The cybersecurity community is slowly leveraging machine learning (ML) to combat ever-evolving threats. One of the biggest drivers for the successful adoption of these models is how well domain experts and users can under­ stand and trust their functionality. Most models are perceived as a black box despite the growing popularity of machine learning models in cybersecurity applications (e.g., an intrusion detection system (IDS)). As these black- box models are employed to make meaningful predictions, the stakeholders’ demand for transparency and explainability increases. Explanations support­ ing the output of ML models are crucial in cybersecurity, where experts require far more information from the model than a simple binary output for their analysis.

Packet : Big_Data_Analytics_and_Intelligent_Systems_for_Cyber_Threat_Intelligence.pdf.zip filelist
Big_Data_Analytics_and_Intelligent_Systems_for_Cyber_Threat_Intelligence.pdf

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．