Using a Subtractive Center Behavioral Model to Detect Malware
In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. This paper proposes a sub...
Main Authors: | Ömer Aslan, Refik Samet, Ömer Özgür Tanrıöver |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2020-01-01
|
Series: | Security and Communication Networks |
Online Access: | http://dx.doi.org/10.1155/2020/7501894 |
Similar Items
-
A Comprehensive Review on Malware Detection Approaches
by: Omer Aslan, et al.
Published: (2020-01-01) -
Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment
by: Omer Aslan, et al.
Published: (2021-01-01) -
A New Malware Classification Framework Based on Deep Learning Algorithms
by: Omer Aslan, et al.
Published: (2021-01-01) -
Combat Mobile Evasive Malware via Skip-Gram-Based Malware Detection
by: Alper Egitmen, et al.
Published: (2020-01-01) -
A Manipulation Prevention Model for Blockchain-Based E-Voting Systems
by: Ruhi Taş, et al.
Published: (2021-01-01)