[HTML][HTML] MalHyStack: a hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis

KS Roy, T Ahmed, PB Udas, ME Karim… - Intelligent Systems with …, 2023 - Elsevier
Since the advent of malware, it has reached a toll in this world that exchanges billions of
data daily. Millions of people are victims of it, and the numbers are not decreasing as the …

Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

RA Yunmar, SS Kusumawardani, F Mohsen - IEEE Access, 2024 - ieeexplore.ieee.org
Over the last decade, numerous research efforts have been dedicated to countering
malicious mobile applications. Given its market share, Android OS has been the primary …

Deep learning-powered malware detection in cyberspace: a contemporary review

A Redhu, P Choudhary, K Srinivasan, TK Das - Frontiers in Physics, 2024 - frontiersin.org
This article explores deep learning models in the field of malware detection in cyberspace,
aiming to provide insights into their relevance and contributions. The primary objective of the …

Android Malware Detection Method Based on CNN and DNN Bybrid Mechanism

S Dong, L Shu, S Nie - IEEE Transactions on Industrial …, 2024 - ieeexplore.ieee.org
With the continuous upgrading and development of malware attack methods, traditional
detection methods have shown a series of serious problems such as low classification …

Protecting Android Devices from Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

EC Bayazit, OK Sahingoz, B Dogan - IEEE Access, 2023 - ieeexplore.ieee.org
Advancements in microelectronics have increased the popularity of mobile devices like
cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad …

Attribution classification method of APT malware based on multi-feature fusion

J Zhang, S Liu, Z Liu - PloS one, 2024 - journals.plos.org
In recent years, with the development of the Internet, the attribution classification of APT
malware remains an important issue in society. Existing methods have yet to consider the …

[HTML][HTML] Radon transform based malware classification in cyber-physical system using deep learning

R Alguliyev, R Aliguliyev, L Sukhostat - Results in Control and Optimization, 2024 - Elsevier
The development of cyber-physical systems entails the growth and diversity of malware,
which increases the scale of cybersecurity threats. Attackers use malicious software to …

Android malware detection framework based on sensitive opcodes and deep reinforcement learning

J Yang, C Gui - Journal of Intelligent & Fuzzy Systems, 2024 - content.iospress.com
Malware attack is a growing problem on the Android mobile platform due to its popularity
and openness. Although numerous malware detection approaches have been proposed, it …

[PDF][PDF] Intelligent Systems with Applications

KS Roy, T Ahmed, PB Udas, ME Karim, S Majumdar - researchgate.net
Since the advent of malware, it has reached a toll in this world that exchanges billions of
data daily. Millions of people are victims of it, and the numbers are not decreasing as the …

[PDF][PDF] Results in Control and Optimization

R Alguliyev, R Aliguliyev, L Sukhostat - Transfer, 2019 - researchgate.net
The development of cyber-physical systems entails the growth and diversity of malware,
which increases the scale of cybersecurity threats. Attackers use malicious software to …