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 …

Static analysis of information systems for IoT cyber security: a survey of machine learning approaches

I Kotenko, K Izrailov, M Buinevich - Sensors, 2022 - mdpi.com
Ensuring security for modern IoT systems requires the use of complex methods to analyze
their software. One of the most in-demand methods that has repeatedly been proven to be …

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 …

[HTML][HTML] Ransomware detection using random forest technique

BM Khammas - ICT Express, 2020 - Elsevier
Nowadays, the ransomware became a serious threat challenge the computing world that
requires an immediate consideration to avoid financial and moral blackmail. So, there is a …

An investigation into the performances of the state-of-the-art machine learning approaches for various cyber-attack detection: A survey

T Ige, C Kiekintveld, A Piplai - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
In this research, we analyzed the suitability of each of the current state-of-the-art machine
learning models for various cyberattack detection from the past 5 years with a major …

Combining multiple feature-ranking techniques and clustering of variables for feature selection

AU Haq, D Zhang, H Peng, SU Rahman - Ieee Access, 2019 - ieeexplore.ieee.org
Feature selection aims to eliminate redundant or irrelevant variables from input data to
reduce computational cost, provide a better understanding of data and improve prediction …

Feature selection using a machine learning to classify a malware

M Al-Kasassbeh, S Mohammed, M Alauthman… - Handbook of Computer …, 2020 - Springer
Generally, malware has come to be known as one of the biggest threats, so malware is a
program which operates malicious actions and steals information, to specifically identify it as …

Leveraging support vector machine for opcode density based detection of crypto-ransomware

J Baldwin, A Dehghantanha - Cyber threat intelligence, 2018 - Springer
Ransomware is a significant global threat, with easy deployment due to the prevalent
ransomware-as-a-service model. Machine learning algorithms incorporating the use of …

Malware detection based on opcode frequency

A Yewale, M Singh - 2016 International Conference on …, 2016 - ieeexplore.ieee.org
Malware is a computer program or a piece of software that is designed to penetrate and
detriment computers without owner's permission. There are different malware types such as …

Transformer oil quality assessment using random forest with feature engineering

MEA Senoussaoui, M Brahami, I Fofana - Energies, 2021 - mdpi.com
Machine learning is widely used as a panacea in many engineering applications including
the condition assessment of power transformers. Most statistics attribute the main cause of …