{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T03:14:32Z","timestamp":1774408472554,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Western Norway University of Applied Sciences, Norway"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2020,11,27]]},"abstract":"<jats:p>Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application\u2019s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography\u2019s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.<\/jats:p>","DOI":"10.1155\/2020\/8861639","type":"journal-article","created":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T02:20:09Z","timestamp":1606530009000},"page":"1-14","source":"Crossref","is-referenced-by-count":12,"title":["cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing"],"prefix":"10.1155","volume":"2020","author":[{"given":"Afifa","family":"Maryam","sequence":"first","affiliation":[{"name":"Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3933-4273","authenticated-orcid":true,"given":"Usman","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen 5063, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8342-5757","authenticated-orcid":true,"given":"Muhammad","family":"Aleem","sequence":"additional","affiliation":[{"name":"National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-9709","authenticated-orcid":true,"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen 5063, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7503-5086","authenticated-orcid":true,"given":"Muhammad","family":"Arshad Islam","sequence":"additional","affiliation":[{"name":"National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Azhar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology (SIST), Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","first-page":"114","article-title":"Automatically granted permissions in android apps","author":"P. Calciati"},{"key":"2","article-title":"An automated dynamic analysis framework for characterizing android applications","author":"M. K. Alzaylaee","year":"2016"},{"key":"3","first-page":"1","article-title":"DREBIN: effective and explainable detection of android malware in your pocket","author":"D. Arp"},{"key":"4","article-title":"IntelliDroid: a targeted input generator for the dynamic analysis of Android Malware","author":"M. Y. Wong"},{"key":"5","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.procs.2015.03.170","article-title":"A novel approach to detect android malware","volume":"45","author":"S. B. Almin","year":"2015","journal-title":"Procedia Computer Science"},{"issue":"3","key":"6","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1142\/S0218194020500175","article-title":"Appperm analyzer: malware detection system based on android permissions and permission groups","volume":"30","author":"I. A. 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