Workshop Title:

Privacy-Preserving Intrusion Detection: Empowering Security with Federated Learning

Date:

September 23rd, 2024 (UTC +10)

Organizer:

Torrens University Australia

Keywords:

• Intrusion Detection
• Privacy Preservation
• Federated Learning
• Cybersecurity
• Data Privacy
• Machine Learning

Workshop Chair:

Personal Bio:

Ammar Alazab is a Senior Cybersecurity Lecturer at Melbourne Institute of Technology, with a wealth of industry experience. His expertise extends beyond academia, as he has a strong background in the cybersecurity industry. This practical experience enriches his teaching and research, providing valuable real-world insights to his students. His work focuses on cyber security, digital forensics of computer systems, and cybercrime detection and prevention. With numerous published articles and over 50 research papers in top-tier journals and conferences, Alazab is a prominent figure in the cybersecurity field. His remarkable contributions have led him to secure research grants amounting to over 1 million from reputed industry players, further validating the practical significance of his work.

Workshop Committee members:

Dr. Sarabjot Singh Melbourne Institute Technology sarabjotsingh@academic.mit.edu.au

Dr. Ansam Khraisat Deakin university sarabjotsingh@academic.mit.edu.au

Workshop Description:

Background:

Intrusion detection plays a critical role in safeguarding digital assets and networks against malicious activities. Traditional approaches rely on centralized data repositories, raising concerns about data privacy and security. Federated Learning, a decentralized machine learning paradigm, offers a promising solution by enabling collaborative model training across distributed devices while preserving data privacy. By harnessing the collective intelligence of edge devices without compromising individual privacy, Federated Learning revolutionizes intrusion detection systems, enhancing both detection accuracy and privacy protection. This workshop explores the intersection of privacy-preserving techniques and intrusion detection through Federated Learning, providing participants with insights, strategies, and practical tools to deploy privacy-preserving intrusion detection systems effectively in real-world scenarios.

Goal/Rationale:

The workshop aims to address the pressing need for privacy-preserving intrusion detection systems in the era of increasing data breaches and privacy concerns. Traditional centralized approaches to intrusion detection compromise data privacy, hindering the adoption of effective security measures. Federated Learning offers a solution by enabling collaborative model training across distributed devices while preserving data privacy. Recent advances in privacy-preserving techniques, such as differential privacy and homomorphic encryption, have further enhanced the feasibility and effectiveness of Federated Learning for intrusion detection. By fostering collaboration and knowledge exchange among cybersecurity professionals, data scientists, and industry stakeholders, the workshop seeks to empower participants with the knowledge and tools needed to deploy robust and privacy-preserving intrusion detection systems using Federated Learning, thereby advancing the state-of-the-art in cybersecurity while protecting individual privacy rights.

Scope and Information for Participants:

The workshop will cover a wide range of topics related to privacy-preserving intrusion detection through Federated Learning. Specific themes include:

1. Fundamentals of Federated Learning and its application to intrusion detection.

2. Privacy-preserving techniques compatible with Federated Learning, such as differential privacy and homomorphic encryption.

3. Real-world implementation challenges and strategies for deploying Federated Learning in intrusion detection systems.

4. Case studies and best practices illustrating successful implementations of Federated Learning for intrusion detection.

5. Open discussion and collaboration on overcoming challenges and advancing the field of privacy-preserving intrusion detection.

Participants are encouraged to contribute presentations, panel discussions, and interactive sessions focusing on these themes to foster a comprehensive understanding of the topic and drive innovation in privacy-preserving intrusion detection.

Venue:

196 Flinders St, Melbourne VIC 3000, Australia

VISA:

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