[HTML][HTML] 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 …

Performance comparison and current challenges of using machine learning techniques in cybersecurity

K Shaukat, S Luo, V Varadharajan, IA Hameed, S Chen… - Energies, 2020 - mdpi.com
Cyberspace has become an indispensable factor for all areas of the modern world. The
world is becoming more and more dependent on the internet for everyday living. The …

A survey on machine learning techniques for cyber security in the last decade

K Shaukat, S Luo, V Varadharajan, IA Hameed… - IEEE …, 2020 - ieeexplore.ieee.org
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

Machine learning algorithm for malware detection: Taxonomy, current challenges, and future directions

NZ Gorment, A Selamat, LK Cheng, O Krejcar - IEEE Access, 2023 - ieeexplore.ieee.org
Malware has emerged as a cyber security threat that continuously changes to target
computer systems, smart devices, and extensive networks with the development of …

Malware detection using machine learning algorithms for windows platform

A Hussain, M Asif, MB Ahmad, T Mahmood… - … and Applications: ICITA …, 2022 - Springer
Windows is a popular Graphical User Interface-based Operating System that provides
services like storage, run third-party software, play videos, network connection, etc. The …

Features engineering to differentiate between malware and legitimate software

AY Daeef, A Al-Naji, AK Nahar, J Chahl - Applied Sciences, 2023 - mdpi.com
Malware is the primary attack vector against the modern enterprise. Therefore, it is crucial for
businesses to exclude malware from their computer systems. The most responsive solution …

Malware Detection With Subspace Learning-Based One-Class Classification

HH Al-Khshali, M Ilyas, F Sohrab, M Gabbouj - IEEE Access, 2024 - ieeexplore.ieee.org
Detecting malware is crucial for ensuring the security of computer systems. Traditional
machine learning models face challenges in effectively detecting malware, mainly due to the …

Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware

IMM Matin - MULTINETICS, 2023 - jurnal.pnj.ac.id
Random forest is one of the popular machine learning algorithms used for classification
tasks. In malware detection tasks, random forest can help identify malware with good …

A framework for collecting and analysis PE malware using modern honey network (MHN)

IMM Matin, B Rahardjo - … Conference on Cyber and IT Service …, 2020 - ieeexplore.ieee.org
Nowadays, Windows is an operating system that is very popular among people, especially
users who have limited knowledge of computers. But unconsciously, the security threat to …

[HTML][HTML] Lightweight and robust malware detection using dictionaries of api calls

AY Daeef, A Al-Naji, J Chahl - Telecom, 2023 - mdpi.com
Malware in today's business world has become a powerful tool used by cyber attackers. It
has become more advanced, spreading quickly and causing significant harm. Modern …