4.7 Article

Dynamic Prototype Network Based on Sample Adaptation for Few-Shot Malware Detection

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

A Hybrid Deep Network Framework for Android Malware Detection

Hui-Juan Zhu et al.

Summary: Android is increasingly targeted by malicious software due to its popularity and functionality. To enhance the capability of malware detection, we propose a malware detection framework based on deep learning algorithms, which learns effective feature representation to improve detection accuracy. Experimental results show that the framework achieves good performance on two real-world datasets and outperforms other classical methods.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Computer Science, Information Systems

Byte-level malware classification based on markov images and deep learning

Baoguo Yuan et al.

COMPUTERS & SECURITY (2020)

Article Chemistry, Multidisciplinary

ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

Zhijie Tang et al.

APPLIED SCIENCES-BASEL (2020)

Article Computer Science, Information Systems

HYDRA: A multimodal deep learning framework for malware classification

Daniel Gibert et al.

COMPUTERS & SECURITY (2020)

Proceedings Paper Computer Science, Software Engineering

Unsuccessful Story about Few Shot Malware Family Classification and Siamese Network to the Rescue

Yude Bai et al.

2020 ACM/IEEE 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2020) (2020)

Article Computer Science, Information Systems

Multi-Loss Siamese Neural Network With Batch Normalization Layer for Malware Detection

Jinting Zhu et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

MANNWARE: A Malware Classification Approach with a Few Samples Using a Memory Augmented Neural Network

Kien Tran et al.

INFORMATION (2020)

Article Engineering, Multidisciplinary

Malware Detection Based on Deep Learning of Behavior Graphs

Fei Xiao et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2019)

Article Computer Science, Hardware & Architecture

ASSCA: API sequence and statistics features combined architecture for malware detection

Lu Xiaofeng et al.

COMPUTER NETWORKS (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Malware Image Classification Using One-Shot Learning with Siamese Networks

Shou-Ching Hsiao et al.

KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KES 2019) (2019)

Proceedings Paper Computer Science, Information Systems

Transfer Learning for Image-based Malware Classification

Niket Bhodia et al.

PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY (ICISSP) (2019)

Proceedings Paper Computer Science, Hardware & Architecture

Classifying Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network

Jiaqi Yan et al.

2019 49TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2019) (2019)

Proceedings Paper Computer Science, Information Systems

Malware Detection with Malware Images using Deep Learning Techniques

Ke He et al.

2019 18TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS/13TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (TRUSTCOM/BIGDATASE 2019) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Task Agnostic Meta-Learning for Few-Shot Learning

Muhammad Abdullah Jamal et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Article Computer Science, Information Systems

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

Jin-Young Kim et al.

INFORMATION SCIENCES (2018)

Proceedings Paper Computer Science, Interdisciplinary Applications

Malware Detection with Deep Neural Network Using Process Behavior

Shun Tobiyama et al.

PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS (COMPSAC), VOL 2 (2016)