scholarly journals Analysis of Permissions Correlation for Android Apps Using Statistical SVD Approach

2020 ◽  
Vol 8 (2) ◽  
pp. 10-19
Author(s):  
Zon Nyein Nway
Keyword(s):  
Private Information ◽  
Statistical Technique ◽  
Mobile App ◽  
Third Party ◽  
Risk Level ◽  
Android Malware ◽  
Android Apps ◽  
Android Applications ◽  
Value Decomposition ◽  
Almost All

Nowadays, almost all the users use Android applications in their smart phones for various reasons Since Android is free operating system, android-apps can be easily downloaded via biggest open app stores and third-party mobile app markets. But these applications were not guaranteed whether these are malware apps or not by legitimate organizations. As mobile phones are glued with most of the people, malware applications threaten all of them for their private information. So, the work of analysis for the apps is very important. The proposed system analyzes the correlation patterns of app’s permissions that must be used in all android apps by developers by using a statistical technique called singular value decomposition (SVD). The analysis phase uses the numbers of malware samples 50 to 300 from https://www.kaggle.com/goorax/static-analysis-of-android-malware-of-2017. The proposed system evaluates the risk level (High, Medium, and Low) of Android applications based on the correlation patterns of permissions. The system accuracy is 85% for both malware and goodware applications. Nowadays, almost all the users use Android applications in their smart phones for various reasons Since Android is free operating system, android-apps can be easily downloaded via biggest open app stores and third-party mobile app markets. But these applications were not guaranteed whether these are malware apps or not by legitimate organizations. As mobile phones are glued with most of the people, malware applications threaten all of them for their private information. So, the work of analysis for the apps is very important. The proposed system analyzes the correlation patterns of app’s permissions that must be used in all android apps by developers by using a statistical technique called singular value decomposition (SVD). The analysis phase uses the numbers of malware samples 50 to 300 from https://www.kaggle.com/goorax/static-analysis-of-android-malware-of-2017. The proposed system evaluates the risk level (High, Medium, and Low) of Android applications based on the correlation patterns of permissions. The system accuracy is 85% for both malware and goodware applications.

scholarly journals Deep Learning Based Static Analysis of Malwares in Android Applications

2021 ◽  
Author(s):  
Nivedha K ◽  
Indra Gandhi K ◽  
Shibi S ◽  
Nithesh V ◽  
Ashwin M
Keyword(s):  
Operating System ◽  
Deep Learning ◽  
Open Source ◽  
Text Messages ◽  
Third Party ◽  
Android Application ◽  
Android Malware ◽  
Android Apps ◽  
User Data ◽  
Android Applications

Android is a widely distributed mobile operating system developed especially for mobile devices with touch screens. It is an open source, Google-distributed Linux-based mobile operating system. Since Android is open source, it enables Android devices to be targeted effectively by malware developers. Third-party markets do not search for malicious applications in their databases, so installing Android Application Packages (APKs) from these uncontrolled market places is often risky. Without user’s notice, these malware infected applications gain access to private user data, send text messages that costs the user, or hide malware apk file inside another application. The total number of new samples of Android malware amounted to 482,579 per month as of March 2020. In this paper deep learning approach that focuses on malware detection in android apps to protect data on user devices. We use different static features that are present in an Android application for the implementation of the proposed system. The system extracts various static features and gives them to the classifier for deep learning and shows the results. This proposed system will assist users in checking applications that are not downloaded from the official market.

scholarly journals Detection and Prevention of Malware in Android Operating System

2021 ◽  
Vol 40 (4) ◽  
pp. 847-859
Author(s):  
Kashif Ali Dahri ◽  
Muhammad Saleem Vighio ◽  
Baqar Ali Zardari
Keyword(s):  
Operating System ◽  
Private Information ◽  
Malicious Code ◽  
The Internet ◽  
End User ◽  
Android Malware ◽  
Novel Approach ◽  
User Data ◽  
Android Applications ◽  
Android Operating System

