27. AI Booming
1. Computing power
What makes AI credible this time around . . .
2. ML, deep learning
algorithms
3. Big data
4. Age of the customer/
digital demand
5. Huge investments
Source: “Artificial Intelligence Can Finally Unleash Your Business Applications' Creativity” Forrester report
28. AI is in Charge
Stock Market: 75+% of all trade orders
generated by Automated Trading Systems
Aviation: Uninterruptible Autopilot System
Military: Nuclear
Weapons
Energy: Nuclear
Power Plants
Utilities: Water
Plants/Electrical Grid
Communications: Satellites
29. AI Applications
Health
Personalized medicine, image analytics
Manufacturing
Predictive and prescriptive maintenance
Consumer tech
Chatbots
Financial services
Fraud detection, ID verification
Government
Cyber-security, smart cities and utilities
Energy
Seismic and reservoir modeling
Service providers
Media delivery
Retail
Video surveillance, shopping patterns
34. AI Ecosystem
IT
Data
Data Platforms
Data Science and ML
/ DL Tools
Solutions
Genome Research Video SurveillanceCustomer 360
Example Industry Use Cases
Fraud Detection
HDFS/NFS
User Access Security Time to Deploy Multi-Tenant
Data DuplicationData Store Cloud
Infrastructure
55. Artificial Neural Networks
Chen, Y., Abraham, A., & Yang, B. (2007). Hybrid flexible neural-tree-based
intrusion detection systems. International Journal of Intelligent Systems, 22,
337–352.
56. Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005). Decision tree classifier for network intrusion detection with GA-based feature selection. In
Paper presented at the proceedings of the 43rd annual Southeast regional conference. Kennesaw, Georgia.
Randomly
Generated
Population
Feature
Selection
Decision Tree
Constructor
Decision Tree
Evaluator
Fitness
Computation
Final Decision
Tree
Classifier
Training Data
Validation
Data
Testing
Data
Generate Next Generation
GA/Decision Tree Hybrid
Genetic Algorithms
57. Teache
r
Correct
(No Training)
Winner
(Decision)
w1 w2 w3 wn
Φ1 Φ2 Φ3 Φn
Y(1) Y(2) Y(3) Y(n)
X(1) X(2) X(3) X(4)
Incorrect
(Training Needed)
Chavan, Sampada, et al. "Adaptive neuro-fuzzy intrusion
detection systems. "Information Technology: Coding and
Computing, 2004. Proceedings. ITCC 2004. International
Conference on. Vol. 1. IEEE, 2004.
Neuro-fuzzing
59. Shon, T., & Moon, J. (2007). A hybrid machine learning approach to network anomaly detection. Information Sciences, 177, 3799–3821.
Hybrid ML NAD
60. Multiple Classifier System for Intrusion Detection
Intrusion Detection as a Pattern Recognition Problem
Giacinto, Giorgio, Fabio Roli, and Luca Didaci. "Fusion of multiple classifiers for intrusion detection in computer networks." Pattern recognition letters 24.12
(2003): 1795-1803.
Pattern Recognition
61. Neural Networks
(Backpropagation)
Neural Networks (Scale
Conjugate Gradient)
Neural Network (One Step
Secant)
Support Vector Machine
Multivariate Regression
Splines
Ensemble
Data
preprocessor
Mukkamala, Srinivas, Andrew H. Sung, and Ajith Abraham. "Intrusion detection using an ensemble of intelligent
paradigms." Journal of network and computer applications 28.2 (2005): 167-182.
Ensemble