The document analyzes several datasets using machine learning algorithms. It summarizes the KDD Cup 1999 and NSL-KDD intrusion detection datasets, describing their characteristics, experiments comparing different classifiers on the data, and issues with the original KDD Cup data. It also briefly outlines three other medical datasets examined - Hypothyroid, Chronic Kidney Disease, and a Leaf dataset.

1. Analysis of
Datasets
Rafsanjani
Muhammod
References Analysis of Datasets
[ Using Machine Learning Algorithms ]
Rafsanjani Muhammod
Undergrad Student, Department of Computer Science & Engineering
United International University, Bangladesh
April 16, 2017

2. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

3. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

4. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

5. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

6. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

7. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

8. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

9. Analysis of
Datasets
Rafsanjani
Muhammod
References
Outlines
Data Analysis
Data Visualization
Data Preprocessing
Working Datasets
Datasets Description
Results
Comparison between classifiers
References

10. Analysis of
Datasets
Rafsanjani
Muhammod
References
Name of Datasets
KDD Cup 1999 Datasets
NSL-KDD Datasets
Hypothyroid Diseases Dataset
Chronic Kidney Diseases Datasets
Leaf Dataset

11. Analysis of
Datasets
Rafsanjani
Muhammod
References
Name of Datasets
KDD Cup 1999 Datasets
NSL-KDD Datasets
Hypothyroid Diseases Dataset
Chronic Kidney Diseases Datasets
Leaf Dataset

12. Analysis of
Datasets
Rafsanjani
Muhammod
References
Name of Datasets
KDD Cup 1999 Datasets
NSL-KDD Datasets
Hypothyroid Diseases Dataset
Chronic Kidney Diseases Datasets
Leaf Dataset

13. Analysis of
Datasets
Rafsanjani
Muhammod
References
Name of Datasets
KDD Cup 1999 Datasets
NSL-KDD Datasets
Hypothyroid Diseases Dataset
Chronic Kidney Diseases Datasets
Leaf Dataset

14. Analysis of
Datasets
Rafsanjani
Muhammod
References
Name of Datasets
KDD Cup 1999 Datasets
NSL-KDD Datasets
Hypothyroid Diseases Dataset
Chronic Kidney Diseases Datasets
Leaf Dataset

15. Analysis of
Datasets
Rafsanjani
Muhammod
References
Hawk eyes view : Datasets Description
Name of Dataset Instances Features Tasks
KDD Cup ’99 4, 898, 431 41 (& 23) Classification
NSL-KDD 125, 973 41 (& 23) Classification
Hypothyroid 3772 29 (& 4) Classification
Chronic Kidney 400 24 (& 2) Classification
Leaf 340 16 (& 36) Classification

16. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 1 : KDD Cup 1999 Datasets I
Problem Description :
This is a “Computer Networks Intrusion Detection”
problem.
Brief Description :
Data Set Characteristics : Multivariate
Attribute Characteristics : Categorical, Integer
Associated Tasks : Classification
Area : Computer Networking
Donar (with Date) : Unknown (January 1, 1991)
Instances : 4, 898, 431
Features : 41 ( & class values : 23)
Missing Values : None

17. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 1 : KDD Cup 1999 Datasets II
There’re ‘4’ types of attacks :
1 Denial of Service Attack (DoS)
2 User to Root Attack (U2R)
3 Remote to Local Attack (R2L)
4 Probing Attack

18. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 1 : KDD Cup 1999 Datasets III
Snapshot of Dataset # 1 :
This datasets have some problems :
Redundancy :
K. Leung et al.[5] observed that threre’re :
Around 78% “train data” are duplicant &
Around 75% “test data” are duplicant.

19. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 1 : KDD Cup 1999 Datasets IV
Data Partitioning :
Portnoy et al.[3] divede this big data into 10 sub data.

20. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 1 : KDD Cup 1999 Datasets V
Key Issues :
M. Tavallaee[4] observed :
1 Data redundancy
2 High accuracy rate
3 Highly imbalanced
Costly Cross-Validation :
Portnoy et al.[3] also observed that the distribution of this
data set(s) are very uneven which made cross-validation
difficult.
For avoid those kind of problem [4] proposed a
modified version of KDD Cup’99 datasets, is
known as NSL-KDD datasets.

21. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets I
“NSL-KDD” datasets is a subset of “KDD
Cup’99” datasets.
Problem Description :
This is also a “Computer Networks Intrusion
Detection” problem.

22. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets II
Brief Description :
Data Set Characteristics : Multivariate
Attribute Characteristics : Categorical,
Integer
Associated Tasks : Classification
Area : Computer Networking
Donar (with Date) : [4] (2009)
Instances : 125, 973
Features : 41 ( & class values : 23)
Missing Values : None

23. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets III
Experiment Results (70% train & 30%test) :
Classifiers vs Accuracy Graph (in R)
Plotting with R : http://goo.gl/OpkU7e

24. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets IV
Details Results (70% train & 30%test) [using WEKA API] :

25. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets V
Experiment Results (10 Folds Cross-Validation) :
Classifiers vs Accuracy Graph (in R)
Plotting with R : https://goo.gl/4MVxwl

26. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets VI
Details Results (10 Folds Cross-Validation) [using WEKA API] :

27. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets VII
Now the question : which classifier is better & why ?
OneR, use feature :
“A5 src byte” which can classify 90% accurate.
Important feature :
“A5 src byte” is most important feature.
Why “Naive Bayes” is poor :
Beacuse :
1 Lack of independence variables.
2 “Naive Bayes classifier” since it is much more robust to
overfitting.
Ensure :
I’m confident that DT related classifiers ensure more that 90% accuracy.

28. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets VIII
ROC Curve (with Python) :

29. Analysis of
Datasets
Rafsanjani
Muhammod
References
Dataset # 2 : NSL-KDD Datasets IX
Data Visualization : [1][2] [ Using Orange3 ]

30. Analysis of
Datasets
Rafsanjani
Muhammod
References
Analysis of Confusion Matrix :

31. Analysis of
Datasets
Rafsanjani
Muhammod
References
References
DM Farid et al. “Hybrid decision tree and “Naive Bayes”
classifiers for multi-class classification tasks”. In: (2014).
J. Han et al. “Data Mining : Concepts and Techniques”.
In: (2012).
L. Portnoy et al. “Intrusion detection with unbalanced
data using clusters”. In: ().
M. Tavallaee et al. “A Detailed Analysis of the KDD Cup
’99 data Set”. In: (2009).
K. Leung and C. Leckie. “Unsupervised Anomaly
Detection in Network Intrusion Detection Using Clusters”.
In: ().

32. Analysis of
Datasets
Rafsanjani
Muhammod
References
Questions & Answers
Ask me.

33. Analysis of
Datasets
Rafsanjani
Muhammod
References
Thankyou !
My LaTeX Template : https://goo.gl/tzFlD1