The document discusses the application of machine learning in improving wireless network performance, focusing on data-driven design and various machine learning approaches such as regression, classification, clustering, and anomaly detection. It emphasizes the importance of data collection, pre-processing, and evaluation in achieving effective device fingerprinting and network security solutions. The conclusion highlights the potential of data traces for optimizing network performance and the need for collaboration among experts in various fields.

1. Machine learning for improving
wireless network performance
Merima Kulin, Eli De Poorter, Dirk Deschrijver, Tom Dhaene and Ingrid Moerman
merima.kulin@intec.ugent.be
Internet Based Communication Networks and Services research group (IBCN)- IDLab
Department of Information Technology (INTEC)
Ghent University - imec BESTCOM, 21.10.2016., Louvain

2. Machine learning for wireless networks
• Introduction
• Data-driven design: examples
• Data science in wireless networks: a tutorial
• Conclusion
2

3. Why machine learning?
• Gartner's Hype Cycle for Emerging Technologies
2015.
2016.

4. Computational
power
Massive amounts
of data
Unprecedented advances
in ML
Why machine learning?
Data is the new oil!

5. What kind of data are generating wireless networks?
IoT
Network
monitoring
Cognitive radio
Wireless networks
as data sources

6. Data Science
What is data science?
6
Machine
learning
Data mining
Data
analysis
ML
algorithm
selection
Model
Evaluation
Pre-
processing
…
Kulin, Merima, Carolina Fortuna, Eli De Poorter, Dirk Deschrijver, and Ingrid Moerman.
"Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial."
Sensors 16, no. 6 (2016): 790.
• Machine learning
vs.
• Data mining
vs.
• Data science
“Data science is the study of generalizable
extraction of knowledge from data”.

7. Machine learning in wireless networks
• Introduction
• Data driven design: examples
• Data science in wireless networks: a tutorial
• Conclusion
7

8. Data mining/Machine learning approaches
8
Regression Classification
Clustering Anomaly detection

9. Regression
9
Regression
Regression
X Y

10. Regression - example
10
Application area: Localization
Vanheel, F.; Verhaevert, J.; Laermans, E.; Moerman, I.; Demeester, P. Automated
linear regression tools improve rssi wsn localization in multipath indoor environment.
EURASIP J. Wirel. Commun. Netw. 2011, 2011, 1–27.
Regression
RSSI distance

11. Classification
11
Classifier
X Y
Classification
C1
C2
C3
C1
C2
C3

12. Classification: example
12
Classifier
RSSI
Zigbee
WiFi
Bluetooth
Application area: System recognition
Zheng, Xiaolong, et al. "ZiSense: towards interference resilient duty cycling in
wireless sensor networks." Proceedings of the 12th ACM Conference on
Embedded Network Sensor Systems. ACM, 2014.
Microwave
-
-
-
-

13. Clustering
13
Clustering
X ?
C1
C2
C3
Clustering

14. Clustering: example
14
Application area: System identification
Shetty, N.; Pollin, S.; Pawełczak, P. Identifying spectrum usage by unknown
systems using experiments in machine learning. In Proceedings of the 2009
IEEE Wireless Communications and Networking Conference, Budapest,
Hungary, 5–8 April 2009
Clustering
X ?
Zigbee
WiFi
Noise

15. Anomaly detection
15
Anomaly
X Y/?
C1
C2
C3
Anomaly detection

16. Which DM/ML method can you use?
16
X Y
Uluagac, A. Selcuk, et al. "A passive technique for fingerprinting wireless devices
with wired-side observations." Communications and Network Security (CNS), 2013
IEEE Conference on. IEEE, 2013
Application area: ?
Measurements from a device Device type
?Classification

17. Machine learning in wireless networks
• Introduction
• Data driven design: examples
• Data science in wireless networks: a tutorial
• Conclusion
17

18. The knowledge discovery process
28/11/201618

19. The knowledge discovery process
28/11/201619
Step 1: Understanding the problem domain
Problem formulation
 Fingerprinting wireless
devices
 Identify devices and
device types
 classification problem
Goal
 A new solution for
Network Access
Control to enhance
network security
Assumptions
 Packet generation is
influenced by hardware
architecture (CPU, DMA,
L1/L2 cache, ..)
Hypothesis
 Identify devices and/or
device types based on
statistical properties of
their traffic flows
Data collection
 Analyze inter-arrival times
(IATs) from several devices

20. The knowledge discovery process
28/11/201620
Collected data
• IAT traces
Validate the data
 Is the selected data a
representative sample for
solving the formulated
problem?
Validate the hypothesis
 Is the stated hypothesis true
and the selected data mining
task is likely to prove it?
Step 2: Understanding the data

21. Device fingerprinting
• Data collection
• Data repositories (e.g. CRAWDAD)
• Run experiments on testbed facilities
• Collect data in situ
21
Overall
 94 files
 Total ~137 mil.
 Mean ~1.46 mil.
 Std ~1.3 mil
Dell
iPad
iPhone
Nokia

22. Device fingerprinting
Step 2: Understanding the data
22
Visual techniques Computational techniques
 Five number summary
 Standard deviation, variance
 skewness
 Coefficient of determination (R2)
 Coefficient of correlation
 …
boxplot
PDF and CDF
Scatter plots
Histograms

23. Device fingerprinting
Step 2: Understanding the data
• Visual techniques
• PDF, time-series, histograms…
23

24. Device fingerprinting
Step 2: Understanding the data
• Computational techniques
• 5-num summary
24

25. Device fingerprinting
Step 2: Understanding the data
• Computational
• Coefficient of determination (R2)
• Analysis for device identification
25
 How much can data from one Dell Notebook tell about the data from other Dell
Notebooks?
DN2
DN3

26. The knowledge discovery process
28/11/201626
Step 3: Data pre-processing
Raw data
• Traces of IAT
data points
Training data
 Features extraction
 Feature vectors
 Training examples

27. Device fingerprinting
Data pre-processing
27

28. Device fingerprinting
Data pre-processing
28

29. Device fingerprinting
Data pre-processing
• Features extraction
29

30. The knowledge discovery process
28/11/201630
Step 4: Data mining
Training data
• Feature vectors of
histogram bins
Model
 Neural network
 HL=6, α=0.1, learned
weights
 k-Nearest Neighbors
 K=1
 Decision trees
 Logistic regression
 …
ML

31. 31
Device fingerprinting
Step 5: Performance evaluation
Test data
• Test set of feature
vectors
Performance
indication
 RMSE, MAE, R2…
 Precision, Recall,
 Confusion matrix
 …

32. • Algorithm selection: k-fold cross validation
32
Device fingerprinting
Step 5: Performance evaluation

33. • Performance evaluation
• Confusion matrix
• Accuracy, Precision, Recall, accuracy, F1-score
33
Device fingerprinting
Step 5: Performance evaluation

34. 34
Device fingerprinting
Step 5: Performance evaluation
• Device type classification results

35. 35
Device fingerprinting
Step 5: Performance evaluation
• Model tuning: neural networks
Kulin, Merima, Carolina Fortuna, Eli De Poorter, Dirk Deschrijver, and Ingrid Moerman. "Data-Driven
Design of Intelligent Wireless Networks: An Overview and Tutorial." Sensors 16, no. 6 (2016): 790.
More details about how to tune your algorithm can be found:

36. Conclusion
36
• Data-driven network design can be used for
• Failure detection
• Systems recognition
• Performance optimization…
• Data traces are valuable
• Considering releasing data traces after use
• Need for increased collaboration
• Network experts, testbed experts, data mining experts,
statisticians, wireless communication, etc.