Security Relevant Methods of Android's API Classification: A Machine Learning Empirical Evaluation

Lightweight, Effective Detection and Characterization of Mobile Malware Families

(2022) Karim Elish et al.   IEEE TRANSACTIONS ON COMPUTERS

OLP++: An online local classifier for high dimensional data

(2022) Mariana A. Souza et al.   Information Fusion

Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT

(2020) Rahim Taheri et al.   IEEE Transactions on Industrial Informatics

Android Malware Detection Using Complex-Flows

(2019) Feng Shen et al.   IEEE TRANSACTIONS ON MOBILE COMPUTING

Static Identification of Injection Attacks in Java

(2019) Fausto Spoto et al.   ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS

Deep Metric Learning: A Survey

(2019) Kaya et al.   Symmetry-Basel

Similarity-based Android malware detection using Hamming distance of static binary features

(2019) Rahim Taheri et al.   Future Generation Computer Systems-The International Journal of eScience

Dynamic classifier selection: Recent advances and perspectives

(2018) Rafael M.O. Cruz et al.   Information Fusion

DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

(2018) Hui-Juan Zhu et al.   NEUROCOMPUTING

Android Malware Detection via Graphlet Sampling

(2018) Tianchong Gao et al.   IEEE TRANSACTIONS ON MOBILE COMPUTING

Static analysis of android apps: A systematic literature review

(2017) Li Li et al.   INFORMATION AND SOFTWARE TECHNOLOGY

High accuracy android malware detection using ensemble learning

(2015) Suleiman Y. Yerima et al.   IET Information Security

Android based malware detection using a multifeature collaborative decision fusion approach

(2015) Shina Sheen et al.   NEUROCOMPUTING

META-DES: A dynamic ensemble selection framework using meta-learning

(2015) Rafael M.O. Cruz et al.   PATTERN RECOGNITION

FlowDroid

(2014) Steven Arzt et al.   ACM SIGPLAN NOTICES

Dynamic selection of classifiers—A comprehensive review

(2014) Alceu S. Britto et al.   PATTERN RECOGNITION

An instance level analysis of data complexity

(2013) Michael R. Smith et al.   MACHINE LEARNING

LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs

(2012) Paulo R. Cavalin et al.   PATTERN RECOGNITION

From dynamic classifier selection to dynamic ensemble selection

(2007) Albert H.R. Ko et al.   PATTERN RECOGNITION