The Internet is not safe anymore, malware can be discovered anywhere on the Internet. The risk of malware has increased also due to the increasing popularity and use of Smartphones and their underlying cost-free applications. With its great market share, the Android operating system has become a prime target for malware developers. When an Android phone is injected with a malware, it may result in compromising the privacy of the user by stealing sensitive and private information like contacts, ids, passwords, photos, call records, and so on. Compared to any other Android-based application category, games are the most preferred zone for attackers, due to the high interest of users in game applications. When an end user downloads a game, which is injected with malicious code, user data is infected without bringing in the knowledge of the user. Though, there still are not sufficient protection mechanisms or guidelines stated for end user against Android malware, this study offers a novel approach to detect Android malware in order to ensure the safe usage of Android applications. The advantage of this approach is its ability to utilize Android manifest files for the detection of malware. The availability of manifest file in every Android application makes this approach applicable to all Android applications. It can also be considered as a lightweight method for malware detection, and its efficiency is experimentally confirmed by testing and comparing the results of 50 Android games samples. Experiments are carried out using the Android Package Kit (APK) tools, and based on the experiments, different kinds of malware identification and prevention guidelines have been proposed for the safe and secure usage of the Android operating system.

Runtime-Based Behavior Dynamic Analysis System for Android Malware Detection

2013 ◽  
Vol 756-759 ◽  
pp. 2220-2225 ◽  
Author(s):  
Luo Xu Min ◽  
Qing Hua Cao
Keyword(s):  
Dynamic Analysis ◽  
Private Information ◽  
Malware Detection ◽  
Third Party ◽  
Android Malware ◽  
Android Malware Detection ◽  
Malicious Behavior ◽  
Result Show ◽  
Android Market ◽  
Analysis System

The most serious threats for Android users is come from application, However, the market lack a mechanism to validate whether these applications are malware or not. So, malware maybe leak users private information, malicious deductions for send premium SMS, get root privilege of the Android system and so on. In the traditional method of malware detection, signature is the only basis. It is far enough. In this paper, we propose a runtime-based behavior dynamic analysis for Android malware detection. The new scheme can be implemented as a system. We analyze 350 applications come from third-party Android market, the result show that our system can effectively detect unknown malware and the malicious behavior of malware.

scholarly journals A detailed study on risk assessment of mobile app permissions

2017 ◽  
Vol 7 (1.1) ◽  
pp. 297
Author(s):  
D Naga Malleswari ◽  
A Dhavalya ◽  
V Divya Sai ◽  
K Srikanth
Keyword(s):  
Risk Assessment ◽  
Private Information ◽  
Mobile Applications ◽  
Mobile Application ◽  
Personal Information ◽  
Mobile App ◽  
Assessment Model ◽  
Privacy Concern ◽  
Proposed Model ◽  
Android Applications

Mobile phone have user’s personal and private information. When mobile applications have the permission to access to this information they may leak it to third parties without user’s consent for their own benefits. As users are not aware of how their personal information would be used once applications are installed and permissions are granted, this raises a potential privacy concern. Therefore, there is a need for a risk assessment model that can intimate the users about the threats the mobile application poses to the user's private information. We propose an approach that helps in increasing user’s awareness of the privacy risk involved with granting permissions to Android applications. The proposed model focuses on the requested permissions of the application and determines the risk based on the permission set asked and gives a risk score.

scholarly journals Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2880
Author(s):  
Altyeb Taha ◽  
Omar Barukab ◽  
Sharaf Malebary
Keyword(s):  
Difficult Problem ◽  
Third Party ◽  
Fuzzy Integral ◽  
User Privacy ◽  
Dynamic Nature ◽  
Android Malware ◽  
Android Apps ◽  
Multiple Classifiers ◽  
Malware Classification ◽  
Security Checks

One of the most commonly used operating systems for smartphones is Android. The open-source nature of the Android operating system and the ability to include third-party Android apps from various markets has led to potential threats to user privacy. Malware developers use sophisticated methods that are intentionally designed to bypass the security checks currently used in smartphones. This makes effective detection of Android malware apps a difficult problem and important issue. This paper proposes a novel fuzzy integral-based multi-classifier ensemble to improve the accuracy of Android malware classification. The proposed approach utilizes the Choquet fuzzy integral as an aggregation function for the purpose of combining and integrating the classification results of several classifiers such as XGBoost, Random Forest, Decision Tree, AdaBoost, and LightGBM. Moreover, the proposed approach utilizes an adaptive fuzzy measure to consider the dynamic nature of the data in each classifier and the consistency and coalescence between each possible subset of classifiers. This enables the proposed approach to aggregate the classification results from the multiple classifiers. The experimental results using the dataset, consisting of 9476 Android goodware apps and 5560 malware Android apps, show that the proposed approach for Android malware classification based on the Choquet fuzzy integral technique outperforms the single classifiers and achieves the highest accuracy of 95.08%.

