22. integration of false data detection with data aggregation and confidential transmission in...
TRANSCRIPT
-
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
1/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Integration of False Data Detection
with Data Aggregation
and Confidential Transmission
in Wireless Sensor Networks
S. Ozdemir H. Cam
IEEE
IEEE/ACM Transactions on Networking, 2009
Presented by Gowun Jeong
1 / 4 7
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
2/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
2 / 4 7
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
3/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
3 / 4 7
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
4/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Security Vulnerability of Wireless Sensor Networks
Security attacks False Data Injection (FDI)
Compromised nodes (CNs) decrease data integrity.
Data Forgery Eavesdropping
Where FDI by CNs possibly occurs?
Data Aggregation (DA) Data Forwarding (DF)
False data transmission depletes the constrained battery power; and the bandwidth utilisation.
4 / 4 7
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
5/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Security Vulnerability of Wireless Sensor Networks
Security attacks False Data Injection (FDI)
Compromised nodes (CNs) decrease data integrity.
Data Forgery Eavesdropping
Where FDI by CNs possibly occurs?
Data Aggregation (DA) Data Forwarding (DF)
False data transmission depletes the constrained battery power; and the bandwidth utilisation.
5 / 4 7
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
6/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Security Vulnerability of Wireless Sensor Networks
Security attacks False Data Injection (FDI)
Compromised nodes (CNs) decrease data integrity.
Data Forgery Eavesdropping
Where FDI by CNs possibly occurs?
Data Aggregation (DA) Data Forwarding (DF)
False data transmission depletes the constrained battery power; and the bandwidth utilisation.
6 / 4 7
C
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
7/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Security Vulnerability of Wireless Sensor Networks
Security attacks False Data Injection (FDI)
Compromised nodes (CNs) decrease data integrity.
Data Forgery Eavesdropping
Where FDI by CNs possibly occurs?
Data Aggregation (DA) Data Forwarding (DF)
False data transmission depletes the constrained battery power; and the bandwidth utilisation.
7 / 4 7
I t d ti A ti d Li it ti DAA P f A l i C l i
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
8/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
False Data Detection (FDD)
Conventional work
Most discussed FDD during DF.
Challenge! Any data change between twocommunicating endpoints is considered as
FDI. Ozdemir and Cams approach
attempts to correctly determine whether any data alterationis due to DA or FDI.
A Data Aggregation and Authentication protocol
against up to T CNs over the encrypted data for FDD both by a data aggregator and by a non-aggregating
node
8 / 4 7
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
9/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
False Data Detection (FDD)
Conventional work
Most discussed FDD during DF.
Challenge! Any data change between twocommunicating endpoints is considered as
FDI. Ozdemir and Cams approach
attempts to correctly determine whether any data alterationis due to DA or FDI.
A Data Aggregation and Authentication protocol
against up to T CNs over the encrypted data for FDD both by a data aggregator and by a non-aggregating
node
9 / 4 7
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
10/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
False Data Detection (FDD)
Conventional work
Most discussed FDD during DF.
Challenge! Any data change between twocommunicating endpoints is considered as
FDI. Ozdemir and Cams approach
attempts to correctly determine whether any data alterationis due to DA or FDI.
A Data Aggregation and Authentication protocol
against up to T CNs over the encrypted data for FDD both by a data aggregator and by a non-aggregating
node
10/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
11/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
False Data Detection (FDD)
Conventional work
Most discussed FDD during DF.
Challenge! Any data change between twocommunicating endpoints is considered as
FDI. Ozdemir and Cams approach
attempts to correctly determine whether any data alterationis due to DA or FDI.
A Data Aggregation and Authentication protocol
against up to T CNs over the encrypted data for FDD both by a data aggregator and by a non-aggregating
node
11/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
12/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
12/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
13/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
Basic Assumptions
Network
A densely deployed sensor network of certain large size
Sensor
Overlapping sensing ranges
Role change Sensor nodes rotatively assumes the role of data aggregator.
Limited computation and communication capabilities
Message
Time-stamped Nonce used to prevent reply attacks
Intrusion ways to compromise nodes
Physical capturing Radio communication channel attack
13/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
14/47
p y
Network Topology
Data aggregators are chosen in such a way that1. there are at least T nodes, called forwarding nodes, on
the path between any two consecutive data aggregators;and
2. each data aggregator has at least T neighbouring nodes.
14/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
15/47
p y
Generation of MACs
Only data aggregators encrypt and decrypt the aggregated
data.
The forwarding nodes first verify data integrity using MACs
and then relay the data if it is not false. Two Full-size MACs (FMACs), each of which consisting of
T + 1 subMACs, for a pair of plain and encrypted data
One computed by a data aggregator T subMACs generated by its T monitoring nodes
The same Pseudo-Random Number Generator (PRNG),termed f
Random numbers between 1 and 32
15/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
16/47
Generation of MACs
subMAC generation of data D by neighbouring node Ni ofdata aggregator Au for its pairmate Fj
1. Establish the shared key Ki,j between Ni and Fj.2. Compute MAC(D) using Ki,j.3. Assuming that S denotes the size of MAC(D) in bits, selects
S/(T + 1) bits to form subMAC(D) using its PRNG and Ki,jas the seed.
subMAC verification of D by Fj for its pairmate Ni1. Compute the MAC(D).2. Run its PRNG S/(T + 1) times to generate subMAC(D)
with Ki,j as the seed.3. Compare two subMAC(D)s.
