scholarly journals DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis

2020 ◽  
Vol 10 (11) ◽  
pp. 3978
Author(s):  
Xin Su ◽  
Lijun Xiao ◽  
Wenjia Li ◽  
Xuchong Liu ◽  
Kuan-Ching Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Behavior Analysis ◽  
High Performance ◽  
Behavioral Characteristics ◽  
Random Forest Algorithm ◽  
Sensitive Data ◽  
Android Malware ◽  
Security Issues ◽  
Android Applications ◽  
Security Incidents

Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of malicious Android applications have become a hot topic. However, the behavioral portrait of Android malware that can depict the behavior of Android malware is not approached in previous literature. To fill this gap, we propose DroidPortrait, an Android malware multi-dimensional behavioral portrait construction approach. We take the behavior of Android malware as an entry point and extract an informative behavior dataset that includes static and dynamic behavior from Android malware. Next, aiming at Android malware that contains different kinds of behaviors, a behavioral tag is defined then combined with a machine learning (ML) algorithm to implement the correlation of these behavioral tags. Android malware behavioral portrait architecture based on behavior analysis and its design is investigated, as also an optimized random forest algorithm is conceived then combined with Android malware behavioral portrait to detect Android malware. The evaluation findings indicate the DroidPortrait can depict behavioral characteristics of Android malware comprehensive and detect them with high performance.

scholarly journals Factors that Influence the Android Apps Permissions

2016 ◽  
Vol 1 (2) ◽  
pp. 59
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Iqram Mahmud
Keyword(s):  
Not Given ◽  
Sensitive Data ◽  
Security Issues ◽  
Android Apps ◽  
Android Applications ◽  
Android Market ◽  
Malicious Apps ◽  
The Right ◽  
User Ratings ◽  
Average User

The Android Market is the official (and primary) storefor Android applications. The Market provides users with average user ratings, user reviews, descriptions, screenshots,and permissions to help them select applications. Generally, prior to installation of the apps, users need to agree on the permissions requested by the apps, they are not given any other option. Essentially, users may not aware on some security issues that may arise from the permissions. Some apps request the right to manipulate sensitive data, such as GPS location, photos, calendar, contact, email and files. In this paper, we explain the sources of sensitive data, what the malicious apps can do to the data, and apply the empirical software engineering analysis to find the factors that could potentially influence the permissions in Android apps. In addition, we also highlight top ten most implemented permissions in Android apps and also analyse the permissions for the apps categories in Android.

Privacy Preserving Machine Learning and Deep Learning Techniques

2020 ◽  
pp. 222-235
Author(s):  
Divya Asok ◽  
Chitra P. ◽  
Bharathiraja Muthurajan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Privacy ◽  
Differential Privacy ◽  
Digital Data ◽  
Sensitive Data ◽  
Security Issues ◽  
Private Data ◽  
Learning Techniques ◽  
Machine Learning Applications

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.

Machine Learning Based Malicious Android Application Detection

2020 ◽  
Vol 17 (8) ◽  
pp. 3468-3472
Author(s):  
S. L. Jany Shabu ◽  
Rohan Loganathan Reddy ◽  
V. Maria Anu ◽  
L. Mary Gladence ◽  
J. Refonaa
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Learning Algorithms ◽  
Identity Theft ◽  
Machine Learning Algorithms ◽  
Android Application ◽  
Sensitive Data ◽  
Android Applications ◽  
Malicious Apps

The ultimate aim of the project is to improve permission for detecting the malicious android mobile application using machine learning algorithms. In recent years, the usages of smartphones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application users, some intruders are creating malicious android applications as a tool to steal the sensitive data and identity theft/fraud mobile bank, mobile wallets. There are so many malicious applications detection tools and software are available. But an effectiveness of malicious applications detection tools is the need for the hour. They are needed to tackle and handle new complex malicious apps created by intruder or hackers.

scholarly journals A Neural Network Based Hybrid Approach for Analysing and Detecting Malware Threat in Android Applications

