Seif ElDein Mohamed
The mobile industry is in rapid evolution making smartphones available with affordable rates for all segments of society. Smartphones’ purposes are not limited to making phone calls or sending messaging, users can also take photos, store personal data, do online banking and trace their daily activities. The more applications appear, the more security becomes a concern to mobile users. This concern arises from the fear of being subjected to a security breach that jeopardizes confidential personal data such as emails, passwords, location, credentials etc. Malware applications which are developed for the sake of compromising users’ personal data are also increasing rapidly day after day. In our work, we aim to design an intelligent detection framework for Android malware applications. The framework uses different analysis-based approaches along with different machine learning algorithms to distinguish between benign and malicious
1-To use different machine learning algorithms to detect malicious Android applications based on permissions and API Calls.
2-To provide firewall from breaching Android users’ critical data.
3-To secure the personal data information.
The proposed system classifies malware behaviors of Android Applications with efficiency and viability, utilizing machine learning techniques and to determine the ability of malware, detect it, and contain it. It additionally helps in determining recognizable examples that can be utilized to fix and prevent future infections.
Documents and Presentations
You will find here the documents and presentation for our proposal.
You will find here the documents and presentation for our SRS.
You will find here the documents and presentation for our SDD.
You will find here the documents and presentation for our Thesis
Detecting Malicious Android Applications Based On API calls and Permissions Using Machine learning Algorithms