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

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

(2020) Danish Vasan et al.   COMPUTERS & SECURITY

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

(2020) Eslam Amer et al.   COMPUTERS & SECURITY

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

(2020) Ferhat Ozgur Catak et al.   PeerJ Computer Science

MalDAE: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics

(2019) Weijie Han et al.   COMPUTERS & SECURITY

Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis

(2018) Ming Fan et al.   IEEE Transactions on Information Forensics and Security

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

(2017) Zahra Salehi et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Malware detection based on deep learning algorithm

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

Feature-Chain Based Malware Detection Using Multiple Sequence Alignment of API Call

(2016) Hyun-Joo KIM et al.   IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

Feature-Chain Based Malware Detection Using Multiple Sequence Alignment of API Call

(2016) Hyun-Joo KIM et al.   IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

Three-phase behavior-based detection and classification of known and unknown malware

(2015) Ying-Dar Lin et al.   Security and Communication Networks

A Novel Approach to Detect Malware Based on API Call Sequence Analysis

(2015) Youngjoon Ki et al.   International Journal of Distributed Sensor Networks

Enhancing the detection of metamorphic malware using call graphs

(2014) Ammar Ahmed E. Elhadi et al.   COMPUTERS & SECURITY

Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software

(2012) Alexis Mylonas et al.   COMPUTERS & SECURITY