Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper.
Query: Smartphone malware detection: From a survey towards taxonomy
Behavioral detection of malware: from a survey towards an established taxonomy
Effective and Explainable Detection of Android Malware Based on Machine Learning Algorithms
DroidLegacy: Automated Familial Classification of Android Malware
HEMD: a highly efficient random forest-based malware detection framework for Android
Profiling mobile malware behaviour through hybrid malware analysis approach
An Evaluation Of N-gram System Call Sequence In Mobile Malware Detection
DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data
DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling
“Andromaly”: a behavioral malware detection framework for android devices
Obfuscation-Resilient , Efficient , and Accurate Detection and Family Identification of Android Malware
Machine learning aided Android malware classification
DroidScribe: Classifying Android Malware Based on Runtime Behavior
An Adaptive Dataset for the Evaluation of Android Malware Detection Techniques
AntiMalDroid: An Efficient SVM-Based Malware Detection Framework for Android
Intelligent Hybrid Approach for Android Malware Detection based on Permissions and API Calls
Detecting Malware for Android Platform: An SVM-Based Approach
DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model
RanDroid: Android malware detection using random machine learning classifiers
DroidDolphin: a dynamic Android malware detection framework using big data and machine learning
Euphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
A Taxonomy of Cyber Attacks on SCADA Systems
Ether: malware analysis via hardware virtualization extensions
Data-driven 3D Voxel Patterns for object category recognition
adaptive cruise control design based on fuzzy - pid .
Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System