An Efficient Malware Detection Approach Based on Machine Learning Feature Influence Techniques for Resource-Constrained Devices
The growing use of computer resources in modern society makes it extremely vulnerable to several cyber-attacks, including unauthorized access to equipment and computer systems’ manipulation or utter breakdown. Malicious attacks in the form of malware cause significant harm to systems with...
Saved in:
Main Authors: | Subir Panja, Subhash Mondal, Amitava Nag, Jyoti Prakash Singh, Manob Jyoti Saikia, Anup Kumar Barman |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10830491/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MALVADA: A framework for generating datasets of malware execution traces
by: Razvan Raducu, et al.
Published: (2025-05-01) -
Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
by: Yadigar Imamverdiyev, et al.
Published: (2025-01-01) -
Feature Selection and Classification Optimization of Transformer Oil Odor Data With Recursive Feature Elimination Using Grid Search and Cross-Validation
by: Bilge Han Tozlu
Published: (2025-01-01) -
Transformer-based malware detection using process resource utilization metrics
by: Dimosthenis Natsos, et al.
Published: (2025-03-01) -
Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers
by: C. Rajathi, et al.
Published: (2025-01-01)