Cyber-threat detection
Outcome
Continuous assessment of asset inventory to gain a complete and accurate view of devices, users and applications with access to IT systems
Models that can detect and respond to deviations from the norm, even with noisy data
Prediction models that will assess where and how a company is most likely to be breached, so planning and resource allocation can be directed toward weak points in the IT system
Explainability of model recommendations and analysis for security operations leaders, CISOS, auditors and Board of Directors
Business Value
Get up to date knowledge of global and industry level threats to help prioritize defense systems
Prevent cyber threat incidents and respond quicker/better when they do happen, improving OPEX
Free up limited cybersecurity teams to focus on complicated cases, while AI takes care of routine tasks
H2O's AI and Data Approaches
Classification Models that can identify threatening vs non threatening events and actors
Anomaly detection, entry classification, domain generation detection
Unsupervised learning for unlabeled data, clustering data based on anomalies
Analyze large data sets of events to identify many different types of threats (eg, malware, ransomware, email phishing, malicious code downloads)
Train neural networks to tell the difference between malicious and safe files
Use images to train classifier neural networks to detect malware in .doc and .pdf files
Resources
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