A Study of Android Malware Detection Techniques and Machine Learning

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 learning algorithms focused on the Android OS

A Study of Android Malware Detection Techniques and Machine Learning

Abstract 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 learning algorithms focused on the Android OS

MACHINE LEARNING APPROACH TO DETECT ANDROID MALWARES

Mobile phone industry is growing at rapid speed .These mobile phones are running on diiferent platform such as JAVA, Android, IOS, Sysmbian and others. Out all these platforms Android cover maximum share amount Smartphone platform. Android platforms supports millions of applications that can be download from various repositories such as google play. These applications are installed and used. The applications present in these repository may be malicious which leady to security problems using these application. In this paper an effective approach has been proposed for detection of the malicious application based on the permission groups. In proposed work, binary classification of applications are carried out into two label i.e. Benign and malicious one. In this developed approach the distinguished features are evaluated and filtered out using features evaluation technique such as Information gain, Gain ratio, Gini Index, Chi-square test. Finally based on the features evaluated the classification is done using supervised machine learning techniques

Challenges and Outlook in Machine Learning-based Malware Detection for Android

Just like in traditional desktop computing, one of the major security issues in mobile computing lies in malicious software. Several recent studies have shown that Android, as today’s most widespread Operating System, is the target of most of the new families of malware. Manually analysing an Android application to determine whether it is malicious or not is a time- consuming process. Furthermore, because of the complexity of analysing an application, this task can only be conducted by highly-skilled—hence hard to come by—professionals. Researchers naturally sought to transfer this process from humans to computers to lower the cost of detecting malware. Machine-Learning techniques, looking at patterns amongst known malware and inferring models of what discriminates malware from goodware, have long been summoned to build malware detectors. The vast quantity of data involved in malware detection, added to the fact that we do not know a priori how to express in technical terms the difference between malware and goodware, indeed makes the malware detection question a seemingly textbook example of a possible Machine- Learning application. Despite the vast amount of literature published on the topic of detecting malware with machine- learning, malware detection is not a solved problem. In this Thesis, we investigate issues that affect performance evaluation and that thus may render current machine learning-based mal- ware detectors for Android hardly usable in practical settings, and we propose an approach to overcome those issues. While the experiments presented in this thesis all rely on feature-sets obtained through lightweight static analysis, several of our findings could apply equally to all Machine Learning-based malware detection approaches. In the first part of this thesis, background information on machine-learning and on malware detection is provided, and the related work is described. A snapshot of the malware landscape in Android application markets is then presented. The second part discusses three pitfalls hindering the evaluation of malware detectors. We show with extensive experiments how validation methodology, History-unaware dataset construction and the choice of a ground truth can heavily interfere with the performance results of malware detectors. In a third part, we present an practical approach to detect Android Malware in real-world settings. We then propose several research paths to get closer to our long term goal of building practical, dependable and predictable Android Malware detectors