@inproceedings{69e8c87ce5314076b5d91596ed1fc92a,
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",
year = "2022",
month = apr,
day = "21",
doi = "10.1007/978-981-16-7618-5_51",
language = "English",
isbn = "9789811676178",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer",
pages = "583--604",
editor = "Abrar Ullah and Steve Gill and {\'A}lvaro Rocha and Sajid Anwar",
booktitle = "Proceedings of International Conference on Information Technology and Applications. ICITA 2021",
}