This paper presentation we have to show Mathematical Model and explain the Proposed System as well as draw the UML Diagram in Paper presentation.we have to explain algorithm for dictionary based provenance compression for Wireless Sensor Network.

1. PRESENTED BY: MR. AJIT M. KARANJKAR
GUIDED BY: PROF. D.H. KULKARNI
EXAM SEAT NUMBER: 5204
DEPARTMENT OF COMPUTER ENGINEERING.
COLLEGE NAME: STES SMT KASHIBAI NAVALE COLLEGE OF ENGG. VADGAON BK, PUNE
COLLEGE CODE:036
DICTIONARY BASED SECURED
PROVENANCE COMPRESSION FOR
WIRELESS SENSOR NETWORK
STE’S SMT.KASHIBAI NAVALE COLLEGE OF ENGINEERING VADGAON
BK, PUNE

2. CONTENTS
• INTRODUCTION
• MOTIVATION
• OUR CONTRIBUTION
• LITERATURE SURVEY
• OUR PROPOSED APPROCH
• ALGORITHM
• UML DIAGRAM
• PERFORMANCE RESULT ANALYSIS
• CONCLUSION AND FUTURE WORKS
• REFERENCES
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 2

3. INTRODUCTION
• Wireless sensor network sometime called wireless sensor and actuator network.
• WSN is built of node from a few to several hundreds or even thousands where each
node is connected to one sensors. Each sensor network node has typically several parts.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 3

4. INTRODUCTION
• Wireless sensor network is a resource restraint if we consider energy, computation,
memory and limited communication capabilities.
• Provenance helps gather, share and store the information which may lead to privacy
and security concern in wireless sensor network.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 4

5. MOTIVATION

6. • Provenance management for sensor networks introduce several challenges
such as low energy and bandwidth consumption, efficient storage and
secure transmission.
• There are numerous techniques and method proposed for confidentiality,
integrity, and trustworthy of secure provenance transmission in WSN
MOTIVATION
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 6

7. OUR CONTRIBUTION

8. OUR CONTRIBUTION
• We introduce a secure packet sequence number generation mechanism and use the
AM-FM sketch technique to secure the provenance.
• We design an efficient and distributed algorithm for encoding the provenance
information as well as a centralized approach for its decoding.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 8

9. LITERATURE
SURVEY

10. LITERATURE SURVEY
AUTHOR TITLE NAME YEAR SUMMARY
S. Sultana, G. Ghinita,
E.Bertino, and M. Shehab.
A lightweight secure scheme for
detecting provenance forgery and
packet drop attacks in wireless
sensor networks.
2013.
Data are produced at a large number of
sensor node sources and processed in
network.
B. Shebaro, S.
Sultana, S. R. Gopavaram,
and E. Bertino ,
Demonstrating a lightweight data
provenance for sensor networks.
2012 Develop a light weight scheme for securely
transmitting provenance for sensor
Network.
S. Sultana, G. Ghinita,
E. Bertino, and M. Shehab,
A lightweight secure
provenance scheme for wireless
sensor networks
2012.
Lightweight provenance encoding and
decoding scheme based on bloom filters.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 10

11. Cont…..
AUTHOR TITLE NAME YEAR SUMMARY
W. Zhou, Q. Fei, A. Narayan,
A. aeberlen, B. T. Loo,
B. and M. Sherr
Secure network provenance. 2011
Evaluated a SNooPy prototype with three
different example applications: the Quagga
BGP daemon, a declarative implementation
of Chord, and Hadoop MapReduce.
S. M. I. Alam and S. Fahmy
Energy-efficient provenance
transmission in large-scale wireless
sensor networks.
2011
We adapt the probabilities packet
marketing(PPM) approach trace back .
Further two encoding methods and
combine to deal with topological changes
in the network.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 11

12. PAPER PUBLICATION
• Ajit M. Karanjkar , Prof,D.H.Kulkarni “ A Dictionry Basesd secure provenance and
compression For Wireless Sensor Network Part-I” ,International Engineering Research
journal(IERJ) ISSN: 2395-1621 Vol : 2 Issue 1 Feb 2018.
• Mr.Ajit M. Karanjkar , Prof,D.H.Kulkarni ” A Dictionary Based Secure Compression And
Provenance For WSN”at, Sinhgad Institute Of Technology Lonavala, Department of
Computer engineering, Lonavala in CPGCON-2018.
• Mr. Ajit M. Karanjkar , Asst. Prof, D. H. Kulkarni “A Dictionary Based Secure Provenance
and Compression For Wireless Sensor Network(WSNs) Part-II”, International Journal of
Research in Engineering, Technology and Science(IJRETS), ISSN 2454-1915 Vol. VIII, Issue
V, May 2018
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 12

13. AN ILLUSTRATION
• This Dictionary Based Provenance will be applicable for military operations.
• For Home Security.
-
-
-
• For Industrial Machine monitoring.
• For Medical monitoring.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 13

14. OUR PROPOSED
APPROCH

15. OUR PROPOSED APPROCH
Step 1: Start
Step 2: Source
Step 3: Upload File.
Step 4: Select Node
The file is send from Base Station to the next node.
Step 5: Attacker Attack in the Node
When attacker attack in the node then file is not transfer , file is
decrypted and shortest path is Changed the automatically alternate node.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 15

16. CONT….
Step 6: Apply Encryption & Decryption Algorithm.
checking for path is secure or not and send file to aggregate node.
Step 7: Forward File to Destination.
Encoded file receive for aggregate node, then Aggregate node deliver the
file to destination.
Step 8: Stop.
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17. MATHEMATICAL MODEL(1/3)
Average provenance size:
• When the BS receives a packet , it computes the traversed and agr fields in the
provenance.
• The average of Provenance Size for m packets, p1,p2,p3……,pm as follows.
• Where the PSi is the denotes provenance size for packet pi.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 17

