Malware Prediction Using LSTM Networks

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of International Conference on Information Technology and Applications. ICITA 2021
EditorsAbrar Ullah, Steve Gill, Álvaro Rocha, Sajid Anwar
PublisherSpringer
Pages583-604
Number of pages22
ISBN (Electronic)9789811676185
ISBN (Print)9789811676178
DOIs
Publication statusPublished - 21 Apr 2022
Event15th International Conference on Information Technology and Applications 2021 - Dubai, United Arab Emirates
Duration: 13 Nov 202114 Nov 2021

Publication series

NameLecture Notes in Networks and Systems
Volume350
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference15th International Conference on Information Technology and Applications 2021
Abbreviated titleICITA 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period13/11/2114/11/21

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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