Immune-Based System to Enhance Malware Detection - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Immune-Based System to Enhance Malware Detection

Manel Jerbi
Slim Bechikh
Lamjed Ben Said
  • Fonction : Auteur
  • PersonId : 967308

Résumé

Malicious apps use various methods to spread viruses, take control of computers and/or IoT devices, and steal sensitive data such as credit card numbers or other personal information. Despite the numerous existing means of intrusion detection, malware code is not easily detectable. The primary issue with current malware detection approaches is their inability to identify novel attacks and obfuscated malware, as they rely on static bases of malware examples, making them susceptible to new unseen malware behaviors. To address this, we propose a new method for malware recognition, which consists of two processes: the first process creates new instances of malware using a memetic algorithm, and the second process detects these new instances of attacks through solid detectors produced by an artificial immune system-based algorithm. Our new malware recognition method has proven its merits through thorough experiments on widely used datasets and evaluation metrics, and has been compared to prominent state-of-the-art methods.
Fichier principal
Vignette du fichier
IEEE CEC.pdf (382.52 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04074893 , version 1 (19-04-2023)

Identifiants

  • HAL Id : hal-04074893 , version 1

Citer

Manel Jerbi, Zaineb Chelly Dagdia, Slim Bechikh, Lamjed Ben Said. Immune-Based System to Enhance Malware Detection. IEEE 2023 Congress on Evolutionary Computation, Jul 2023, Chicago, United States. ⟨hal-04074893⟩
119 Consultations
101 Téléchargements

Partager

Gmail Facebook X LinkedIn More