Team Members

Jumana Nehad

Team Leader

Nour Ahmed

Team Member

Rawan Hesham

Team Member

Abdallah Amr

Team Member


Dr. Ayman Tahaa


Eng. Verina Adel Saber

Teaching Assistant


Nowadays, interactive applications are witnessing significant development. These applications are popular targets for cyberattacks as they are network accessible and contain a large amount of user data. To ensure user privacy protection, it’s important to develop privacy protection solutions that utilize network traffic features and artificial intelligence. Distributed Denial of Service (DDoS) attacks are one of the most significant threats to network security, causing severe financial losses and reputational damage. This paper provides detailed information about security in interactive applications and proposes an artificial intelligence-based security system for detecting Distributed Denial-of-Service (DDoS) attacks on a network.

System Objectives

Classifying and detecting DDOS attacks in real-time using AI and network traffic analysis.

The system will provide real-time monitoring of network traffic.

Building a prediction model to classify the incoming network traffic as either normal or malicious during the use of an interactive application.

Providing a user interface to allow administrators to monitor and manage the system.

Allows users with the ability to start and stop the application, view attack history, manually block or unblock IP addresses, clear IPs from the database, and retrain the model.

Build MySQL database to store the list of known benign and malicious IPs that can be blocked without querying the database on subsequent requests.

Implementing a firewall to automatically block incoming traffic from identified malicious IP addresses.

System Scope

innovative secure-oriented software that manages security features to protect the servers from being attacked.

A traffic capture module that uses Scapy to capture network traffic in real-time.

A traffic pre-processing model that extracts features from the captured traffic data, such as Frequency of requests, Header Length, Protocol, and Packet Size.

An AI classification model that uses a trained deep learning model to classify the pre-processed traffic as either benign or malicious.

A MySQL database for storing the pre-processed traffic data and the results of the AI analysis.

If the pre-processed traffic has been benign for more than time, the IP will be stored in the whitelist.

The system shall block the malicious IP using Advanced Firewall and store it in the blacklist in Database.

A user interface for displaying system logs.

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






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