Mobile Device Profiling and Intrusion Detection Using Smart Batteries

Buennemeyer, T.K.; Nelson, Theresa; Clagett, L.M.; Dunning, John P.; Marchany, Randy; Tront, Joseph G.

doi:10.1109/hicss.2008.319

Cited by 67 publications

(24 citation statements)

References 8 publications

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“…K. Buennemeyer et al [3], in their work introduce capabilities developed for Battery -Sensing Intrusion protection system (B-SIPS) that raises an alert when an abnormal current change occurs. They have developed a Correlation Intrusion Detection Engine (CIDE) that provides power profiling for mobile devices and a correlated view of B-SIPS and Snort alerts.…”

Section: Recent Workmentioning

confidence: 99%

Prevention of Malicious Attack on Smart Phones using Amendment Capture Service

FouzulHidhaya¹,

Geetha²

2014

IJCA

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Sensitive data and information is at a greater risk due to the exploitation of the smart phones by Malwares. Malwares that once targeted the computers are now fully designed to extract all the information possible from the mobile phones. This work focuses on preventing the malicious attack on the mobile phones by using Amendment capture service, which checks the smart phone periodically or when a change has taken place and executes the DtKFc algorithm to detect the malicious applications.

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Section: Recent Workmentioning

confidence: 99%

Prevention of Malicious Attack on Smart Phones using Amendment Capture Service

FouzulHidhaya¹,

Geetha²

2014

IJCA

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“…There exist several attempts to detect mobile malware by monitoring the power consumption patterns of applications [26][27][28]. For example, if our backdoor application consumes an excessive amount of battery power deviated from typical power usage, this would make our backdoor detected easily by antimalware systems based on power consumption monitoring.…”

Section: Power Consumptionmentioning

confidence: 99%

Design and Analysis of Push Notification-Based Malware on Android

Hyun

Cho

et al. 2018

Security and Communication Networks

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Establishing secret command and control (C&C) channels from attackers is important in malware design. This paper presents design and analysis of malware architecture exploiting push notification services as C&C channels. The key feature of the push notification-based malware design is remote triggering, which allows attackers to trigger and execute their malware by push notifications. The use of push notification services as covert channels makes it difficult to distinguish this type of malware from other normal applications also using the same services. We implemented a backdoor prototype on Android devices as a proofof-concept of the push notification-based malware and evaluated its stealthiness and feasibility. Our malware implementation effectively evaded the existing malware analysis tools such as 55 antimalware scanners from VirusTotal and SandDroid. In addition, our backdoor implementation successfully cracked about 98% of all the tested unlock secrets (either PINs or unlock patterns) in 5 seconds with only a fraction (less than 0.01%) of the total power consumption of the device. Finally, we proposed several defense strategies to mitigate push notification-based malware by carefully analyzing its attack process. Our defense strategies include filtering subscription requests for push notifications from suspicious applications, providing centralized management and access control of registration tokens of applications, detecting malicious push messages by analyzing message contents and characteristic patterns demonstrated by malicious push messages, and detecting malware by analyzing the behaviors of applications after receiving push messages.

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“…Concretely, in June of the same year, the first malware specially written for Symbian OS platform was discovered [15]. After the infection success carried out by Cabir malware and its variants [19], researchers proposed approaches and Due to the lack of smartphone malware patterns by that time, most of anomaly detection techniques used the battery power consumption as the main malware detection system feature [16,17,18]. These techniques were based on checking and monitoring mobile phones power consumption and comparing them with the normal power consumption pattern to detect anomalies.…”

Section: Literature Reviewmentioning

confidence: 99%

Malware Defense in Mobile Network using Dynamic Analysis of Android Application

Ashwini¹

2015

IJRITCC

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Abstract-Today Android has the biggest market share as compared to other operating system for smart phone. As users are continuously increasing day by day the Security is one of the main concerns for Smartphone users. As the features and power of Smartphone are increase, so that they has their vulnerability for attacks by Malwares. But the android is the operating system which is more secure than any other operating systems available for Smart phones. The Android operating system has very few restrictions for developers and it will increase the security risk for end users. I am proposing an android application which is able to perform dynamic analysis on android program. To perform this analysis i have to deploy the android application, In this proposed system I am going to deploy android application on a cloud. This application executes automatically without any human interaction. It automatically detects malware by using pattern matching algorithm. If malware get detected then user get inform that particular application is malicious and restrict the user from installing application.

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Mobile Device Profiling and Intrusion Detection Using Smart Batteries

Cited by 67 publications

References 8 publications

Prevention of Malicious Attack on Smart Phones using Amendment Capture Service

Prevention of Malicious Attack on Smart Phones using Amendment Capture Service

Design and Analysis of Push Notification-Based Malware on Android

Malware Defense in Mobile Network using Dynamic Analysis of Android Application

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