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Using Machine Learning to Detect Computer Network Security Threats

Booth Id:

Robotics and Intelligent Machines


Finalist Names:
Jogalekar, Anushka

Purpose: The purpose of this project is to find out if/how machine learning can be used to detect network security threats. Hypothesis: Using machine learning tools in the statistical computation programming language R, it should be possible to train a program for detecting signatures of network security threats such as DOS, R2L, U2R and probing (supervised learning). Procedure: 1. Equipment: Dell laptop, RStudio, the R libraries gmodels, caret, c50, and e1071, and KDD cup internet traffic sample data published by MIT Lincoln Labs. 2) Training the program for normal and anomalous traffic: the R algorithms Naive Bayes and Decision Tree using the reduced KDD dataset (~500,000 packets). The training dataset has 41 comma-separated features. 3) Prediction: There were two major methods explored in this experiment: Naive Bayes and Decision Tree. Effectiveness was measured with confusion matrix. Results: Both Naive Bayes and Decision Tree algorithms were able to detect all 23 types of network intrusions. Naive Bayes had 80 percent net accuracy ( true positives and true negatives). Decision Tree had 99% net accuracy, observed in three trials each. Running Naive Bayes took 1.45 hours on average for 5e+6 packets. Running Decision Tree took 7.25 minutes on average per trial. Conclusion: “R” language ecosystem has powerful data processing and machine learning modules that can be effectively used for detecting internet security threats. Decision Tree proved to be a very effective algorithm for network security, with 99 percent accuracy, and can be effective in real time network monitoring. Naive Bayes can be a second choice for simplicity, but not suited for real time detection of security threats. Results are easily extensible for IOT security using hybrid dataset.

Awards Won:
Oracle Academy: Award of $5,000 for outstanding project in the systems software category.