Android Malware Detection: Feature Update Using Incremental Learning
Approach: Further Investigation of UFILAZAKARIA SAWADOGO ˚: ,JEAN-MARIE DEMBELE: , GERVAIS MENDY ˚ ,SAMUEL OUYA ˚ .
˚ LITA(Laboratory of Computer Science, Telecommunications and Applications), Cheikh Anta Diop University of Dakar, Senegal: LANI(Laboratory of Numerical Analysis and Computer Science), Gaston Berger University,Senegal{sawadogo.zakaria,jean-marie.dembele}@ugb.edu.sn, {gervais.mendy,samuel.ouya}@ucad.edu.sn

Abstract—Several kinds of mobile applications are available onplatforms offering various services to users. Both malware andgood applications are found in software repositories, which is amajor cybersecurity problem. To address this problem, machinelearning approaches have been proposed in the literature for thedetection of malware in general and malicious Android applications in particular. But obfuscation techniques are used by somedevelopers to hide malicious applications, which implies the needto update Android malware detection models.In the literature
few works are focused on updating features. Our contribution istherefore an incremental learning approach capable of detecting
Android malware by taking into account both static and dynamicfeatures. We propose through the UFILA approach an updated
feature set for Android malware detection and classification byadding new features. We evaluated 13 classification algorithms
and selected the four most effective algorithms to implement
our approach. The results obtained by our approach outperformseveral malware detection approaches in the literature. Our
approach allows to detect malicious android applications basedon static and dynamic features with feature updating. It also
allows to efficiently classify the different classes.
Index Terms—imbalanced dataset, Android malware detection,Malware classification, Artificial intelligence, Machine learning.

Zakaria SAWADOGO received
his Master II degree in software
engineering and information systems from Joseph Ki-Zerbo University in Burkina Faso. He iscurrently a researcher in cybersecurity including artificial intelligence and is affiliated with
the Laboratoire d’Informatique,de Télécommunications et Applications ( LITA) of the University
Cheikh Anta Diop of Dakar and the Laboratoire d’Analyse
Numérique et d’Informatique ( LANI) of the University Gaston Berger of Saint Louis. His research interests include
mobile security, system security and artificial intelligence. Heis a member of the IEEE.
Pr. Gervais MENDY is a
researcher-scientist at the ESP
polytechnic school at UCAD university where he was head of theIT department from 2012 to 2016.
Holder of a PhD in Computer
Science from Paris-Sud XI University. His research interests arein Computer Combinatory, Social Network Analysis, Internet ofThings (IoT), Artificial Intelligence and IT Security.He is amember of the LITA Laboratory of UCAD.
Pr. Jean-Marie DEMEBELE isAssimilated Professor in Computer Science at the UGB, ex Director of the UFR SAT and member of the UMI UMMISCO Senegal. He has to his credit, several
scientific publications in international peer-reviewed journals. His
research interests include artificial
intelligence, agent-based modeling, dynamical systems, evolutionary algorithms, genetic regulatory networks, machinelearning
Pr. Samuel Ouya is currently
the director of the LITA laboratory at the UCAD. He was from2013 to May 2017 the first Director of Infrastructure and Information System of the first virtual university of Senegal (UVS).
Holder of a thesis in Applied
Mathematics from the Gaston Berger University of SaintLouis Senegal and a Telecommunications Thesis from theUCAD university in Dakar-Senegal, he is interested in he isinterested in Applications of innovative telecom services tovirtual organizations.