Understanding and detecting wake lock misuses for Android applications

Liu, Yepang; Xu, Chang; Cheung, Shing-Chi; Terragni, Valerio

doi:10.1145/2950290.2950297

Cited by 56 publications

(31 citation statements)

References 33 publications

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“…We employed a specific type of denominated , as it is able to turn off the screen while keeping the CPU and sensors awake. In this way, the WakeLock manager component ensures that the smartphone will be kept awake when accessing to GPS and processing location data [55]. …”

Section: Android Implementationmentioning

confidence: 99%

“…This module is aware of the limitations of smartphone’s battery, so that after notifying the detected stay point to the subscribed mobile app, it also notifies to the Policy executor the end of location processing for the immediate release of the active . The proper management of is essential for the continuous operation of mobile apps, and their unexpected retention and missuses are common causes of a poor management of energy resources [55]. Another critical aspect, described later on, is that location updates might not be obtained under specific circumstances.…”

Section: Android Implementationmentioning

confidence: 99%

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Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Pérez-Torres¹,

Torres-Huitzil²,

Galeana-Zapién³

2016

Sensors

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The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

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Section: Android Implementationmentioning

confidence: 99%

Section: Android Implementationmentioning

confidence: 99%

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Pérez-Torres¹,

Torres-Huitzil²,

Galeana-Zapién³

2016

Sensors

View full text Add to dashboard Cite

show abstract

“…We also collected a number of real-world Android applications from the internet. Resources of these les include apkpure [1] with 5400 samples, 700 samples from 360.com and 13K commercial applications from the HKUST Wake Lock Misuse Detection Project [19]. In total, we have 19100 real-world applications.…”

Section: Metricsmentioning

confidence: 99%

Android Malware Detection Based on Factorization Machine

Mills

Niu

et al. 2019

IEEE Access

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As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data the mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security. Traditional methods like signature-based routines are unable to protect users from the ever-increasing sophistication and rapid behavior changes in new types of Android malware. erefore, a great deal of e ort has been made recently to use machine learning models and methods to characterize and generalize the malicious behavior pa erns of mobile apps for malware detection.In this paper, we propose a novel and highly reliable classi er for Android Malware detection based on a Factorization Machine architecture and the extraction of Android app features from manifest les and source code. Our results indicate that the numerical feature representation of an app typically results in a long and highly sparse vector and that the interactions among di erent features are critical to revealing malicious behavior pa erns. A er performing an extensive performance evaluation, our proposed method achieved a test result of 100.00% precision score on the DREBIN dataset and 99.22% precision score with only 1.10% false positive rate on the AMD dataset. ese metrics match the performance of state-of-the-art machine-learning-based Android malware detection methods and several commercial antivirus engines with the bene t of training up to 50 times faster.

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“…An energy bug-in contrast to the traditional programming bugs producing incorrect/unwanted resultsspecifically leads to reduced battery life of battery-driven devices [56]. The sources of energy bugs can be of different types: no sleep bug-result of waking up the CPU, but not putting back to sleep mode [35]; loop bug-waiting for an event to happen, and thus periodically inspecting changes in a variable [43]; and tail energy leak-observed with some hardware components that do not stop drawing power immediately after job completion [11,44].…”

Section: Energy Bugsmentioning

confidence: 99%

“…Wake locks are frequently used by Android developers to continue operations even when a device goes to sleep status [35]. Programmers write code to acquire wake lock before starting a task and release the lock after the task completion.…”

Section: Energy Optimization and Guidelinesmentioning

confidence: 99%

Did I make a mistake? Finding the impact of code change on energy regression

Chowdhury

Gil

Romansky

et al. 2017

Preprint

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Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet many researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data one can make more accurate energy models but this data was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. We address these concerns by automatically generating test cases to drive applications undergoing energy measurement. Automatic test generation allows a model to be continuously improved in a model building process whereby applications are extracted, tests are generated, energy is measured and combined with instrumentation to train a grander big-data model of software energy consumption. This continuous process has allowed the authors to generate and extract measurements from hundreds of applications in order to build accurate energy models capable of predicting the energy consumption of applications without end-user energy measurement. We clearly show that models built from more applications reduce energy modelling error.

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Understanding and detecting wake lock misuses for Android applications

Cited by 56 publications

References 33 publications

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Android Malware Detection Based on Factorization Machine

Did I make a mistake? Finding the impact of code change on energy regression

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