TY - GEN
T1 - Dynamic Malware Detection Using LSTM Based GANs and Linux System Calls
AU - Rombough, Jeffrey C.
AU - Esmahi, Larbi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Malware developers have learned how to confuse researchers who are trying to reverse engineer their methods in static analysis. Dynamic analysis monitors the computer’s behaviour during malware execution and has an advantage over static analysis as it is less susceptible to malware’s attempts of method obfuscation. With the widespread use of Linux-based Internet of Things (IoT) devices, attacks on Linux-based assets have significantly increased. Linux uses system calls to allow a user’s program to interface with the operating system’s resources. These system calls can be analyzed in a dynamic fashion to determine if malware is affecting the operating system’s behaviour. In this paper, the combination of two AI technologies, Generative Adversarial Network (GAN) and Long-Short-Term-Memory (LSTM) network are used for detecting malware in Linux systems. The experimental findings of this research show promising results for using such technology in malware detection.
AB - Malware developers have learned how to confuse researchers who are trying to reverse engineer their methods in static analysis. Dynamic analysis monitors the computer’s behaviour during malware execution and has an advantage over static analysis as it is less susceptible to malware’s attempts of method obfuscation. With the widespread use of Linux-based Internet of Things (IoT) devices, attacks on Linux-based assets have significantly increased. Linux uses system calls to allow a user’s program to interface with the operating system’s resources. These system calls can be analyzed in a dynamic fashion to determine if malware is affecting the operating system’s behaviour. In this paper, the combination of two AI technologies, Generative Adversarial Network (GAN) and Long-Short-Term-Memory (LSTM) network are used for detecting malware in Linux systems. The experimental findings of this research show promising results for using such technology in malware detection.
KW - Generative Adversarial Network
KW - Linux system calls
KW - LSTM
KW - Machine learning
KW - Malware detection
UR - https://www.scopus.com/pages/publications/105014511104
U2 - 10.1007/978-3-031-94956-2_6
DO - 10.1007/978-3-031-94956-2_6
M3 - Published Conference contribution
AN - SCOPUS:105014511104
SN - 9783031949555
T3 - Communications in Computer and Information Science
SP - 77
EP - 90
BT - Computational Science and Computational Intelligence - 11th International Conference, CSCI 2024, Proceedings
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Shenavarmasouleh, Farzan
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
T2 - 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024
Y2 - 11 December 2024 through 13 December 2024
ER -