that outperforms other activation function. This work can be helpful in malware detection to improve security posture.",

keywords = "Deep learning, LSTM, Malware, Neural networks, Security",

author = "Saba Iqbal and Abrar Ullah and Shiemaa Adlan and Soobhany, {Ahmad Ryad}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 15th International Conference on Information Technology and Applications 2021, ICITA 2021 ; Conference date: 13-11-2021 Through 14-11-2021",

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Iqbal, S, Ullah, A, Adlan, S & Soobhany, AR 2022, Malware Prediction Using LSTM Networks. in A Ullah, S Gill, Á Rocha & S Anwar (eds), Proceedings of International Conference on Information Technology and Applications. ICITA 2021. Lecture Notes in Networks and Systems, vol. 350, Springer, pp. 583-604, 15th International Conference on Information Technology and Applications 2021, Dubai, United Arab Emirates, 13/11/21. https://doi.org/10.1007/978-981-16-7618-5_51

Malware Prediction Using LSTM Networks. / Iqbal, Saba; Ullah, Abrar; Adlan, Shiemaa et al.

Proceedings of International Conference on Information Technology and Applications. ICITA 2021. ed. / Abrar Ullah; Steve Gill; Álvaro Rocha; Sajid Anwar. Springer, 2022. p. 583-604 (Lecture Notes in Networks and Systems; Vol. 350).

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

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T1 - Malware Prediction Using LSTM Networks

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AU - Adlan, Shiemaa

AU - Soobhany, Ahmad Ryad

PY - 2022/4/21

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N2 - With a recent increase in the use of the Internet, there has been a rise in malware attacks. Malware attacks can lead to stealing confidential data or make the target a source of further attacks. The detection of malware has been posing a unique challenge. Malware analysis is the study of malicious code to prevent cyber-attacks and vulnerability assessment. This article aims for classification of malware using a deep learning model to obtain an accurate and efficient performance. The system proposed in this study extracts a number of features and trains the long short-term memory (LSTM) model. The study utilises hyper-parameter tuning to improve the accuracy and efficiency of the LSTM model. The findings revealed 99.65% accuracy using sigmoid function that outperforms other activation function. This work can be helpful in malware detection to improve security posture.

AB - With a recent increase in the use of the Internet, there has been a rise in malware attacks. Malware attacks can lead to stealing confidential data or make the target a source of further attacks. The detection of malware has been posing a unique challenge. Malware analysis is the study of malicious code to prevent cyber-attacks and vulnerability assessment. This article aims for classification of malware using a deep learning model to obtain an accurate and efficient performance. The system proposed in this study extracts a number of features and trains the long short-term memory (LSTM) model. The study utilises hyper-parameter tuning to improve the accuracy and efficiency of the LSTM model. The findings revealed 99.65% accuracy using sigmoid function that outperforms other activation function. This work can be helpful in malware detection to improve security posture.

KW - Deep learning

KW - LSTM

KW - Malware

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Malware Prediction Using LSTM Networks

Abstract

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Keywords

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