A survey of android malware detection with deep neural models

J Qiu, J Zhang, W Luo, L Pan, S Nepal… - ACM Computing Surveys …, 2020 - dl.acm.org
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

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 …

[HTML][HTML] The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

D Gibert, C Mateu, J Planes - Journal of Network and Computer …, 2020 - Elsevier
The struggle between security analysts and malware developers is a never-ending battle
with the complexity of malware changing as quickly as innovation grows. Current state-of-the …

Malware detection issues, challenges, and future directions: A survey

FA Aboaoja, A Zainal, FA Ghaleb, BAS Al-Rimy… - Applied Sciences, 2022 - mdpi.com
The evolution of recent malicious software with the rising use of digital services has
increased the probability of corrupting data, stealing information, or other cybercrimes by …

A comprehensive review on malware detection approaches

ÖA Aslan, R Samet - IEEE access, 2020 - ieeexplore.ieee.org
According to the recent studies, malicious software (malware) is increasing at an alarming
rate, and some malware can hide in the system by using different obfuscation techniques. In …

A review of android malware detection approaches based on machine learning

K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are develo** rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

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 …

Survey of machine learning techniques for malware analysis

D Ucci, L Aniello, R Baldoni - Computers & Security, 2019 - Elsevier
Co** with malware is getting more and more challenging, given their relentless growth in
complexity and volume. One of the most common approaches in literature is using machine …

The age of ransomware: A survey on the evolution, taxonomy, and research directions

S Razaulla, C Fachkha, C Markarian… - IEEE …, 2023 - ieeexplore.ieee.org
The proliferation of ransomware has become a significant threat to cybersecurity in recent
years, causing significant financial, reputational, and operational damage to individuals and …

Machine learning in cybersecurity: a comprehensive survey

D Dasgupta, Z Akhtar, S Sen - The Journal of Defense …, 2022 - journals.sagepub.com
Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …