“Andromaly”: a behavioral malware detection framework for android devices

By Asaf Shabtai, Uri Kanonov, Yuval Elovici, Chanan Glezer, Yael Weiss. Journal of Intelligence and Information Systems, 2012

825 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/507f4c6b3d25f346293d7532fb30dd61ee29dd84

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Citations in the corpus

A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, neutral): “[34], called Andromaly which also uses machine learning algorithms.”
DroidMat: Android Malware Detection through Manifest and API Calls Tracing (2012, background): “Efficiency: It does not need to dynamically investigate the Android application behavior from the sandbox or by emulation, which saves the cost in environment deployment and manual efforts in investigation.”
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android (2013, background): “Andromaly [24] continuously monitors various system metrics to detect suspicious activities through applying supervised anomaly detection techniques.”
Deep Android Malware Detection (2017, background): “…Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.”
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “The malware detection approaches can be classified into two categories; static analysis based detection [2-19] and dynamic analysis based detection [20-24].”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Most malware detection methods are anomaly based [Shabtai et al. 2012] (e.g., deﬁning normal and abnormal attribute sets), misuse based [Yajin Zhou 2013] (e.g., identifying speciﬁc malicious actions), or signature based (e.g. semantic or bytecode) [Crussell et al. 2012].”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, methodology): “Andromaly [40] is an anomaly detector for Android devices based on ondevice monitoring and evaluated different feature selection and machine learning algorithms.”
A Survey on Security for Mobile Devices (2013, methodology): “Andromaly [115] is a general and modular framework for detecting malware on Android smartphones using a supervised anomaly detection technique.”
A Comprehensive Review on Malware Detection Approaches (2020, background): “Host-based malware detection system for Android is presented in [58], [101].”
Machine Learning for Android Malware Detection Using Permission and API Calls (2013, background): “In this section, we briefly describe Android application structure with focus on the important files.”
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, methodology): “Many security solutions use a fingerprinting method for the purpose of app recognition [15, 57, 62].”
DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Arp et al. (2014) ; Dini et al. (2012) ; Peiravian and Zhu (2013) ; Rasthofer et al. (2014) ; Shabtai et al. (2012) ; Yerima et al. (2015b, 2016b) are examples of published research that apply machine learning techniques to detect zero-day Android malware.”
Challenges and Opportunities in Edge Computing (2016, background): “There are attempts to mitigate the energy challenge by performing analytics on the edge device [16], [17], [18].”