Android Security: A Survey of Issues, Malware Penetration, and Defenses

By Parvez Faruki, Ammar Bharmal, Vijay Laxmi, Vijay Ganmoor, Manoj Singh Gaur, Mauro Conti, Muttukrishnan Rajarajan. IEEE Communications Surveys and Tutorials, 2015

571 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/996fcf5bf7b7145fdcfb89ee6997efb6d6d0c210

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Citations in the corpus

A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “That is, an overview of Android malware penetration and defense strategies was presented in Reference [32] along with ML-based detection tools with 117 references; and four ML algorithm families, including SVM, NB, perceptron, and DNN for Android malware detection were briefly reviewed in Reference…”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “A detailed survey of various types of machine learning based IDS for mobile devices particularly mobile phones can be found here [36].”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, unknown): “, Android OS [64]–[66].”
Static analysis of android apps: A systematic literature review (2017, background): “[181] present another survey mainly focusing on a set of known Android malware detection approaches/tools, e.”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “The rapid proliferation of smartphones has provided malicious actors to disguise malicious applications and malware as benign utility mobile applications and publish them through applications to store without being detected [57,58].”
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, result): “This can be seen by comparing our survey to parts of [20], in which Faruki et al.”
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019, unknown): “and robustness [1], [6], [7].”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “to their relatively simple and open architecture and application programming interface [76], [77].”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “without thorough security checks [112], [114].”