Securing the Cloud with Advanced Artificial Intelligence

Daniel Kovach, Mike Simms

Neural Networks

• Emulate biological nervous systems• Cutting edge area of research

Output

Processing

Input Input

Processing

Input Input

Why Neural Networks?

• Highly adaptable• Data driven• Very little knowledge is needed about the data• Reduced latency due to optimized Raytheon

proprietary library

Advantages

• Greater adaptability• More accurate• Ability to deal with noise• Less false positives

Method

• Determine what variables indicate malicious behavior

• Train NN on data• NN determines what constitutes malicious

behavior

Application Neural Networks Statistical Methods

IDS Systems 1 false positive/day 50 false positives/day

Option Pricing

Bank Failure Prediction

Bankruptcy Prediction

Studies in Metabolism

Cancer Detection

Melanoma Detection 74.5% accuracy 74.8% accuracy

Medical Comparative Study 10 cases 4 cases

Predicting River Flow

Analyzing Sales Data

Marketing Predictions

Image Classification

Well Log Data (Slight)

Wind Turbine Data

Seismic Activity 3-7% improvement

Student Learning Rates Up to 30% improvement

Neu

ral N

etw

orks • 80%

Accuracy• 1 FP/day

Stati

stica

l Tec

hniq

ues • 80%

Accuracy• 50

FP’s/day

Neural Networks in IDS Systems

AdobeMicrosoft Word

Microsoft Excel

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10

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40

50

60

70

80

90

100

Neural Network Results

False PositivesFalse Negatives

Perc

ent

References1. Amilon, Henrik. "A Neural Network Versus Black–Scholes: A Comparison of Pricing and Hedging Performances." Journal of Forecasting 22 (2003): 317-335. Print.

2. Amini, J.. "Optimum Learning Rate in Back-Propagation Neural Network for Classification of Satellite Images (IRS-1D)." Scientia Iranica 15.6 (2008): 558-567. Print.

3. Baxt, WG. "Application of artificial neural networks to clinical medicine.." Lancet 346.8983 (1995): 1135-1138. Print.

4. Couvreur, K. and Couvreur, P. (1997) "Neural networks and statistics: a naive comparison" Belgian Journal of Operations Research, Statistics, and Computer Science 36, 217-225.

5. Ganster, H., Rohrer, R., Paletta, L., Ebner, T., Pinz, A., Binder, M., 1998, „Comparison of Neural Networks and Statistical Methods in Melanoma Classification“, Proc. 22nd OAGM/AAPR Wrksp, Illmitz, Austria

6. Jung, C. "Comparing Statistical Methods and Artificial Neural Networks in Bankruptcy Prediction". Ph.D. dissertation, University of Warwick, 1997.

7. Li, Shuhui, Donald C. Wunsch, Edgar O'hair, and Michael G. Giesselmann. "Cooperative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation." Journal of Solar Energy Engineering 123 (2001): 327-332. Print.

8. Lippmann R. P., Cunningham, R. K. Improving intrusion detection performance using keyword selection and neural networks. Computer Networks (Amsterdam, Netherlands: 1999), 34:597–603, 2000.

9. Perai, A. H., H. Nassiri Moghaddam, S. Asadpour, J. Bahrampour, and Gh. Mansoori. "A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal." Poultry Science 89 (2010): 1562-1568. Print.

10. Rahman, N.F.A. "Mining Student's Performance in UPSR Using Statistics and Neural Networks". Ph.D. dissertation, Universiti Utara Malaysia, 2008.

11. Razi, Muhammad, and Kuriakose Athappilly. "A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models." Expert Systems with Applications 29 (2005): 65-74. Print.

12. Resop, J.P. "A Comaprison of Artificial Neural Networks and Statistical Regression with Biological Resources Applications". M.S. dissertation, University of Maryland, 2006.

13. Roli F., Giacinto, G., Vernazza, G., Comparison and combination of statistical and neural network algorithms for remote-sensing image classification, Neurocomputation in Remote Sensing Data Analysis, Advances in Spatial Science Series, Springer, 1997.

14. Sargent, D. J.. "Comparison of Artificial Neural Networks with other Statistical Approaches: Results from Medical Data Sets." Cancer 91.8 (2001): 1636-1642. Print.

15. Schumann, A., Neural Network Versus Statistics : A Compa- ring Study of Their Classification Performance on Well Log Data,<URL: http:\\citeseer.ist.psu.edu.html>.

16. Shanmugasundaram, Jayavel, Nagendra Prasad, M.V., Vadhavkar, Sanjeev and Gupta, Amar, Use of Recurrent Neural Networks for Strategic Data Mining of Sales (February 2002). MIT Sloan Working Paper No. 4347-02; Eller College Working Paper No. 1029-05. Available at SSRN: http://ssrn.com/abstract=300679 or http://dx.doi.org/10.2139/ssrn.300679

17. Sereno, Thomas J., Gagan B. Patnaik, and Mari J. Mortell. Data to test and Evalueate the Performance of Neural Network Architectures for Seismic Signal Discrimination: Data Sets #2-3. San Diego, California: Phillips Laboratory, Directorate of Geophysics, 1992. Print.