[PDF][PDF] Ransomware, threat and detection techniques: A review

S Kok, A Abdullah, N Jhanjhi… - Int. J. Comput. Sci. Netw …, 2019 - academia.edu
The popularity of ransomware has created a unique ecosystem of cybercriminals. Therefore,
the objectives of this paper are to provide a thorough understanding of ransomware's threat …

Evaluation metric for crypto-ransomware detection using machine learning

SH Kok, A Azween, NZ Jhanjhi - Journal of Information Security and …, 2020 - Elsevier
Ransomware is a type of malware that blocks access to its victim's resources until a ransom
is paid. Crypto-ransomware is a type of ransomware that blocks access to its victim's files by …

Crypto-ransomware early detection model using novel incremental bagging with enhanced semi-random subspace selection

BAS Al-rimy, MA Maarof, SZM Shaid - Future Generation Computer Systems, 2019 - Elsevier
The irreversible effect is what characterizes crypto-ransomware and distinguishes it from
traditional malware. That is, even after neutralizing the attack, the targeted files remain …

A pseudo feedback-based annotated TF-IDF technique for dynamic crypto-ransomware pre-encryption boundary delineation and features extraction

BAS Al-Rimy, MA Maarof, M Alazab, F Alsolami… - IEEE …, 2020 - ieeexplore.ieee.org
The cryptography employed against user files makes the effect of crypto-ransomware attacks
irreversible even after detection and removal. Thus, detecting such attacks early, ie during …

Redundancy coefficient gradual up-weighting-based mutual information feature selection technique for crypto-ransomware early detection

BAS Al-Rimy, MA Maarof, M Alazab, SZM Shaid… - Future Generation …, 2021 - Elsevier
Crypto-ransomware is a type of malware whose effect is irreversible even after detection and
removal. Thus, early detection is crucial to protect user files from being encrypted and held …

Efficient deep learning models for DGA domain detection

J Namgung, S Son, YS Moon - Security and Communication …, 2021 - Wiley Online Library
In recent years, cyberattacks using command and control (C&C) servers have significantly
increased. To hide their C&C servers, attackers often use a domain generation algorithm …

An empirical evaluation of supervised learning methods for network malware identification based on feature selection

C Manzano, C Meneses, P Leger, H Fukuda - Complexity, 2022 - Wiley Online Library
Malware is a sophisticated, malicious, and sometimes unidentifiable application on the
network. The classifying network traffic method using machine learning shows to perform …

A multi-tier streaming analytics model of 0-day ransomware detection using machine learning

H Zuhair, A Selamat, O Krejcar - Applied Sciences, 2020 - mdpi.com
Desktop and portable platform-based information systems become the most tempting target
of crypto and locker ransomware attacks during the last decades. Hence, researchers have …

Reverse-Engineering Malware

M Omar, LB Gouveia, J Al-Karaki… - … in Developing Nations …, 2022 - igi-global.com
Cyberspace is quickly becoming overwhelmed with ever-evolving malware that breaches all
security defenses and secretly leaks confidential business data. One of the most pressing …

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

SS Hussain, MF Ab Razak… - Journal of Cyber …, 2024 - journals.riverpublishers.com
Globally extensive digital revolutions involved with every process related to human progress
can easily create the critical issues in security aspects. This is promoted due to the important …