HELIX: malware detection and classification through temporal sequence modeling of system-call group behavior
- 24 August 2026
- journal article
- research article
- Published by Springer Nature in Journal of Computer Virology and Hacking Techniques
- Vol. 22 (1) , 79
Abstract
No abstract availableKeywords
This publication has 44 references indexed in Scilit:
- A new deep boosted CNN and ensemble learning based IoT malware detectionComputers & Security, 2023
- Behavior-based detection and classification of malicious software utilizing structural characteristics of group sequence graphsJournal of Computer Virology and Hacking Techniques, 2022
- Detection and classification of malicious software utilizing Max-Flows between system-call groupsJournal of Computer Virology and Hacking Techniques, 2022
- Sub-curve HMM: A malware detection approach based on partial analysis of API call sequencesComputers & Security, 2020
- Evolution of Malware and Its Detection Techniques2019
- Clustal Omega for making accurate alignments of many protein sequencesProtein Science, 2017
- Improvement of malware detection and classification using API call sequence alignment and visualizationCluster Computing, 2017
- A graph-based model for malware detection and classification using system-call groupsJournal of Computer Virology and Hacking Techniques, 2016
- Similarity analysis of DNA sequences based on codon usageChemical Physics Letters, 2008
- Identification of common molecular subsequencesJournal of Molecular Biology, 1981