Android Malware Detection Using Category Based Machine Learning Classifiers
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Android Malware Detection Using Category-based Machine Learning Classifiers
"Android malware growth has been increasing dramatically along with increasing of the diversity and complicity of their developing techniques. Machine learning techniques are the current methods to model patterns of static features and dynamic behaviors of Android malware. Whereas the accuracy rates of the classifiers increase with increasing the quality of the features, we relate between the apps' features and the features that are needed to deliver the category's functionality. Differently, our classification approach defines legitimate static features for benign apps under a specific category as opposite to identifying malicious patterns. We utilize the features of the top rated apps in a specific category to learn a malware detection classifier for the given category. Android apps stores organize apps into different categories; For example, Google play store organizes apps into 26 categories such as: Health and Fitness, News and Magazine, Music and Audio, etc. Each category has its distinct functionality which means the apps under a specific category are similar in their static and dynamic features. In general, benign apps under a certain category tend to share a common set of features. On the contrary, malicious apps tend to request abnormal features, less or more than what are common for the category that they belong to. This study proposes category-based machine learning classifiers to enhance the performance of classification models at detecting malicious apps under a certain category. The intensive machine learning experiments proved that category-based classifiers report a remarkable higher average performance compared to non-category based."--Abstract.
Android Malware Detection using Machine Learning
Author: ElMouatez Billah Karbab
language: en
Publisher: Springer Nature
Release Date: 2021-07-10
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Proceedings of the Second International Conference on the Future of ASEAN (ICoFA) 2017 – Volume 2
This book examines how business, the social sciences, science and technology will impact the future of ASEAN. Following the ASEAN VISION 2020, it analyses the issues faced by ASEAN countries, which are diverse, while also positioning ASEAN as a competitive entity through partnerships. On the 30th anniversary of ASEAN, all ASEAN leaders agreed to the establishment of the ASEAN VISION 2020, which delineates the formation of a peaceful, stable and dynamically developed region while maintaining a community of caring societies in Malaysia, Indonesia, Singapore, Brunei, Vietnam, Thailand, the Philippines, Myanmar, Laos and Cambodia. In keeping with this aspiration, Universiti Teknologi MARA Perlis took the initial steps to organise conferences and activities that highlight the role of the ASEAN region. The Second International Conference on the Future of ASEAN (ICoFA) 2017 was organised by the Office of Academic Affairs, Universiti Teknologi MARA Perlis, to promote more comprehensive integration among ASEAN members. This book, divided into two volumes, offers a useful guide for all those engaged in research on business, the social sciences, science and technology. It will also benefit researchers worldwide who want to gain more knowledge about ASEAN countries