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HawkSec: an AI Transformer Solution to Malware Detection in C/C++ Source Codes

Year: 2025

Type: conference-paper

Abstract: The increasing prevalence of sophisticated malware targeting software applications requires robust detection mechanisms, particularly for C/C++ codebases that underpin critical systems. This paper introduces HawkSec, an innovative AI-based model designed to enhance malware detection within C/C++ codes. Using advanced machine learning algorithms and deep neural networks, HawkSec analyzes code patterns and behaviors to identify potential threats with high accuracy of 99.19% and minimal false positives. The model is trained on an extensive dataset that comprises both benign and malicious code samples.

DOI: 10.1109/ic_etc65981.2025.11141236

Authors: Mustapha Khairan Ghliss, Salam Doumiati, Yehya Nasser

Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, IMT Atlantique

Language: English

Topic: Advanced Malware Detection Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4413886269

FWCI:

Cites: 28

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Related to: 10