A review on machine learning styles in computer vision—techniques and future directions

SV Mahadevkar, B Khemani, S Patil, K Kotecha… - Ieee …, 2022 - ieeexplore.ieee.org
Computer applications have considerably shifted from single data processing to machine
learning in recent years due to the accessibility and availability of massive volumes of data …

Noncontact sensing techniques for AI-aided structural health monitoring: a systematic review

A Sabato, S Dabetwar, NN Kulkarni… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Engineering structures and infrastructure continue to be used despite approaching or having
reached their design lifetime. While contact-based measurement techniques are challenging …

BSFR-SH: Blockchain-enabled security framework against ransomware attacks for smart healthcare

M Wazid, AK Das, S Shetty - IEEE Transactions on Consumer …, 2022 - ieeexplore.ieee.org
Ransomware is a type of malicious program or software that encrypts the contents on a hard
disc and prevents the users from accessing them unless they pay an amount (called a …

A few-shot class-incremental learning method for network intrusion detection

L Du, Z Gu, Y Wang, L Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the rapid development of information technologies, the security of cyberspace has
become increasingly serious. Network intrusion detection is a practical scheme to protect …

A two‐stage deep learning framework for image‐based android malware detection and variant classification

P Yadav, N Menon, V Ravi… - Computational …, 2022 - Wiley Online Library
With the popularity of the internet and smartphones, malware on smartphones has increased
dramatically. In addition, the ubiquity and openness of the Android operating system have …

Android malware detection methods based on convolutional neural network: A survey

L Shu, S Dong, H Su, J Huang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Android malware detection (AMD) is a challenging task requiring many factors to be
considered during detection, such as feature extraction and processing, performance …

EfficientNet convolutional neural networks-based Android malware detection

P Yadav, N Menon, V Ravi, S Vishvanathan… - Computers & …, 2022 - Elsevier
Owing to the increasing number and complexity of malware threats, research on automated
malware detection has become a hot topic in the field of network security. Traditional …

Explainable Artificial Intelligence‐Based IoT Device Malware Detection Mechanism Using Image Visualization and Fine‐Tuned CNN‐Based Transfer Learning Model

H Naeem, BM Alshammari… - Computational Intelligence …, 2022 - Wiley Online Library
Automated malware detection is a prominent issue in the world of network security because
of the rising number and complexity of malware threats. It is time‐consuming and resource …

Efficient android malware identification with limited training data utilizing multiple convolution neural network techniques

A Ksibi, M Zakariah, L Almuqren… - Engineering Applications of …, 2024 - Elsevier
Abstract The Internet of Things (IoT) has experienced phenomenal expansion over the past
few years and has emerged as one of the most dynamic sectors of the international market …

Attention‐based convolutional neural network deep learning approach for robust malware classification

V Ravi, M Alazab - Computational intelligence, 2023 - Wiley Online Library
Recently, transforming windows files into images and its analysis using machine learning
and deep learning have been considered as a state‐of‐the art works for malware detection …