Related references
Note: Only part of the references are listed.Ensemble machine learning approaches for webshell detection in Internet of things environments
Binbin Yong et al.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES (2022)
Deep Learning and Visualization for Identifying Malware Families
Guosong Sun et al.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING (2021)
Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection
Robertas Damasevicius et al.
ELECTRONICS (2021)
Windows PE Malware Detection Using Ensemble Learning
Nureni Ayofe Azeez et al.
INFORMATICS-BASEL (2021)
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
Danish Vasan et al.
COMPUTERS & SECURITY (2020)
Detecting malware evolution using support vector machines
Mayuri Wadkar et al.
EXPERT SYSTEMS WITH APPLICATIONS (2020)
DeNNeS: deep embedded neural network expert system for detecting cyber attacks
Samaneh Mahdavifar et al.
NEURAL COMPUTING & APPLICATIONS (2020)
Malware detection in industrial internet of things based on hybrid image visualization and deep learning model
Hamad Naeem et al.
AD HOC NETWORKS (2020)
Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features
Maryam Nisa et al.
APPLIED SCIENCES-BASEL (2020)
Android malware detection method based on bytecode image
Yuxin Ding et al.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2020)
Deep Feature Extraction and Classification of Android Malware Images
Jaiteg Singh et al.
SENSORS (2020)
Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm
S. Abijah Roseline et al.
IEEE ACCESS (2020)
MSIC: Malware Spectrogram Image Classification
Ahmad Azab et al.
IEEE ACCESS (2020)
A multi-level deep learning system for malware detection
Wei Zhong et al.
EXPERT SYSTEMS WITH APPLICATIONS (2019)
Robust Intelligent Malware Detection Using Deep Learning
R. Vinayakumar et al.
IEEE ACCESS (2019)
Improvement of malware detection and classification using API call sequence alignment and visualization
Hyunjoo Kim et al.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2019)
Malware identification using visualization images and deep learning
Sang Ni et al.
COMPUTERS & SECURITY (2018)
Recent Progress in Software Security
Edward Amoroso
IEEE SOFTWARE (2018)
Detection of Malicious Code Variants Based on Deep Learning
Zhihua Cui et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
Malware Visualization for Fine-Grained Classification
Jianwen Fu et al.
IEEE ACCESS (2018)
A state-of-the-art survey of malware detection approaches using data mining techniques
Alireza Souri et al.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES (2018)
Automatic Malware Classification via PRICoLBP
Yan Hanbing et al.
CHINESE JOURNAL OF ELECTRONICS (2018)
Densely Connected Convolutional Networks
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Polymorphic Malware Detection Using Sequence Classification Methods
Jake Drew et al.
2016 IEEE SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (SPW 2016) (2016)
The MALICIA dataset: identification and analysis of drive-by download operations
Antonio Nappa et al.
INTERNATIONAL JOURNAL OF INFORMATION SECURITY (2015)
A survey of emerging threats in cybersecurity
Julian Jang-Jaccard et al.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES (2014)
Malware Analysis Using Visualized Image Matrices
KyoungSoo Han et al.
SCIENTIFIC WORLD JOURNAL (2014)
Metamorphic Malware Detection Using Code Metrics
Gerardo Canfora et al.
INFORMATION SECURITY JOURNAL (2014)
Opcode sequences as representation of executables for data-mining-based unknown malware detection
Igor Santos et al.
INFORMATION SCIENCES (2013)
Classification of malware based on integrated static and dynamic features
Rafiqul Islam et al.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS (2013)
Obfuscation: The Hidden Malware
Philip O'Kane et al.
IEEE SECURITY & PRIVACY (2011)
Automatic analysis of malware behavior using machine learning
Konrad Rieck et al.
JOURNAL OF COMPUTER SECURITY (2011)