Energy inefficiency diagnosis for Android applications: a literature review

Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.

Investigation of Android Malware Using Deep Learning Approach

In recent days the usage of android smartphones has increased exten-sively by end-users.There are several applications in different categories bank-ing/finance,social engineering,education,sports andfitness,and many more applications.The android stack is more vulnerable compared to other mobile plat-forms like IOS,Windows,or Blackberry because of the open-source platform.In the Existing system,malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware.The attackers target the victim with various attacks like adware,backdoor,spyware,ransomware,and zero-day exploits and create threat hunts on the day-to-day basics.In the existing approach,there are various tradi-tional machine learning classifiers for building a decision support system with limitations such as low detection rate and less feature selection.The important contents taken for building model from android applications like Intent Filter,Per-mission Signature,API Calls,and System commands are taken from the manifestfile.The function parameters of various machine and deep learning classifiers like Nave Bayes,k-Nearest Neighbors(k-NN),Support Vector Machine(SVM),Ada Boost,and Multi-Layer Perceptron(MLP)are done for effective results.In our pro-posed work,we have used an unsupervised learning multilayer perceptron with multiple target labels and built a model with a better accuracy rate compared to logistic regression,and rank the best features for detection of applications and clas-sify as malicious or benign can be used as threat model by online antivirus scanners.

Novel Colorimetry-Based Android Smartphone Application for Detection of Ibuprofen

Sensitive and fast detection of ibuprofen( Ibu) in an aquatic environment often requires costly, time-consuming and sophisticated techniques. To tackle those limitations,a novel android smartphone application was developed based on a colorimetric analysis method using unmodified gold nanoparticles( AuNPs)aptamer probes to quantitatively detect Ibu. Under optimal conditions,it could detect Ibu as low as 0. 25 ng/mL with high selectivity. The determination of Ibu in real water samples was also carried out to confirm the practicability of the application.

Understanding and Detecting Inefficient Image Displaying Issues in Android Apps

Mobile applications(apps for short)often need to display images.However,inefficient image displaying(IID)issues are pervasive in mobile apps,and can severely impact app performance and user experience.This paper first establishes a descriptive framework for the image displaying procedures of IID issues.Based on the descriptive framework,we conduct an empirical study of 216 real-world IID issues collected from 243 popular open-source Android apps to validate the presence and severity of IID issues,and then shed light on these issues’characteristics to support research on effective issue detection.With the findings of this study,we propose a static IID issue detection tool TAPIR and evaluate it with 243 real-world Android apps.Encouragingly,49 and 64 previously-unknown IID issues in two different versions of 16 apps reported by TAPIR are manually confirmed as true positives,respectively,and 16 previously-unknown IID issues reported by TAPIR have been confirmed by developers and 13 have been fixed.Then,we further evaluate the performance impact of these detected IID issues and the performance improvement if they are fixed.The results demonstrate that the IID issues detected by TAPIR indeed cause significant performance degradation,which further show the effectiveness and efficiency of TAPIR.