Andere Kunden interessierten sich auch für
![Network Attack Detection in IoT using Artificial Intelligence]( "Network Attack Detection in IoT using Artificial Intelligence")
Network Attack Detection in IoT using Artificial Intelligence29,99 €![Maize Diseases Recognition and Classification Using Image Processing]( "Maize Diseases Recognition and Classification Using Image Processing")
Maize Diseases Recognition and Classification Using Image Processing36,99 €![Glaucoma Diseased Image Classification Using Different Classifiers]( "Glaucoma Diseased Image Classification Using Different Classifiers")
Glaucoma Diseased Image Classification Using Different Classifiers23,99 €![Human Action Recognition From Video Using SVM & KNN Classifiers]( "Human Action Recognition From Video Using SVM & KNN Classifiers")
Human Action Recognition From Video Using SVM & KNN Classifiers29,99 €![Improving Classification Accuracy of Automated Text Classifiers]( "Improving Classification Accuracy of Automated Text Classifiers")
Improving Classification Accuracy of Automated Text Classifiers26,99 €![ICT can change education scenario of developing countries]( "ICT can change education scenario of developing countries")
ICT can change education scenario of developing countries26,99 €![The use of mobile learning for visually impaired students]( "The use of mobile learning for visually impaired students")
The use of mobile learning for visually impaired students18,99 €
Mobile devices have become an essential part of our daily life. The rising number of users invite hackers to develop malicious applications besides the security of sensitive data in mobile devices is taken lightly. Relying on currently developed approaches is not sufficient, given that intelligent malware keeps modifying rapidly and thus becomes more difficult to detect. The main goal of the proposed framework intends to develop a machine learning based malware detection system on Android to detect malicious applications and to enhance security and privacy of smartphone users. This system exploits supervised machine learning techniques to distinguish between normal and malware applications. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.