Philo-AD
Deep-learning based Anomaly Pattern Detection Solution
What is Anomaly?
Anomaly is defined as something that deviates from the normal behavior or what is expected. Anomaly detection is the identification of items, events which do not conform to an expected pattern Typically the anomalous items can be translated as an indication of intrusions, frauds, defects, error and etc.
Anomaly Detection
AIM: Automatic detection of Anomaly in specific domain using AI Technology
Current Status: Automatic detection of Anomaly candidates in specific domain using AI Technology
Pain Points of Existing Technologies
1. Rule based approaches
- Need experts on specific domain to make rule sets
- NOT robust (Unable to detect slightly modified patterns)
2. Data-driven machine learning approaches
- Need vectorization of data records
- Users need to keep the entire data sets from its deployment for system upgrades

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.
- Simplified AI deployment process with deep learning based time-series NORMAL DATA MODELING
Enables a rapid deployment of the solution, saves at least 80% of the time for integration with 50% less cost
- Minimized alarm fatigues through Hybrid approach for anomaly detection
Deep Neural Network + HBKS (Augmented Pattern Memory)
- Simplified system update process by applying Model Management Framework
- Patented: Anomaly Pattern Detection System and Method 10-2019-0025066
Application

Finance
FDS / AML

Network Security
Network Intrusion Monitoring

Manufacturing
Predictive maintenance
Smart Factory
Philo-AD Structure
1. Data Digitization
Convert non-digital data to digital vectors
2. Time-series Modeling
Applying a neural network model reflecting time-series characteristics
3. Hierarchical Behavioral Knowledge Space (HBKS)
Storing the anomaly patterns and its root cause identified by users, applying this information instantly for future analysis reference

Philo–AD Dashboard
Visualization of the data set
1. Monitoring & Graph
- Graphical presentation of monitoring, learning and verification process
- Analytic graphic presentation of anomalies
2. Auto Learning
Automatically collects data-set for automated learning
3. Explainable
Explaining the reason and the cause of suspicious patterns (Anomalies)
