Workshop Title:

Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning

Date:

December 3rd, 2024 (UTC +11)

Organizer:

Deakin University

Keywords:

• Intrusion Detection
• Cybersecurity
• Machine Learning
• Deep Learning
• Cyber Defense

Workshop Chair:

Personal Bio:

Dr. Ansam Khraisat is a cybersecurity lecturer and member of Centre for Cyber Resilience and Trust (CREST). School of Info Technology Deakin University, Australia. She is a cyber security researcher and practitioner with industry and academic experience. She worked as a Postdoctoral Research Fellow at the RMIT Centre of Cyber Security Research and Innovation and as a lecturer of Cyber Security at RMIT University, Australia. Ansam is a cyber security researcher and practitioner with industry and academic experience. She received her PhD degree in cybersecurity from the Federation University of Australia, School of Science, Information Technology and Engineering. Dr. Ansam's research is multidisciplinary that focuses on cyber security with a focus on cybercrime detection and prevention. She is a member of the woman in Cybersecurity. She presented at many invited keynotes talks and panels, at conferences and venues nationally and internationally.

Workshop Committee Members:

Dr. Muna Al-Hawawreh Deakin university [email protected]

Workshop Description:

Background:

The exponential growth of digital data and interconnected systems has fueled a parallel rise in cyber threats, ranging from targeted attacks to sophisticated malware. Traditional cybersecurity methods, reliant on static rules and signatures, often struggle to keep pace with the evolving tactics of cybercriminals. In response, the fusion of artificial intelligence (AI) and cybersecurity has emerged as a potent strategy to fortify defenses. Machine learning (ML) and deep learning (DL) techniques, capable of discerning complex patterns from vast datasets, offer a dynamic and proactive approach to threat detection and mitigation. By leveraging AI algorithms to analyze network traffic, identify anomalies, and predict potential threats, organizations can bolster their resilience against cyber attacks in an increasingly interconnected and digitally reliant world.

Goal/Rationale:

The goal of integrating machine learning and deep learning techniques into cybersecurity is to enhance the effectiveness and efficiency of threat detection and response mechanisms. By harnessing the power of AI-driven analytics, organizations can augment their traditional cybersecurity measures with adaptive and proactive defenses. The rationale behind this approach lies in the recognition that static and rule-based systems are no longer sufficient to combat the ever-evolving landscape of cyber threats. ML and DL techniques offer the capability to analyze vast amounts of data in real-time, identify subtle patterns indicative of malicious activity, and enable rapid response to security incidents. Ultimately, the goal is to empower organizations to stay one step ahead of cyber adversaries and safeguard their digital assets with greater precision and agility.

Scope and Information for Participants:

Participants in this workshop will gain a comprehensive understanding of machine learning and deep learning techniques as applied to cybersecurity. The scope of the workshop covers fundamental concepts in ML and DL, practical applications in threat detection, anomaly detection, and malware analysis, as well as hands-on experience in implementing these techniques in real-world cybersecurity scenarios. Participants will learn best practices for data preprocessing, model selection, and evaluation metrics, equipping them with the knowledge and skills needed to integrate ML/DL into their organization's cybersecurity workflows effectively. Additionally, the workshop will explore emerging trends and future directions in ML/DL-enabled cybersecurity, providing participants with valuable insights into staying ahead of evolving cyber threats.

Venue:

221 Burwood Highway, Melbourne, Victoria, 3125, Australia

VISA:

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