18. MATHEMATICAL MODEL(2/3)
Verification Failure Rate:
• BS receives a packets p1,p2,p3……,pm, the verification failure rate (VFR) is defined
as the ratio of the number of packets for which verification fails over the total number
of packets received and m is the total number of received packets.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 18

19. MATHEMATICAL MODEL(3/3)
Total energy consumption:
• Power-TOSSIM Z to measure the amount of energy consumed by each sensor node.
If there are n1,n2, n3,…..,nm nodes in the network, the total energy consumption
(TEC) is computed as follows:
• Where ECni represents the energy consumed by a node ni and m is the total number of
nodes network.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 19

20. ALGORITHM

21. ALGORITHM
Algorithm 1: Provenance Encoding.
Input: (ni,seqi)
Output: prindex=(v,pathIndex)
if ni is a data source node then
prIndex:v = vi
pp = ni
agr = φ ;
prIndex.pathIndex= <ni , φ>
end if
if ni is a forwarder node then
prIndex:v = vi
pp = pp (∪) ni
agr = φ ;
prIndex.pathIndex= <nk , ni >
end if
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 21

22. Cont…
if ni is an aggregator node then
prIndex:v =vi
pp =ni
agr ={ seqi1, seqi2. . . . ; seqiM}
prIndex:pathIndex =<nb1, ni; . . . ; nbM , ni:>
end if
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 22

23. ALGORITHM
Algorithm 2: Provenance Decoding.
Input: pr Index = (v,pathIndex)
Output: T(Vp,Ep)
if the AM-FM verification fails then
drop the received packet
else
if v:agr = φ then
T(Vp,Ep) = Query pathIndex to PPD.
else
¥= number of ’;’ in pathIndex +1
for i = 1 to ¥ do
path i = Query branch i of pathIndex to PPD.
end for
T(Vp,Ep)= (path 1;. . . ; path¥)
end if
end if College Name:-SKNCOE Pune, Exam No:5204 , College code=036 23

24. SYSTEM REQUIREMENT
• HARDWARE REQUIREMENTS:
System : Pentium Dual Core.
Hard Disk : 120 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 1 GB
• SOFTWARE REQUIREMENTS:
Operating system : Windows 7.
Coding Language : JAVA/J2EE
Tool : Netbeans 7.2.1
Database : MYSQL
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25. UML DIAGRAM

26. CLASS DIAGRAM
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27. USE CASE DIAGRAM
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28. ACTIVITY DIAGRAM
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29. PERFORMANCE
RESULT & ANALYSIS

30. PERFORMANCE RESULT & ANALYSIS
0
1
2
3
4
5
6
7
8
9
10
500 1000 1500 2000 2500 3000
AverageProvenanceSize(Bytes)
Number Of Packets Sent By 15 Data Source Node
DP BFP
Figure 1: Average Provenance Size
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 30

31. FUTURE WORK
&
CONCLUSION

32. FUTURE WORK
• Although the provenance for a packet only records the packet’s forwarding and
aggregation information, the average provenance size expands with the increases of
the packet transmission hops and the amount of nodes in a WSN.
• Provenance scheme combines the block provenance methods and the lossless
provenance compression methods.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 32

33. CONCLUSION
• In WSN Using packet path dictionaries, we enclose path indexes alternative of the
path itself in the provenance.
• Hence, the size of the compressed provenance in our lossless approach is smaller than
that of the existing lossy provenance schemes.
• By using the AM-FM sketch scheme and a secure packet sequence number generation
technique, we ensure the security objectives of our scheme.
• Simulation and experimental results show that our scheme can save more energy and
bandwidth than other existing provenance schemes.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 33

34. REFERENCES
[1]S. Sultana, G. Ghinita, E. Bertino, and M. Shehab, “A lightweight secure scheme for
detecting provenance forgery and packet drop attacks in wireless sensor networks,”
IEEE Trans. Dependable Secure Comput., vol. PP, no. 99, p. 1, 2013.
[2]B.Shebaro,S.Sultana,S.R.Gopavaram,and E. Bertino,“Demonstrating a lightweight
data provenance for sensor networks,” in Proc.ACM Conf. Comput. Commun. Security,
2012,pp. 1022–1024.
[3]S. Sultana, G. Ghinita, E. Bertino, and M. Shehab, “A lightweight secure provenance
scheme for wireless sensor networks,” in Proc. IEEE 18th Int. Conf. Parallel Distrib.
Syst., 2012, pp. 101–108.
[4]W. Zhou, Q. Fei, A. Narayan, A. Haeberlen, B. T. Loo, and M. Sherr, “Secure
network provenance,” in Proc. 23rd ACM Symp. Oper. Syst. Principles, 2011, pp. 295–
310.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 34

35. REFERENCES
[5]S. M. I. Alam and S. Fahmy, “Energy-efficient provenance transmission in large-scale
wireless sensor networks,” in Proc. IEEE Int. Symp. World Wireless, Mobile Multimedia
Netw., 2011, pp. 1–6.
[6]S. Sultana, E. Bertino, and M. Shehab, “A provenance based mechanism to identify
malicious packet dropping adversaries in sensor networks,” in Proc. 31st Int. Conf.
Distrib. Comput. Syst. Workshops, 2011, pp. 332–338.
[7]H.-S. Lim, Y.-S. Moon, and E. Bertino, “Provenance-based trust worthiness
assessment in sensor networks,” in Proc. 7th Int. Workshop Data Manage. Sensor Netw.,
2010, pp. 2–7.
College Name:-SKNCOE Pune, Exam No:5204 , College code=036 35

36. LITERATURE
SURVEY