A comprehensive survey on deep learning based malware detection techniques

M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

Cybersecurity data science: an overview from machine learning perspective

IH Sarker, ASM Kayes, S Badsha, H Alqahtani… - Journal of Big …, 2020 - Springer
In a computing context, cybersecurity is undergoing massive shifts in technology and its
operations in recent days, and data science is driving the change. Extracting security …

Robust intelligent malware detection using deep learning

R Vinayakumar, M Alazab, KP Soman… - IEEE …, 2019 - ieeexplore.ieee.org
Security breaches due to attacks by malicious software (malware) continue to escalate
posing a major security concern in this digital age. With many computer users, corporations …

Deep learning based Sequential model for malware analysis using Windows exe API Calls

FO Catak, AF Yazı, O Elezaj, J Ahmed - PeerJ computer science, 2020 - peerj.com
Malware development has seen diversity in terms of architecture and features. This
advancement in the competencies of malware poses a severe threat and opens new …

[PDF][PDF] A survey on malware analysis techniques: Static, dynamic, hybrid and memory analysis

R Sihwail, K Omar, KAZ Ariffin - Int. J. Adv. Sci. Eng. Inf. Technol, 2018 - core.ac.uk
The threats malware pose to the people around the world are increasing rapidly. A software
that sneaks to your computer system without your knowledge with a harmful intent to disrupt …

API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques

P Maniriho, AN Mahmood, MJM Chowdhury - Journal of Network and …, 2023 - Elsevier
This paper presents API-MalDetect, a new deep learning-based automated framework for
detecting malware attacks in Windows systems. The framework uses an NLP-based encoder …

An adaptive multi-layer botnet detection technique using machine learning classifiers

RU Khan, X Zhang, R Kumar, A Sharif, NA Golilarz… - Applied Sciences, 2019 - mdpi.com
In recent years, the botnets have been the most common threats to network security since it
exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been …

A method for windows malware detection based on deep learning

X Huang, L Ma, W Yang, Y Zhong - Journal of Signal Processing Systems, 2021 - Springer
As the Internet rapidly develops, the types and quantity of malware continue to diversify and
increase, and the technology of evading security software is becoming more and more …

[PDF][PDF] Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures.

M Alazab, S Venkatraman, PA Watters, M Alazab - AusDM, 2011 - academia.edu
Zero-day or unknown malware are created using code obfuscation techniques that can
modify the parent code to produce offspring copies which have the same functionality but …

Profiling and classifying the behavior of malicious codes

M Alazab - Journal of Systems and Software, 2015 - Elsevier
Malware is a major security threat confronting computer systems and networks and has
increased in scale and impact from the early days of ICT. Traditional protection mechanisms …