☆ 4.5 Article

CTIMD: Cyber threat intelligence enhanced malware detection using API call sequences with parameters

COMPUTERS & SECURITY (2024)

Related references

Note: Only part of the references are listed.

Article Computer Science, Theory & Methods

CruParamer: Learning on Parameter-Augmented API Sequences for Malware Detection

Xiaohui Chen et al.

Summary: In this paper, we propose CruParamer, a deep neural network based malware detection approach that learns from sequences of parameter-augmented APIs. The method evaluates parameter sensitivity, labels APIs based on sensitivity, and encodes API sequences to characterize their relationship. Experimental results show that CruParamer achieves superior performance and robustness in malware detection.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2022)

Add to Collection

Article Computer Science, Information Systems

FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis

Younghoon Ban et al.

Summary: This study focuses on deep learning-based familial analysis of Android malware by examining different features and their effectiveness in representing malicious behaviors. The evaluation on a real-world malware dataset of 28,179 samples reveals the contribution of different features to the performance of familial analysis. With all features combined, the study achieves a high accuracy and micro F1-score.

IEEE ACCESS (2022)

Add to Collection

Article Computer Science, Information Systems

A novel deep framework for dynamic malware detection based on API sequence intrinsic features

Ce Li et al.

Summary: This paper proposes a novel malware detection framework using deep learning models to capture and combine intrinsic features of API sequences. Experimental results show that the proposed model achieves high accuracy and F1-score on a large real dataset, outperforming baseline models.

COMPUTERS & SECURITY (2022)

Add to Collection

Article Computer Science, Information Systems

DMalNet: Dynamic malware analysis based on API feature engineering and graph learning

Ce Li et al.

Summary: In this study, we propose a novel malware analysis framework called DMalNet, which extracts semantic features from API names and arguments, converts the relationship between API calls into the structural information of a graph, and achieves accurate malware detection and classification.

COMPUTERS & SECURITY (2022)

Add to Collection

Article Computer Science, Information Systems

Malware Detection by Control-Flow Graph Level Representation Learning With Graph Isomorphism Network

Yun Gao et al.

Summary: This study proposes a malware classification system based on Control-Flow Graph (CFG) and Graph Isomorphism Network (GIN) using machine learning methods to process large-scale data. Experimental results show that the method achieves high accuracy and AUC in malware detection.

IEEE ACCESS (2022)

Add to Collection

Article Computer Science, Information Systems

A dynamic Windows malware detection and prediction method based on contextual understanding of API call sequence

Eslam Amer et al.

COMPUTERS & SECURITY (2020)

Add to Collection

Article Computer Science, Information Systems

Recurrent neural network for detecting malware

Sudan Jha et al.

COMPUTERS & SECURITY (2020)

Add to Collection

Article Computer Science, Information Systems

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)

Add to Collection

Article Automation & Control Systems

CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains

Alejandro Martin et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)

Add to Collection

Article Automation & Control Systems

MAAR: Robust features to detect malicious activity based on API calls, their arguments and return values

Zahra Salehi et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2017)

Add to Collection

Article Computer Science, Theory & Methods

A Survey on Malware Detection Using Data Mining Techniques

Yanfang Ye et al.

ACM COMPUTING SURVEYS (2017)

Add to Collection

Article Computer Science, Information Systems

Automatic analysis of malware behavior using machine learning

Konrad Rieck et al.

JOURNAL OF COMPUTER SECURITY (2011)

Add to Collection

Article Computer Science, Information Systems

A general definition of malware

Simon Kramer et al.

JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES (2010)

Add to Collection

Article Computer Science, Information Systems

SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging

Yanfang Ye et al.

JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES (2009)

Add to Collection

Article Computer Science, Information Systems

A scalable multi-level feature extraction technique to detect malicious executables

Mohammad M. Masud et al.

INFORMATION SYSTEMS FRONTIERS (2008)

Add to Collection

Article Computer Science, Information Systems

An intelligent PE-malware detection system based on association mining

Yanfang Ye et al.

JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES (2008)

Add to Collection

© Peeref 2019-2024. All rights reserved.