Abstract
Smartphones and mobile tablets are rapidly becoming essential in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are intermingled with a large number of benign apps in Android markets that seriously threaten Android security. The botnet is an example of using good technologies for bad intentions. A botnet is a collection of Internet-connected devices, each of which is running one or more bots. The Bot devices include PCs, Internet of Things, mobile devices, etc. Botnets can be used to perform Distributed Denial of Service (DDoS attack), steal data, send spam and allow the attacker access to the device and its connection. To ensure the security of mobile devices, malwares have to be resolved. Malware analysis can be carried out using techniques like static, dynamic, behavioural, hybrid and code analysis. In this chapter, several machine learning techniques and classifiers are used to categorize mobile botnet detection.
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Abbreviations
- DDoS:
-
Distributed Denial of Service
- CCTree:
-
Categorical Clustering Tree
- APK:
-
Android Package
- SVM:
-
Support Vector Machine
- ELM:
-
Extreme Learning Machine
- SLFN:
-
Single-Layer Feedforward Neural Network
- CNN:
-
Convolutional Neural Network
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Selvaganapathy, S.G., Sadasivam, G.S., N, H.P., N, R., M, D., Karthik, K. (2020). Android Malware Detection. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_73
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DOI: https://doi.org/10.1007/978-3-030-24051-6_73
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