Improve Paging

8/22/2019 Improve Paging

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Mining call data toincrease the robustness

of cellular networks

to DoS attacks

Hui Zang and Jean BolotSprint

http://research.sprintlabs.com/


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Better Security viaRobust Paging

Using Mobility Data

Hui Zang and Jean BolotSprint



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Cellular networks are at risk

(650)123-7777 70.2.35.5

Paging channel


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Threats identified

SMS DoS attacks>Mobicom 06 (Penn State)

Battery attacks via paging>SecureComm 2006 (UC Davis)

Signaling DoS via data paging>Mobicom WiSe workshop 06 (Sprint)


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Increase the robustness ofthe paging channel

Increase paging channel capacity

Reduce/block unwanted traffic

Decrease paging channel utilization>Efficient paging schemes


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Contributions

Data-driven approach

Large-scale cellular mobility data

Efficient paging algorithms>Reduce paging utilization by 80%>Increase delay by 10%


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Per Call Measurement Data(PCMD)

Collected by each switch

Record of every call

>Call type (voice, data, SMS)>Start/end cell, sector>Source/destination

Three month-long traces Feb 2006


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Trace statistics

Switch Nb.records Nb.cells Nb. users

Manhattan 120 M 139 1061 K

Philadelphia 140 M 150 543 K

Brisbane 50 M 144 404 K

Total 310 M 433 2 M

Size of data: 65GB


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Mobility

96% users visit < 40 cells in a month


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Calling activity

60% users make < 26 calls in a month


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Joint calling and mobility

4% most mobile make 35% of calls


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Mobility patterns over time

Correlation between day X and Y>Mutual informationI(X,Y) = H(X) + H(Y) H(X,Y)

Normalized by entropy of the datafrom a reference day

NMI(X,Y) = I(X,Y)/H(X)


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Correlation between two days

Weekday traces are highly correlated

NMI(current day, n days ago)

2/28 Tuesday, 2/26 Sunday


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How much history is needed

14 days of data is usually enough

NMI(current day, past n days)


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Recap - what we found so far

96% users in < 40 cells 60% users make < 26 calls 4% most mobile users make 35% of calls Locations are correlated across days

Higher correlation between weekday data 14 days of data is sufficient

Use this to design better paging schemes


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Paging Locate the mobile

Mobile

Switching

Center

I am here

(650)123-4567is in my cell


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Paging establish the channel

Mobile

Switching

Center


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Broadcast vs. profile-basedpaging

Mobile

Switching

Center

One paging/location area

Incoming

call


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Broadcast vs. Profile-basedpaging

Mobile

Switching

Center

Broadcast


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Mobile

Switching

Center

Profile-based

1st step

Incoming

call


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Mobile

Switching

Center

2nd step(broadcast)

Profile-based

No reply

back


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Profile-based paging

Fixed profile - update profile periodically+: low management cost

-: up-to-date mobility data cannot be utilized

Dynamic profile - update with every call+: more accurate predication

-: high management cost


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Performance Metrics

Cost: number of cells paged per call

Paging delay: call arrival until mobileresponds

Success rate of the 1st step - pagingselected cells


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Fixed-profile updated biweekly


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Dynamic ProfileHigh success ratefor data calls


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Dynamic Profile cost vs delay


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Smart paging

Dynamic profile-based>14 days of history data

Voice/SMS:

>most recently visited N cells>top X fraction of most popular cells

Data:

>most recently visited N cells


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Success rate

Fixedprofile

Dynamicprofile

SmartpagingN=10

X=0.95Brisbane2/28

0.87 0.96 0.94

Manhattan

2/26

0.81 0.91 0.90


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Cost and delay tradeoff


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Conclusions

Use large-scale mobility data>mobility and activity>patterns over time

To increase paging efficiency>optimized profile-based

And increase robustness>decrease utilization

>limit cost of data pages

Next: nationwide, data


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Improve Paging