Detecting energy-greedy anomalies and mobile malware variants

By Hahnsang Kim, Joshua Smith, Kang G. Shin. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2008

319 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f59dd54b9cfc46f73035ce40023c366a5e0b1c4e

How researchers see this paper

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In Computer Networks and Communications

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

A Survey on Security for Mobile Devices (2013, background): “Some studies for preventing this class of attacks are proposed in [97, 59].”
DroidMat: Android Malware Detection through Manifest and API Calls Tracing (2012, methodology): “The applied data for our experiments are introduced in Sec IV-A.”
Collaborative Security (2015, background): “In [Dagon et al. 2004; Nash et al. 2005; Racic et al. 2006; Kim et al. 2008], battery life has been proven as a prominent shortcoming that the attackers likely use to make the device unavailable.”
A Machine Learning Approach to Android Malware Detection (2012, background): “Several approaches [11], [16], [18] monitor the power usage of applications, and report anomolous consumption.”
Self-constructive high-rate system energy modeling for battery-powered mobile systems (2011, methodology): “Our solution, Sesame, leverages the possibility of self power measurement through the smart battery interface and employs a suite of novel techniques to achieve accuracy and rate much higher than that of the smart battery interface.”
The Evolution of Android Malware and Android Analysis Techniques (2017, background): “Several studies monitor the hardware status for abnormal behavior through app power signatures [Kim et al. 2008] and power/CPU consumption [Buennemeyer et al. 2008; Nash et al. 2005; Jacoby 2004].”
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets (2012, background): “Malware detection onmobile devices The second area of related work includes systems [29, 30, 33, 43, 45, 49, 52] to detect malware on mobile devices.”
Soundcomber: A Stealthy and Context-Aware Sound Trojan for Smartphones (2011, unknown): “[7] present a similar approach.”
FM 99.9, Radio Virus: Exploiting FM Radio Broadcasts for Malware Deployment (2013, methodology): “We underline that the proposed attack detection techniques, like the one introduced in [31], would not be effective against our novel approach to stealthily attack any device equipped with RDS capabilities.”
“Andromaly”: a behavioral malware detection framework for android devices (2012, background): “This enables to provide richer detection capabilities, not relying merely on the standard call records (Emm 2006), or power consumption patterns (Kim et al. 2008; Buennemeyer et al. 2008; Nash et al. 2005; Jacoby and Davis 2004).”
FPGA-Based Remote Power Side-Channel Attacks (2018, background): “[16] showed that embedded power monitors can be used to detect malware in mobile systems.”