2018 ◽  
Vol 7 (4.6) ◽  
pp. 410
Author(s):  
Hetal Suresh ◽  
Joseph Raymond V
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Static Analysis ◽  
Mobile Phones ◽  
Recurrent Neural Network ◽  
Hybrid Approach ◽  
Sensitive Data ◽  
Social Accounts ◽  
Android Applications ◽  
Do So

Mobile phones has become very integral part in our day to day life. In the digitalized world most of our day to day activities rely on mobile phone like banking activities, wallet payments, credentials, social accounts etc. Our system works in such a way that if there is an advantage to a technology there also exists a disadvantage. Every users have all their private and sensitive data in their mobile phones and download random applications from different platforms like play store, App store etc. There is a huge possibility that the applications downloaded are malicious applications. The existing system provides a solution for detection of such applications with the help of antivirus which has pre-built signatures that can be used to obtain an already existing malware which can be modified and manipulated by the hacker if they tend to do so. In this project, our purpose is to identify the malicious applications using Machine learning. By combining both static analysis and dynamic analysis we can use a Hybrid approach for analysing and detecting malware threats in android applications using Recurrent Neural Network (RNN). The main aim of this project will be to ensure that the application installed is benign, if it is not, it should block such applications and notify the user. 

scholarly journals Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Priyanka Dixit ◽  
Rashi Kohli ◽  
Angel Acevedo-Duque ◽  
Romel Ramon Gonzalez-Diaz ◽  
Rutvij H. Jhaveri
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
High Performance ◽  
Optimization Techniques ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Complex Data ◽  
Efficient Computation ◽  
Security Issues

Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

scholarly journals Convolutional Neural Network for Classification of Android Applications Represented as Grayscale Images

2019 ◽  
Vol 8 (12S) ◽  
pp. 835-843
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Performance ◽  
Smart Phone ◽  
Convolution Neural Network ◽  
Android Malware ◽  
Android Applications ◽  
Conventional Solution ◽  
Grayscale Images

A rapid dissemination of Android operating system in smart phone market has resulted in an exponential growth of threats to mobile applications. Various studies have been carried out in academia and industry for the identification and classification of malicious applications using machine learning and deep learning algorithms. Convolution Neural Network is a deep learning technique which has gained popularity in speech and image recognition. The conventional solution for identifying Android malware needs learning based on pre-extracted features to preserve high performance for detecting Android malware. In order to reduce the efforts and domain expertise involved in hand-feature engineering, we have generated the grayscale images of AndroidManifest.xml and classes.dex files which are extracted from the Android package and applied Convolution Neural Network for classifying the images. The experiments are conducted on a recent dataset of 1747 malicious Android applications. The results indicate that classes.dex file gives better results as compared to the AndroidManifest.xml and also demonstrate that model performs better as the image become larger.

Deep Neural Network-Based Android Malware Detection (D-AMD)

2021 ◽  
pp. 161-175
Author(s):  
Sangeetha D. ◽  
Umamaheswari S. ◽  
Rakshana Gopalakrishnan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Personal Information ◽  
Probabilistic Neural Network ◽  
Android Malware ◽  
Static And Dynamic Analysis ◽  
Android Malware Detection ◽  
Batch Normalization ◽  
Android Applications ◽  
Security Incidents

Android is an operating system that presently has over one billion active users for their mobile devices in which a copious quantity of information is available. Mobile malware causes security incidents like monetary damages, stealing of personal information, etc., when it's deep-rooted into the target devices. Since static and dynamic analysis of Android applications to detect the presence of malware involves a large amount of data, deep neural network is used for the detection. Along with the introduction of batch normalization, the deep neural network becomes effective, and also the time taken by the training process is less. Probabilistic neural network (PNN), convolutional neural network (CNN), and recurrent neural network (RNN) are also used for performance analysis and comparison. Deep neural network with batch normalization gives the highest accuracy of 94.35%.

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.

Privacy Preserving Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 1621-1634
Author(s):  
Divya Asok ◽  
Chitra P. ◽  
Bharathiraja Muthurajan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Privacy ◽  
Differential Privacy ◽  
Digital Data ◽  
Sensitive Data ◽  
Security Issues ◽  
Private Data ◽  
Learning Techniques ◽  
Machine Learning Applications

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.

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