Ransomware Classification with Deep Neural Network and Bi-LSTM

Authors

  • Mujeeb ur Rehman Shaikh Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia
  • Mohd Fadzil Hassan Centre for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
  • Rehan Akbar School of Computing and Information Sciences, Florida International University, Miami, United States of America
  • K.S. Savita Positive Computing Research Centre, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Malaysia
  • Rafi Ullah Positive Computing Research Centre, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Malaysia
  • Satria Mandala Human Centric (HUMIC) Engineering & School of Computing Telkom University Bandung, Indonesia

DOI:

https://doi.org/10.37934/araset.47.2.266280

Keywords:

Ransomware Classification, Deep Neural Network, Bi-LSTM, Deep Learning, Cybersecurity

Abstract

Malicious attacks, malware, and ransomware families present essential risks to cybersecurity and may result in significant harm to computer systems, data clusters, networks, and mobile apps across a range of industries. Recently, there has been interest in ransomware classification using DNN and Bi-LSTM. DNN, a subset of machine learning techniques, has been found to improve ransomware detection and classification precision and efficacy. Ransomware has been affecting commercial, public, and governmental organizations' networks and computer systems for more than a decade, enabling new dynamic detection techniques to help DNNs detect ransomware. However, deep neural network-based architectures and DL classifiers (such as DNN, and Bi-LSTM classifiers) will be employed to detect ransomware. These networks may learn to correctly identify and categorize new ransomware incidents by integrating various datasets, including known and unknown ransomware samples. The classification of ransomware detection has been thoroughly investigated, and a model incorporating classic DL techniques with DNN and Bi-LSTM-based architecture will be proposed. A model execution experiment will be carried out to facilitate comparative testing of various approaches. This study focuses on the detection and classification of ransomware using DNN and Bi-LSTM. This study provides the groundwork for future investigations into the issues with ransomware detection. To protect against several ransomware attack types, deep neural networks have become an effective tool for ransomware detection. These networks combine machine learning and deep learning techniques.

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Author Biographies

Mujeeb ur Rehman Shaikh, Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia

mujeeb_22007910@utp.edu.my

Mohd Fadzil Hassan, Centre for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia

mfadzil_hassan@utp.edu.my

Rehan Akbar, School of Computing and Information Sciences, Florida International University, Miami, United States of America

rakbar@fiu.edu

K.S. Savita, Positive Computing Research Centre, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Malaysia

savitasugathan@utp.edu.my

Rafi Ullah, Positive Computing Research Centre, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Malaysia

rafi.ullah@utp.edu.my

Satria Mandala, Human Centric (HUMIC) Engineering & School of Computing Telkom University Bandung, Indonesia

satriamandala@telkomuniversity.ac.id

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Published

2024-06-28

How to Cite

Mujeeb ur Rehman Shaikh, Mohd Fadzil Hassan, Rehan Akbar, K.S. Savita, Rafi Ullah, & Satria Mandala. (2024). Ransomware Classification with Deep Neural Network and Bi-LSTM. Journal of Advanced Research in Applied Sciences and Engineering Technology, 47(2), 266–280. https://doi.org/10.37934/araset.47.2.266280

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Articles