CyberGuardian.io

Project Info

Team Name

Chronos2

Team Members

Chris Vella

Project Description

CyberGuardian Image 2

Project Description

CyberGuardian Image 2

#elderly protection #fraud detection #ai security #financial safety #scam prevention #generative ai #ethical ai #vulnerable communities

Data Story

Evidence of Work

Video

Homepage

Team DataSets

Cyber.gov.au - Reports and statistics

Description of Use Used to gain an understanding of the current cybersecurity trends.

Data Set

ACSC Annual Cyber Threat Report, July 2021 to June 2022

Description of Use The ACSC threat report provides comprehensive insights into the latest cybersecurity risks, attacks and threat actor profiles impacting Australia. By analysing this data, machine learning models can be developed to identify patterns in emerging hacking techniques, compromised vulnerabilities and high-risk threat vectors. These AI models can power predictive systems to forecast the types of threats likely to target Aussie organisations in future. The data provides real-world examples of attacks that can be used to simulate and train adversarial AI to penetrate systems. The learnings can then improve cyber defence and resilience. Additionally, the mitigation advice given in the report can inform the development of automated security hardening tools. By proactively patching the weaknesses highlighted in the report, systems can be secured against the most up-to-date attack vectors threatening Australia. In summary, the ACSC threat report is a vital source of intelligence on the nation's threat landscape. It can equip individuals and organisations with the situational awareness and practical insights needed to get ahead of cybersecurity risks.

Data Set

Detecting and responding to cyber security incidents

Description of Use The "Detecting and responding to cyber security incidents" dataset provides detailed information on various types of cybersecurity attacks and threats. This data can be utilized to train machine learning models to identify patterns and signatures of different hacking techniques. The models can then be integrated into security solutions like antivirus software, firewalls, and intrusion detection systems to recognise and block malicious activities in real-time. Additionally, the data includes response strategies and mitigation steps taken for past incidents. This knowledge can be used to develop automated response playbooks and remediation workflows. When a new incident is detected, the appropriate response plan can be triggered to quickly contain the attack and minimise damage.

Data Set

Challenge Entries

Generative AI: Unleashing the Power of Open Data

Explore the potential of Generative AI in conjunction with Open Data to empower communities and foster positive social impact. This challenge invites participants to leverage Generative AI models to analyse and derive insights from Open Data sourced from government datasets. By combining the power of Generative AI with the wealth of Open Data available, participants can create innovative solutions that address real-world challenges and benefit communities.

Eligibility: Ethical use of a generative AI in your project and at least one Australian or New Zealand data set.

Go to Challenge | 29 teams have entered this challenge.

Using machine learning and generative AI to improve health outcomes

How can machine learning or generative AI be used to help Australians to live longer, healthier, happier lives?

Eligibility: Any submission that uses machine learning or generative AI for the purpose of improving the health and wellbeing of Australians, or provides a use case for using machine learning or generative AI for improving the health and wellbeing of Australians.

Go to Challenge | 14 teams have entered this challenge.