| Title | A Survey on Mobile Malware Detection Methods using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zadeh Nojoo Kambar, Mina Esmail, Esmaeilzadeh, Armin, Kim, Yoohwan, Taghva, Kazem |
| Conference Name | 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) |
| Keywords | compositionality, Conferences, dynamic malware detection, human factors, iOS Security, machine learning, Malware, Metrics, Mobile communication, mobile malware, mobile security, Operating systems, pubcrawl, resilience, Resiliency, Runtime, telecommunication traffic, Traffic detection |
| Abstract | The prevalence of mobile devices (smartphones) along with the availability of high-speed internet access world-wide resulted in a wide variety of mobile applications that carry a large amount of confidential information. Although popular mobile operating systems such as iOS and Android constantly increase their defenses methods, data shows that the number of intrusions and attacks using mobile applications is rising continuously. Experts use techniques to detect malware before the malicious application gets installed, during the runtime or by the network traffic analysis. In this paper, we first present the information about different categories of mobile malware and threats; then, we classify the recent research methods on mobile malware traffic detection. |
| DOI | 10.1109/CCWC54503.2022.9720753 |
| Citation Key | zadeh_nojoo_kambar_survey_2022 |
A Survey on Mobile Malware Detection Methods using Machine Learning