Project Home
PDF access


Seif ElDein Mohamed, Mostafa Ashraf, Amr Ehab, Omar Shereef,  Dr.Eslam Amer, Eng.Haytham Metawie, Eng.Mostafa Badr

Publishing Date

October 31, 2020


Nowadays, 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 applications.

1.1 Background

Malware operations are extremely reference to getting to users’ private data by stealing, spying and showingof the regrettable ad. Malware is included within malicious software and it is frequently indicate to as software program that consciously own the deep attributes of malware aggressors and describes its malicious aim. Various kinds of malware are depicted in Figure 2 based on their different purposes,and methods of infiltration.

1.2 Motivation

1.2.1 Academic

Unfortunately, the popularity of android and it is facilities gives to develop and transfer applications with harmful side. Moreover, the variety of android markets favors the presence of rebel markets really track their effectiveness and have appeased even more the improvement of an enormous malware environment. In this light, Android has gotten one of the most important focuses for malware developers, our work is motivated by minimum 49 malware families have been identified . Therefore, detection of malware with ordinary techniques becomes unwieldy, which represents the need to build up a novel and proficient methodology for detecting malicious applications.

1.2.2 Business

Due the using of android framework in industry, company’s like OnePlus target to build security system to avoid malware attacks. This research aims to improve the applications that detect malicious ones. Such as DroidKungFu, this piece of malware is unique in that it is able to avoid detection by anti-malware software, according to the Wall Street Journal. It installs a backdoor in the android OS that allows hackers to gain full control over a user’s mobile device. And a lately malware attack “CovidLock” ransomware is an example. This type of ransomware infects victims via malicious files promising to offer more information about the disease. The problem is that, once installed, CovidLock encrypts data from android devices and denies data access to victims. To be conceded access, you must pay a ransom of USD 100 per device.

1.3 Problem Statement

Malicious attacks are increasingly appearing, and shows up on the Google Play Store, disguised as legitimate applications. They set out to harm the data on the device and often steal user data, commit financial fraud, negatively impact device performance, and more. IT departments implement security technologies that detect the malware, provide visibility into and protection against it.