A Survey of Android Malware Detection with Deep Neural Models

التفاصيل البيبلوغرافية
العنوان: A Survey of Android Malware Detection with Deep Neural Models
المؤلفون: Jun Zhang, Yang Xiang, Surya Nepal, Lei Pan, Junyang Qiu, Wei Luo
المصدر: ACM Computing Surveys. 53:1-36
بيانات النشر: Association for Computing Machinery (ACM), 2020.
سنة النشر: 2020
مصطلحات موضوعية: General Computer Science, Artificial neural network, Computer science, business.industry, Deep learning, Feature extraction, Big data, 02 engineering and technology, Semantics, Data science, Field (computer science), Theoretical Computer Science, Obfuscation (software), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Android (operating system), business
الوصف: Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber security research. Deep learning models have many advantages over traditional Machine Learning (ML) models, particularly when there is a large amount of data available. Android malware detection or classification qualifies as a big data problem because of the fast booming number of Android malware, the obfuscation of Android malware, and the potential protection of huge values of data assets stored on the Android devices. It seems a natural choice to apply DL on Android malware detection. However, there exist challenges for researchers and practitioners, such as choice of DL architecture, feature extraction and processing, performance evaluation, and even gathering adequate data of high quality. In this survey, we aim to address the challenges by systematically reviewing the latest progress in DL-based Android malware detection and classification. We organize the literature according to the DL architecture, including FCN, CNN, RNN, DBN, AE, and hybrid models. The goal is to reveal the research frontier, with the focus on representing code semantics for Android malware detection. We also discuss the challenges in this emerging field and provide our view of future research opportunities and directions.
تدمد: 1557-7341
0360-0300
DOI: 10.1145/3417978
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::3db62802fdbec37d1ef04d4ae5ca347b
https://doi.org/10.1145/3417978
رقم الانضمام: edsair.doi...........3db62802fdbec37d1ef04d4ae5ca347b
قاعدة البيانات: OpenAIRE
الوصف
تدمد:15577341
03600300
DOI:10.1145/3417978