A Machine Learning Approach to Anomaly-Based Detection on Android Platforms (original) (raw)

Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning

International Journal of Advanced Computer Science and Applications, 2020

The increasing number of mobile devices using the Android operating system in the market makes these devices the first target for malicious applications. In recent years, several Android malware applications were developed to perform certain illegitimate activities and harmful actions on mobile devices. In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications. However, the most recent Android malware apps, such as zero-day, cannot be detected through conventional methods that are still based on fixed signatures or identifiers. Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing information to detect the new Android malware apps. This paper presents the basic concepts of Android architecture, Android malware, and permission features utilized as effective malware predictors. Furthermore, a comprehensive review of the existing static, dynamic, and hybrid Android malware detection approaches is presented in this study. More significantly, this paper empirically discusses and compares the performances of six supervised machine learning algorithms, known as K-Nearest Neighbors (K-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), which are commonly used in the literature for detecting malware apps.

Machine learning-assisted signature and heuristic-based detection of malwares in Android devices

Computers & Electrical Engineering, 2018

Malware detection is an important factor in the security of the smart devices. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. In this context, an efficient hybrid framework is presented for detection of malware in Android Apps. The proposed framework considers both signature and heuristic-based analysis for Android Apps. We have reverse engineered the Android Apps to extract manifest files, and binaries, and employed state-of-the-art machine learning algorithms to efficiently detect malwares. For this purpose, a rigorous set of experiments are performed using various classifiers such as SVM, Decision Tree, W-J48 and KNN. It has been observed that SVM in case of binaries and KNN in case of manifest.xml files are the most suitable options in robustly detecting the malware in Android devices. The proposed framework is tested on benchmark datasets and results show improved accuracy in malware detection.

A Study of Android Malware Detection Techniques and Machine Learning

2016

Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine l...

Malware detection in android mobile platform using machine learning algorithms

2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), 2017

Malware has always been a problem in regards to any technological advances in the software world. Thus, it is to be expected that smart phones and other mobile devices are facing the same issues. In this paper, a practical and effective anomaly based malware detection framework is proposed with an emphasis on Android mobile computing platform. A dataset consisting of both benign and malicious applications (apps) were installed on an Android device to analyze the behavioral patterns. We first generate the system metrics (feature vector) from each app by executing it in a controlled environment. Then, a variety of machine learning algorithms: Decision Tree, K Nearest Neighbor, Logistic Regression, Multilayer Perceptron Neural Network, Naive Bayes, Random Forest, and Support Vector Machine are used to classify the app as benign or malware. Each algorithm is assessed using various performance criteria to identify which ones are more suitable to detect malicious software. The results suggest that Random Forest and Support Vector Machine provide the best outcomes thus making them the most effective techniques for malware detection.

Android Malware Detection through Machine Learning Techniques: A Review

International Journal of Online and Biomedical Engineering (iJOE)

The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for ...

A Machine Learning Approach to Android Malware Detection

2012 European Intelligence and Security Informatics Conference, 2012

With the recent emergence of mobile platforms capable of executing increasingly complex software and the rising ubiquity of using mobile platforms in sensitive applications such as banking, there is a rising danger associated with malware targeted at mobile devices. The problem of detecting such malware presents unique challenges due to the limited resources avalible and limited privileges granted to the user, but also presents unique opportunity in the required metadata attached to each application. In this article, we present a machine learningbased system for the detection of malware on Android devices. Our system extracts a number of features and trains a One-Class Support Vector Machine in an offline (off-device) manner, in order to leverage the higher computing power of a server or cluster of servers.

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

Asian Journal of Research in Computer Science

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 w...

MACHINE LEARNING APPROACH TO LEARN AND DETECT MALWARE IN ANDROID

IRJET, 2023

Smartphones have become indispensable in modern life as a result of their extensive use in recent years. New solutions have been developed by users to allow them to keep critical data on their mobile devices. Attackers' main focus, however, is on data related to privacy. As a result, hackers constantly develop new methods to steal data from users' devices. To guarantee the defence of users' confidential information from intruders, several antimalware solutions are created. Based on how they detect malware, we classify a lot of recent antimalware techniques. Our goal is to present a clear and brief overview of malware detection and defence procedures. We provide an ANN and SVM-based technique to identify malicious and good apps in this study.

A Comparison of Features for Android Malware Detection

Proceedings of the SouthEast Conference

With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware. We are able to differentiate between benign and malicious apps by applying a machine learning algorithm. The model that is presented here achieved a classification accuracy of around 80% using permissions and 60% using system calls for a relatively small dataset. In the future, different machine learning algorithms will be examined to see if there is a more suitable algorithm. More features will also be taken into account and the training set will be expanded.

Machine Learning Based Identification for Android Malicious Applications

2018

Mobile malware is rapidly becoming a serious threat. There are many different types of mobile malwares in real world and they depend on the platforms or operating systems which are installed in mobile devices. The Fake Player Trojan is the first malware for android platform that was discovered in 2010. The number of android malicious applications has been consistently rising because android phones are widespread and steadily gaining popularity. Therefore, android malicious applications detection has become a popular research area. We propose a framework to identify different types of android malicious applications by using machine learning approaches in this paper. This framework intends to extract features from android applications and will identify malware application type with better accuracy results. In an evaluation with 1,000 applications, the proposed framework identifies malicious applications with accuracy rate over 90% and few false alarms.