Networking Industry Based Project Proposal Form

Networking Industry Based Project Proposal Form

(This project proposal should be for 2 trimesters 24 weeks over 2 trimesters)

From: Miqdad Hassan

CareerDC Group Pty. Ltd.

T: 02 7209 3850 | W: www.careerdc.com.au

Project Title: Deepfake Detection and Cybersecurity Enhancement

Industry Client Details (Compulsory for all proposals)

Company Name: BRR Technology Solutions

ABN: 93475772006

Company address: 21/296 Marrickville Road, Marrickville NSW 2204, Sydney, Australia

Company Profile: Full-service technology Consultancy company delivering high performing and advanced functionality software solutions for growing companies.

Website: https://brrtechnology.com

Industry Assigned Supervisor Detail

Contact Name: Raafat Alsameraai

Email ID: projects@careerdc.com.au

Contact Number: 02 7209 3850

Brief Biography: Raafat is an experienced Engineer and Technologist with over 6 years experience in technology and engineering.

Client Name and Signature: Date: 27/06/2024

Raafat Alsameraai

Industry Assigned Supervisor Name and Signature: Date: 27/06/2024

Raafat Alsameraai

Project Proposal

1. Background and Rationale for the Project:

With the rise of deepfake technology that uses artificial intelligence (AI) to create realistic but fabricated audio, video, or image content, there is a growing concern about its potential misuse for cyberattacks and social engineering. This capstone project aims to develop a Deepfake Detection System to identify and mitigate the risks posed by deepfake content in various online platforms and communication channels. By leveraging machine learning algorithms and digital forensics techniques, the system will detect and flag suspicious deepfake media to prevent cyberattacks, networking hacking attempts, and misinformation campaigns.

2. Project Goals and Objectives:

• Deepfake Detection Algorithms: Develop and implement machine learning algorithms capable of analysing multimedia content to detect signs of deepfake manipulation, including facial inconsistencies, speech anomalies, and image artefacts.


• Real-time Detection and Analysis: Design a real-time deepfake detection system that can analyse streaming media, social media posts, and communication channels for potential deepfake content and provide immediate alerts to users and administrators.


• Integration with Cybersecurity Tools: Integrate the deepfake detection system with cybersecurity tools, such as intrusion detection systems (IDS), firewalls, and antivirus software, to enhance threat detection capabilities and prevent networking hacking


• User Education and Awareness: Develop educational resources and guiding materials to raise awareness among users about the risks associated with deepfake content and provide guidance on identifying and handling suspicious media.

3. Desired Outcomes/Deliverables:

• Deepfake Detection System: A robust and scalable deepfake detection system capable of accurately identifying and flagging suspicious multimedia content across various online platforms and communication channels.


• Integration with Cybersecurity Infrastructure: Successful integration of the deepfake detection system with cybersecurity tools and infrastructure to enhance threat detection capabilities and prevent networking hacking attempts.


• User Guide Materials: Comprehensive user materials, including guides, and best practices, to educate users about the risks of deepfake content and empower them to identify and report suspicious media effectively.

4. Project Resources:

? Data Sources

• Publicly available videos/images datasets (e.g., DeepFake Detection Challenge Dataset, Celeb-DF).

? Data Management Systems

• Relational Databases (e.g., MySQL, PostgreSQL).


• NoSQL Databases (e.g., MongoDB, Cassandra).

? Deep Learning Frameworks

• , PyTorch and Keras.

? Pre-trained Models and Libraries

• OpenCV for image


• Dlib for facial


• Deepfake detection models (e.g., XceptionNet, Capsule-Forensics).

? Network Security Tools

• Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS).


• Firewalls and Network Traffic Analysis

? Programming Languages

• Python (for machine learning and AI development), JavaScript (for web-based interfaces).and C++ (for performance-critical components).

? Development Frameworks

• Django or Flask for web backend development and React or Angular for frontend

? Reporting Tools

• Custom reporting solutions using HTML, CSS, and


• Automated report generation

? Encryption Tools

• SSL/TLS for secure data


• AES and RSA for data

? Access Control

• Identity and Access Management (IAM)

5. Additional information

This capstone project provides students with an opportunity to address the emerging challenges posed by deepfake technology and its potential implications for cybersecurity. By developing a Deepfake Detection System and integrating it with existing cybersecurity infrastructure, students can contribute to enhancing the security posture and resilience of organisations against deepfake threats and networking hacking attempts.