Philo-AD
Deep-learning based Anomaly Pattern Detection Solution
SME Network Security
Web / log analysis
Anomaly Detection
APT
ESM/IDS/IPS/DRM
Smart Factory
Process Management
Energy management
Quality Analysis
Predictive Maintenance
Finance
FDS
Data Collection
Analysis / Detection
Monitoring
Philo-AD
Applies Deep learning artificial intelligence technology to effectively monitor suspicious behaviors and patterns from internal network as well as identify external threats.
The process is in real-time, and it supports instant update feature to maximize system reliability.
Since buying and running a computing power necessary to implement deep learning can be cost prohibitive, Ellexi offers monthly subscription based services for customers.
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Simplified AI deployment process with deep learning-based time-series NORMAL DATA MODELING
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Enables a rapid deployment of the solution, saves at least 80% of the time for integration with 50% less cost
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Minimized alarm fatigues through Hybrid approach for anomaly detection
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Deep Neural Network + HBKS (Augmented Pattern Memory)
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Simplified system update process by applying Model Management Framework
Features
Versatile
Customizable solution since it has been developed not just for a specific domain
Precision
Gradually minimize false positive issues with anomaly pattern storage
Convenience
Assume every data is ‘Normal’, no tagging or labeling necessary
Practical
Using technology that is required before applying the applied Neural network model by reinforcement learning, which is being actively researched
No expert
No need of experts to generate rules by consulting, therefore minimize time and cost for deployment
Dashboard
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Monitoring & Graph
Graphical presentation of monitoring, learning and verification process
Analytic graphic presentation of anomalies
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Auto Learning
Automatically collects data-set for automated learning
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Explainable
Explaining the reason and the cause of suspicious patterns (Anomalies)
