Team Members

Hazem Mostafa

Team Leader

Mohamed Amin

Team Member

Adham Samir

Team Member

Amer Mohamed

Team Member


Dr Eslam Amer

Associate Professor

Eng Youssef Talaat

Teaching Assistant


The rising increase of malicious software poses a threat of an immense nature, whereas the reciprocating of data is not limited to personal daily transactions, but dwelled deeply within large enterprises and organizations. The purpose of this project is to achieve a new approach in detecting mimicry malware that disguises itself to resemble a valid software to bypass the conventional antiviruses which are mainly signature-based anti-viruses. The proposed antivirus would follow a dynamic analysis interpretation of detecting malicious software using machine learning techniques, thus evolving and adapting to the ever-changing process of formation of malware.

System Objectives

Detecting Malware using a dynamic analysis approach.

Building a deep learning model that classifies mimicry malware.

Building a software that is able to sniff the multiple variants of a malware.

Delivering an endpoint protection software product that falls under the category of next generation anti-viruses.

System Scope

The system will implement malware analysis through a dynamic approach rather the traditional static approach used in most antivirus programs. API call sequences which are a set of functions and data structures that a program can use to ask the operating system to do some functionality of the selected program will be used as the dataset along with NLP techniques such as TF-IDF and word embedding then processing using swarm intelligence algorithms specifically (Ant-Colony Optimization) and accordingly classification using deep learning to identify mimicry malware.

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





Malware Detection Approach Based on the Swarm-Based Behavioural Analysis over API Calling Sequence