Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning

Cyber Security in the Age of COVID-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemic

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Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection

(2021) Robertas Damaševičius et al.   Electronics

Catch them alive: A malware detection approach through memory forensics, manifold learning and computer vision

(2021) Ahmet Selman Bozkir et al.   COMPUTERS & SECURITY

Image-Based Malware Classification using Ensemble of CNN Architectures (IMCEC)

(2020) Danish Vasan et al.   COMPUTERS & SECURITY

Adversarial Attacks and Defenses in Deep Learning

(2020) Kui Ren et al.   Engineering

End-to-end malware detection for android IoT devices using deep learning

(2020) Zhongru Ren et al.   Ad Hoc Networks

DeepDetectNet vs RLAttackNet: An adversarial method to improve deep learning-based static malware detection model

(2020) Yong Fang et al.   PLoS One

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

(2020) Maryam Nisa et al.   Applied Sciences-Basel

Ensemble machine learning approaches for webshell detection in Internet of things environments

(2020) Binbin Yong et al.   Transactions on Emerging Telecommunications Technologies

Machine-Learning based analysis and classification of Android malware signatures

(2019) Ignacio Martín et al.   Future Generation Computer Systems-The International Journal of eScience

Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach

(2018) Sen Chen et al.   COMPUTERS & SECURITY

Malware classification using self organising feature maps and machine activity data

(2018) Pete Burnap et al.   COMPUTERS & SECURITY

Big Data Framework for Zero-Day Malware Detection

(2018) Deepak Gupta et al.   CYBERNETICS AND SYSTEMS

Picking on the family: Disrupting android malware triage by forcing misclassification

(2018) Alejandro Calleja et al.   EXPERT SYSTEMS WITH APPLICATIONS

Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders

(2018) Jin-Young Kim et al.   INFORMATION SCIENCES

A state-of-the-art survey of malware detection approaches using data mining techniques

(2018) Alireza Souri et al.   Human-centric Computing and Information Sciences

The arms race: Adversarial search defeats entropy used to detect malware

(2018) Héctor D. Menéndez et al.   EXPERT SYSTEMS WITH APPLICATIONS

Survey of machine learning techniques for malware analysis

(2018) Daniele Ucci et al.   COMPUTERS & SECURITY

Malware detection based on deep learning algorithm

(2017) Ding Yuxin et al.   NEURAL COMPUTING & APPLICATIONS

Automatic malware classification and new malware detection using machine learning

(2017) Liu Liu et al.   Frontiers of Information Technology & Electronic Engineering

Classification of malware based on integrated static and dynamic features

(2012) Rafiqul Islam et al.   JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

Adversarial Machine Learning

(2011) J.D. Tygar   IEEE INTERNET COMPUTING