A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

A Gaurav, BB Gupta, PK Panigrahi - Enterprise Information …, 2023 - Taylor & Francis
ABSTRACT The Internet of Things (IoT) is a relatively new technology that has piqued
academics' and business information systems' attention in recent years. The Internet of …

An efficient densenet-based deep learning model for malware detection

J Hemalatha, SA Roseline, S Geetha, S Kadry… - Entropy, 2021 - mdpi.com
Recently, there has been a huge rise in malware growth, which creates a significant security
threat to organizations and individuals. Despite the incessant efforts of cybersecurity …

Intelligent vision-based malware detection and classification using deep random forest paradigm

SA Roseline, S Geetha, S Kadry, Y Nam - IEEE Access, 2020 - ieeexplore.ieee.org
Malware is a rapidly increasing menace to modern computing. Malware authors continually
incorporate various sophisticated features like code obfuscations to create malware variants …

Deep feature extraction and classification of android malware images

J Singh, D Thakur, F Ali, T Gera, KS Kwak - Sensors, 2020 - mdpi.com
The Android operating system has gained popularity and evolved rapidly since the previous
decade. Traditional approaches such as static and dynamic malware identification …

An improved two-hidden-layer extreme learning machine for malware hunting

AN Jahromi, S Hashemi, A Dehghantanha… - Computers & …, 2020 - Elsevier
Detecting unknown malware and their variants remains both an operational challenge and a
research challenge. In recent years, there have been attempts to design machine learning …

An enhanced stacked LSTM method with no random initialization for malware threat hunting in safety and time-critical systems

AN Jahromi, S Hashemi… - … on Emerging Topics …, 2020 - ieeexplore.ieee.org
Malware detection is an increasingly important operational focus in cyber security,
particularly, given the fast pace of such threats (eg, new malware variants introduced every …

A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding

J Zhang, Z Qin, H Yin, L Ou, K Zhang - Computers & Security, 2019 - Elsevier
Being able to detect malware variants is a critical problem due to the potential damages and
the fast paces of new malware variations. According to surveys from McAfee and Symantec …

Malware visualization for fine-grained classification

J Fu, J Xue, Y Wang, Z Liu, C Shan - IEEE Access, 2018 - ieeexplore.ieee.org
Due to the rapid rise of automated tools, the number of malware variants has increased
dramatically, which poses a tremendous threat to the security of the Internet. Recently, some …

[HTML][HTML] Segmentation and density statistics of mariculture cages from remote sensing images using mask R-CNN

C Yu, Z Hu, R Li, X Xia, Y Zhao, X Fan, Y Bai - Information Processing in …, 2022 - Elsevier
The normal growth of fishes is closely relevant to the density of mariculture. It is of great
significance to accurately calculate the breeding area of specific sea area from satellite …

Dalvik opcode graph based android malware variants detection using global topology features

J Zhang, Z Qin, K Zhang, H Yin, J Zou - IEEE Access, 2018 - ieeexplore.ieee.org
Since Android has become the dominator of smartphone operating system market with a
share of 86.8%, the number of Android malicious applications are increasing rapidly as well …