Join NITDA Cybersecurity Innovation Challenge in addressing the evolving dangers of the dark web, encouraging cybersecurity awareness, collaboration, research.
This innovation challenge aims to address the urgent need for innovative solutions that bolster organizations’ ability to proactively detect, analyse, and mitigate cyber threats emerging from the dark web.
– Inadequate Dark Web Visibility
– Timely Threat Detection
– Lack of Effective Dark Web Analysis
– Insufficient Threat Attribution
– Limited Automation
In this innovation challenge, participants are tasked with developing innovative solutions that address the challenges associated with dark web monitoring and threat mitigation. Potential solutions may include:
Advanced Dark Web Crawlers: Build advanced web crawling tools that navigate the dark web, collect relevant data, and provide organizations with insights into emerging threats and vulnerabilities. This tool should feature an analytics platform that uses machine learning and data analytics to process and categorize unstructured dark web data, providing actionable insights to security teams.
Real-time Threat Detection: Design a platform that leverage AI to detect anomalies and patterns in dark web activities, as well as offer real-time threat intelligence feeds from the dark web, enabling organizations to identify potential threats in real time.
Threat Attribution Tools: Develop methods to link dark web activities to known threat actors or campaigns, construct detailed profiles of dark web threat actors facilitating more accurate threat attribution and enabling targeted response strategies.
Automation for Dark Web Monitoring: Create automated workflows that continuously monitor dark web sources, extract relevant information, and provide security teams with timely alerts about emerging threats.
HOW TO APPLY
Join the conversation>>>> Whatsapp Group
Note: The shortlisted candidates will be contacted via email, and all other important information regarding this programme will be communicated to the shortlisted candidates.
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