scholarly journals Consciousness about Mobile App Permissions

2019 ◽  
Vol 8 (4) ◽  
pp. 11384-11386
Keyword(s):  
Mobile Phones ◽  
Mobile Apps ◽  
Mobile App ◽  
Negative Consequences ◽  
Android Apps ◽  
The People ◽  
Almost All ◽  
Lack Of Knowledge

This paper gives us the attentiveness on opening files from unknown sources as sometimes it may cause damage to our mobile phones. In almost all the mobile apps after downloading it will ask some of the permissions to allow clicking for the allow button only we can able to access anything in that application otherwise we are unable to use all the features of that application. Many popular android apps including Facebook messenger, WhatsApp, Skype, Twitter, Share it, Instagram and other party apps get user permission after the installation. By allowing these permissions they can recording with the phone audio and video at any time, they can see contacts and modifying the USB storage contents(files). Lack of knowledge and awareness about permissions to the people may cause significant negative consequences. This research evaluates effectiveness of a demo app with visual ques to increase permissions awareness and avoid negative consequences.

scholarly journals A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

2021 ◽  
pp. 44-60
Author(s):  
Suhaib Jasim Hamdi ◽  
Naaman Omar ◽  
Adel AL-zebari ◽  
Karwan Jameel Merceedi ◽  
Abdulraheem Jamil Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Malware Detection ◽  
Personal Digital Assistants ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Apps ◽  
Digital World ◽  
Android Malware Detection ◽  
Android Applications

Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.

scholarly journals RAPID-Risk Assessment of Android Permission and Application Programming Interface (API) Call for Android Botnet

2018 ◽  
Vol 7 (4.15) ◽  
pp. 49 ◽  
Author(s):  
Zubaile Abdullah ◽  
Madihah Mohd Saudi
Keyword(s):  
Risk Assessment ◽  
Application Programming Interface ◽  
Assessment Method ◽  
Risk Level ◽  
Android Apps ◽  
Risk Assessment Method ◽  
Application Programming ◽  
Android Applications ◽  
Android Permission ◽  
Google Play

Android applications may pose risks to smartphone users. Most of the current security countermeasures for detecting dangerous apps show some weaknesses. In this paper, a risk assessment method is proposed to evaluate the risk level of Android apps in terms of confidentiality (privacy), integrity (financial) and availability (system). The proposed research performs mathematical analysis of an app and returns a single easy to understand evaluation of the app’s risk level (i.e., Very Low, Low, Moderate, High, and Very High). These schemes have been tested on 2488 samples coming from Google Play and Android botnet dataset. The results show a good accuracy in both identifying the botnet apps and in terms of risk level. 

scholarly journals Detecting Colluding Inter-App Communication in Mobile Environment

2020 ◽  
Vol 10 (23) ◽  
pp. 8351
Author(s):  
Rosangela Casolare ◽  
Fabio Martinelli ◽  
Francesco Mercaldo ◽  
Antonella Santone
Keyword(s):  
Model Checking ◽  
Mobile Devices ◽  
Private Information ◽  
End Users ◽  
Home Automation ◽  
Mobile Environment ◽  
Data Support ◽  
Bank Transfer ◽  
Android Applications

The increase in computing capabilities of mobile devices has, in the last few years, made possible a plethora of complex operations performed from smartphones and tablets end users, for instance, from a bank transfer to the full management of home automation. Clearly, in this context, the detection of malicious applications is a critical and challenging task, especially considering that the user is often totally unaware of the behavior of the applications installed on their device. In this paper, we propose a method to detect inter-app communication i.e., a colluding communication between different applications with data support to silently exfiltrate sensitive and private information. We based the proposed method on model checking, by representing Android applications in terms of automata and by proposing a set of logic properties to reduce the number of comparisons and a set of logic properties automatically generated for detecting colluding applications. We evaluated the proposed method on a set of 1092 Android applications, including different colluding attacks, by obtaining an accuracy of 1, showing the effectiveness of the proposed method.

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