[HTML][HTML] Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling

Q Kang, EJ Chen, ZC Li, HB Luo, Y Liu - Underground Space, 2023 - Elsevier
Shield machine may deviate from its design axis during excavation due to the uncertainty of
geological environment and the complexity of operation. This study therefore introduced a …

A survey of recent advances in deep learning models for detecting malware in desktop and mobile platforms

P Maniriho, AN Mahmood, MJM Chowdhury - ACM Computing Surveys, 2024 - dl.acm.org
Malware is one of the most common and severe cyber threats today. Malware infects
millions of devices and can perform several malicious activities including compromising …

Detecting malware by analyzing app permissions on android platform: A systematic literature review

A Ehsan, C Catal, A Mishra - Sensors, 2022 - mdpi.com
Smartphone adaptation in society has been progressing at a very high speed. Having the
ability to run on a vast variety of devices, much of the user base possesses an Android …

MalSPM: Metamorphic malware behavior analysis and classification using sequential pattern mining

MS Nawaz, P Fournier-Viger, MZ Nawaz, G Chen… - Computers & …, 2022 - Elsevier
Malware pose a serious threat to the computers of individuals, enterprises and other
organizations. In the Windows operating system (OS), Application Programming Interface …

Efficient malware classification by binary sequences with one-dimensional convolutional neural networks

WC Lin, YR Yeh - Mathematics, 2022 - mdpi.com
The rapid increase of malware attacks has become one of the main threats to computer
security. Finding the best way to detect malware has become a critical task in cybersecurity …

IIoT malware detection using edge computing and deep learning for cybersecurity in smart factories

H Kim, K Lee - Applied Sciences, 2022 - mdpi.com
The smart factory environment has been transformed into an Industrial Internet of Things
(IIoT) environment, which is an interconnected and open approach. This has made smart …

MCTVD: A malware classification method based on three-channel visualization and deep learning

H Deng, C Guo, G Shen, Y Cui, Y ** - Computers & Security, 2023 - Elsevier
With the rapid increase in the number of malware, the detection and classification of
malware have become more challenging. In recent years, many malware classification …

Deep learning fusion for effective malware detection: leveraging visual features

JA Johny, KA Asmitha, P Vinod, G Radhamani… - Cluster …, 2025 - Springer
Malware has become a formidable threat as it has grown exponentially in number and
sophistication. Thus, it is imperative to have a solution that is easy to implement, reliable …

Bhmdc: A byte and hex n-gram based malware detection and classification method

Y Tang, X Qi, J **g, C Liu, W Dong - Computers & Security, 2023 - Elsevier
In recent years, malware and their variants have proliferated, which poses a grave threat to
the systems and networks' security, so it is urgent to detect and classify malware in time to …

Enhanced Image-Based Malware Classification using Snake Optimization Algorithm with Deep Convolutional Neural Network

S Duraibi - IEEE Access, 2024 - ieeexplore.ieee.org
Malware is a malicious software intended to cause damage to computer systems. In recent
times, significant proliferation of malware utilized for illegal and malicious goals has been …