@inproceedings{374084d880d944dc8e2c44b1ed6bbfeb,
title = "A Lightweight Detection of Sequential Patterns in File System Events During Ransomware Attacks",
abstract = "Ransomware poses a major threat by encrypting files and demanding ransom for decryption. This paper introduces a lightweight hybrid model for detecting ransomware by analyzing file system events. By combining XGBoost and Long Short-Term Memory (LSTM) networks, the approach identifies and predicts malicious behaviors with high accuracy and low computational cost. A File System Monitor Watchdog was developed to track file activities, collecting a dataset from 20 ransomware families. XGBoost is used for initial pattern detection, and LSTM networks for sequential analysis. The model achieved 97.12% detection accuracy, outperforming traditional methods in accuracy and efficiency, while reducing computational costs.",
keywords = "Data collection tool, Dataset, File System, File system attributes, Ransomware, Sequence LSTM Recurrent Neural Networks",
author = "Arash Mahboubi and Bui, {Hang Thanh} and Hamed Aboutorab and Khanh Luong and Seyit Camtepe and Keyvan Ansari",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 25th International Conference on Web Information Systems Engineering, WISE 2024 ; Conference date: 02-12-2024 Through 05-12-2024",
year = "2025",
doi = "10.1007/978-981-96-0576-7_16",
language = "English",
isbn = "9789819605750",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "204--215",
editor = "Mahmoud Barhamgi and Hua Wang and Xin Wang",
booktitle = "Web Information Systems Engineering – WISE 2024 - 25th International Conference, Proceedings",
address = "United States",
}