Mobile malware is rapidly becoming a serious threat. There are many different types of mobile mal... more 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.
It is essential to solve linear equations in various fields such as electronic circuit simulation... more It is essential to solve linear equations in various fields such as electronic circuit simulation, structural programming, etc. If the matrix of a linear system is strictly diagonally dominant, iterative methods can be used. Even though the iterative methods are simple, they often require many computations. In this environment, parallel computing techniques are useful for solving numerical problems. This system analyses the performance of iterative methods in parallel computing and how mobile agents fit in parallel computing have been explored. Mobile agents are autonomous computer programs that can migrate between different network locations. Using the capabilities of mobile agent, this system proposes the iterative methods for solving linear equations and performance analysis of these iterative methods. Keywords— — linear equations ,mobile agent ,parallel computing ,performance analysis ,iterative methods
Mobile applications market has expanded at a very massive rate. Amongst mobile phones, android-ba... more Mobile applications market has expanded at a very massive rate. Amongst mobile phones, android-based devices have received incredible adoption. The reason of the increased demand for android phones is the huge availability of applications that can be downloaded and installed easily on mobile phones. Android applications request the permissions allowance at installation. It is difficult for general users to differentiate between the set of permissions which are potentially harmful and those which are not. This weakness has become the chance for malware writers to infect the mobile devices. Therefore, we propose machine learning-based android malware detection framework to detect android malware applications in this paper. This framework extracts automatically various permission-based features from the android applications by using feature extraction tool written python script file, and create a dataset (in arff format) using these extracted features. Then, Kmeans clustering algorithm...
Mobile phones are used as the central computing and communication device today. They are started ... more Mobile phones are used as the central computing and communication device today. They are started to replace traditional computers because of their superior mobility and nearly omnipresent Internet access. At the same time, Android is the latest technology for mobile OS that is getting a lot of attention, and its popularity is growing exponentially each year. There has been increased in android applications because android phones are widely used and they have become more popular than other platforms. Unfortunately, such phenomenon becomes an ideal target for malware developers so that android malware applications have rapidly increased. Therefore, many researchers developed malware detection framework to detect malicious applications. In this paper, an android malware classification system which classifies android applications (normal or malicious) is proposed. Our proposed framework intends to evaluate the performance of machine learning classifiers in android malware classification...
International Journal of Scientific & Technology Research, 2013
Mobile devices have become popular in our lives since they offer almost the same functionality as... more Mobile devices have become popular in our lives since they offer almost the same functionality as personal computers. Among them, Android-based mobile devices had appeared lately and, they were now an ideal target for attackers. Android-based smartphone users can get free applications from Android Application Market. But, these applications were not certified by legitimate organizations and they may contain malware applications that can steal privacy information for users. In this paper, a framework that can detect android malware applications is propos ed to help organizing Android Market. The proposed framework intends to develop a machine learning-based malware detection system on Android to detect malware applications and to enhance security and privacy of smartphone users. This system monitors various permissionbased features and events obtained from the android applications, and analyses these features by using machine learning classifiers to classify whether the application i...
Mobile malware is rapidly becoming a serious threat. There are many different types of mobile mal... more 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.
It is essential to solve linear equations in various fields such as electronic circuit simulation... more It is essential to solve linear equations in various fields such as electronic circuit simulation, structural programming, etc. If the matrix of a linear system is strictly diagonally dominant, iterative methods can be used. Even though the iterative methods are simple, they often require many computations. In this environment, parallel computing techniques are useful for solving numerical problems. This system analyses the performance of iterative methods in parallel computing and how mobile agents fit in parallel computing have been explored. Mobile agents are autonomous computer programs that can migrate between different network locations. Using the capabilities of mobile agent, this system proposes the iterative methods for solving linear equations and performance analysis of these iterative methods. Keywords— — linear equations ,mobile agent ,parallel computing ,performance analysis ,iterative methods
Mobile applications market has expanded at a very massive rate. Amongst mobile phones, android-ba... more Mobile applications market has expanded at a very massive rate. Amongst mobile phones, android-based devices have received incredible adoption. The reason of the increased demand for android phones is the huge availability of applications that can be downloaded and installed easily on mobile phones. Android applications request the permissions allowance at installation. It is difficult for general users to differentiate between the set of permissions which are potentially harmful and those which are not. This weakness has become the chance for malware writers to infect the mobile devices. Therefore, we propose machine learning-based android malware detection framework to detect android malware applications in this paper. This framework extracts automatically various permission-based features from the android applications by using feature extraction tool written python script file, and create a dataset (in arff format) using these extracted features. Then, Kmeans clustering algorithm...
Mobile phones are used as the central computing and communication device today. They are started ... more Mobile phones are used as the central computing and communication device today. They are started to replace traditional computers because of their superior mobility and nearly omnipresent Internet access. At the same time, Android is the latest technology for mobile OS that is getting a lot of attention, and its popularity is growing exponentially each year. There has been increased in android applications because android phones are widely used and they have become more popular than other platforms. Unfortunately, such phenomenon becomes an ideal target for malware developers so that android malware applications have rapidly increased. Therefore, many researchers developed malware detection framework to detect malicious applications. In this paper, an android malware classification system which classifies android applications (normal or malicious) is proposed. Our proposed framework intends to evaluate the performance of machine learning classifiers in android malware classification...
International Journal of Scientific & Technology Research, 2013
Mobile devices have become popular in our lives since they offer almost the same functionality as... more Mobile devices have become popular in our lives since they offer almost the same functionality as personal computers. Among them, Android-based mobile devices had appeared lately and, they were now an ideal target for attackers. Android-based smartphone users can get free applications from Android Application Market. But, these applications were not certified by legitimate organizations and they may contain malware applications that can steal privacy information for users. In this paper, a framework that can detect android malware applications is propos ed to help organizing Android Market. The proposed framework intends to develop a machine learning-based malware detection system on Android to detect malware applications and to enhance security and privacy of smartphone users. This system monitors various permissionbased features and events obtained from the android applications, and analyses these features by using machine learning classifiers to classify whether the application i...
Uploads
Papers by zarni aung