2026-06-02T04:16:21Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72652023-03-17T12:41:39Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Sedano, Javier author González González, Silvia . author Chira, Camelia author Herrero Cosío, Álvaro author Corchado, Emilio author Villar, José Ramón author 2017-02 In recent years, mobile devices such as smartphones, tablets and wearables have become the new paradigm of user–computer interaction. The increasing use and adoption of such devices is also leading to an increased number of potential security risks. The spread of mobile malware, particularly on popular and open platforms such as Android, has become a major concern. This paper focuses on the bad-intentioned Android apps by addressing the problem of selecting the key features of such software that support the characterization of such malware. The accurate detection and characterization of this software is still an open challenge, mainly due to its ever-changing nature and the open distribution channels of Android apps. Maximum relevance minimum redundancy and evolutionary algorithms guided by information correlation measures have been applied for feature selection on the well-known Android Malware Genome (Malgenome) dataset, attaining interesting results on the most informative features for the characterization of representative families of existing Android malware. 1367-0751 http://hdl.handle.net/10259/7265 10.1093/jigpal/jzw046 1368-9894 Feature selection Evolutionary computation Max-relevance min-redundancy criteria Information correlation coefficient Android Malware Key features for the characterization of Android malware families