|
gptkbp:instanceOf
|
gptkb:academic
|
|
gptkbp:application
|
network security
fraud detection
anomaly detection
incident response
threat detection
malware analysis
vulnerability management
phishing detection
intrusion detection
|
|
gptkbp:challenge
|
data privacy
scalability
explainability
adversarial attacks
bias in models
|
|
gptkbp:concerns
|
gptkb:legislation
data leakage
lack of labeled data
model poisoning
overfitting to known threats
|
|
gptkbp:conference
|
gptkb:RSA_Conference
gptkb:Black_Hat
gptkb:DEF_CON
|
|
gptkbp:goal
|
enhance threat intelligence
improve security automation
reduce false positives
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
AI for Security
|
|
gptkbp:organization
|
gptkb:Google
gptkb:IBM
gptkb:Microsoft
gptkb:Palo_Alto_Networks
gptkb:Darktrace
gptkb:CrowdStrike
gptkb:FireEye
|
|
gptkbp:publishedIn
|
gptkb:USENIX_Security_Symposium
gptkb:IEEE_Security_&_Privacy
gptkb:ACM_CCS
|
|
gptkbp:relatedTo
|
gptkb:artificial_intelligence
cybersecurity
|
|
gptkbp:standardizedBy
|
gptkb:ISO/IEC_27001
gptkb:NIST_AI_Risk_Management_Framework
|
|
gptkbp:trend
|
AI for zero-day detection
AI-driven phishing attacks
AI-powered SOCs
increasing automation
use of generative AI
|
|
gptkbp:uses
|
gptkb:machine_learning
deep learning
natural language processing
data mining
|
|
gptkbp:bfsParent
|
gptkb:German_Research_Center_for_Artificial_Intelligence_(DFKI)
|
|
gptkbp:bfsLayer
|
5
|