Intrusion-Detection

Intrusion Detection using various Data Mining Techniques (KDD Cup 1999 Data)

Dataset available on http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

Techniques Used:

1. K Means (K=59)

   Accuracy 93.077 %

	precision	recall	f1-score	support
attack.	0.95	0.96	0.96	250436
normal.	0.83	0.80	0.82	60593
avg / total	0.93	0.93	0.93	311029

2. Decision Trees

   Accuracy 92.956 %

	precision	recall	f1-score	support
attack.	1.0	0.91	0.95	250436
normal.	0.74	0.99	0.85	60593
avg / total	0.95	0.93	0.93	311029

3. Multi-Level Perceptron

   Accuracy 92.388 %

	precision	recall	f1-score	support
attack.	0.99	0.91	0.95	250436
normal.	0.73	0.97	0.83	60593
avg / total	0.94	0.92	0.93	311029

4. Random Forrest Classifier

   Accuracy 92.775 %

	precision	recall	f1-score	support
attack.	1.0	0.91	0.95	250436
normal.	0.73	0.99	0.84	60593
avg / total	0.95	0.93	0.93	311029

5. K Neighbours

   Accuracy 92.469 %

	precision	recall	f1-score	support
attack.	1.0	0.91	0.95	250436
normal.	0.72	0.99	0.84	60593
avg / total	0.94	0.92	0.93	311029