Trimming Approach of Robust Clustering for Smartphone Behavioral Analysis
| Autor: | Marc Lemercier, Rida Khatoun, Ali El Attar |
|---|---|
| Přispěvatelé: | Environnement de Réseaux Autonomes (ERA), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Télécom ParisTech, Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Computer science
02 engineering and technology computer.software_genre 01 natural sciences Behavioral analysis 010104 statistics & probability [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] Robustness (computer science) CURE data clustering algorithm 0202 electrical engineering electronic engineering information engineering Malware 020201 artificial intelligence & image processing Trimming [INFO]Computer Science [cs] Data mining 0101 mathematics Cluster analysis computer |
| Zdroj: | 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing (EUC) 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Aug 2014, Milano, Italy. pp.315-320 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Aug 2014, Milano, France. pp.315-320, ⟨10.1109/EUC.2014.54⟩ EUC |
| DOI: | 10.1109/EUC.2014.54⟩ |
| Popis: | International audience; Nowadays, smart phones get increasingly popular which also attracted hackers. With the increasing capabilities of such phones, more and more malicious softwares targeting these devices have been developed. Malwares can seriously damage an infected device within seconds. In this paper, we propose to use the trimming approaches for automatic clustering (trimmed k-means, Tclust) of smartphone's applications. They aim to identify homogenous groups of applications exhibiting similar behavior and allow to handle a proportion of contaminating data to guarantee the robustness of clustering. Then, a clustering-based detection technique is applied to compute an anomaly score for each application, leading to discover the most dangerous among them. Initial experiments results prove the efficiency and the accuracy of the used clustering methods in detecting abnormal smartphone's applications and that with a low false alerts rate. |
| Databáze: | OpenAIRE |
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