PRNG synchronisation achieved by packet sequence
numbers
16/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
17/47
Key Establishment
Pairwise key establishment Sybil attacks
A compromised node fakes multiple identities to establish
pair relations with more than one monitoring nodes. To prevent from Sybil attacks, a monitoring node can share
a pairwise key with another node in multiple hops.
Group key establishment
Group key Kugroup for data aggregator Au and its
neighbouring nodes is used to select the monitoring nodesand to protect data confidentiality while data transmitting.
17/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
18/47
Limitations
The value of T depends strictly on several factors, such as
geographical area conditions, modes of deployment, and
so on.
The pairwise key establishment between non-neighbouring
nodes takes more time than that between direct
neighbouring nodes.
Compromising only one legitimate group member
discloses not only some or all of the past group keys butalso the current group key.
18/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
19/47
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
19/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
20/47
Notations used in DAA
20/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
21/47
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)
Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
21/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
22/47
Algorithm MNS (Monitoring Node Selection)
Table: Choose T monitoring nodes from n neighbouring nodes of Au
1. Au all nodes request two random numbers with node ID
2. Ni Au Ra and Rb generated by f(Ku,i)MACKu,i(Ra | Rb)3. Au all nodes {N1, . . . , Nn} in the receiving order
{R1, . . . , R2n} labeled in an ascending orderMACKugroup(R1 | | R2n)
4-1. Ni Au (verified)EKu,
i
(MACKugroup
(R1 | | R2n))
4-2. Ni Au, Njs (unverified)restart from 1.5. Ni for 1 k T, compute
Ik = [(n1+k
j=k Rj + Ku
group)mod(n)] + 1
to determine T monitoring node IDs of Au
22/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
23/47
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)
Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
23/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
24/47
Three Types of Node Pairs
2T + 1 node pairs are formed.
AA-type pair One pair between Au and AfMF-type pair T pairs between M
kof Au
and Fj towards AfMN-type pair T pairs between Mk of Au
and Ni of Af
T Mks selected in Step 1 distinctly choose
their own pairmates to form MF-type andMN-type pairs.
24/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
25/47
Pairmate Selection
1. Af Fj Au pairmate discovery messageNis of AfMACKf,u(Nis)
Fjs IDs for 1 j h2. Au T Mks MACKugroup(F1 | | Fh) for new, random
forwarding node labelingMACKugroup(Nis)s
3. Mk Au one forwarding node
one neighbouring node4. Au T Mks two pairmate lists of size T5. Mk pairmate verification
25/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
26/47
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)
Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
26/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
27/47
Data Confidentiality
One pairmate computes a subMAC, and the otherpairmate verifies the subMAC.
subMACs for plain data are used for FDD during DA.
subMACs for encrypted data are used for FDD during DF.
Each data aggregator forms two FMACs as the followingfigure.
Au determines the order of subMACs and informs eachforwarding node about its subMAC location individually.
probability of FDI at a forwarding node = (1/2)32
27/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
28/47
Algorithm SDFC
28/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
29/47
Algorithm SDFC
29/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
30/47
Algorithm SDFC
30/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
31/47
Algorithm SDFC
31/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
32/47
Algorithm SDFC
32/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
33/47
Algorithm SDFC
33/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
34/47
Algorithm SDFC
34/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
35/47
Algorithm SDFC
35/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
36/47
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)
Step 1: Monitoring Node Selection for an Aggregator
Step 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
36/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
37/47
Security Analysis of Algorithm SDFC
Lemma 1Assuming that Au is compromised and there are additional at
most T 1 collaborating compromised nodes among theneighbouring nodes of Au and Af, any false data injected by Auare detected by the Afs neighbouring nodes only in SDFC.
Data verification by the monitoring nodes of Au and the
neighbouring nodes of Af
Lemma 2
Assuming that Au and Af are not compromised, any false datainjected by any subset of Aus forwarding nodes are detected by
Af in SDFC.
Data verification by Af
37/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
38/47
Security Analysis of FMAC and subMAC
Changing the size of MAC
Security Level vs. Communication Overhead Probability of FDI at a node = (1/2)32 for 4-byte FMACs
Probability of FDI into a subMAC = (1/2)32/(T+1)
The size of FMAC = T + 1
38/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
39/47
Security Analysis of FMAC and subMAC
Changing the size of MAC
Security Level vs. Communication Overhead Probability of FDI at a node = (1/2)32 for 4-byte FMACs
Probability of FDI into a subMAC = (1/2)32/(T+1)
The size of FMAC = T + 1
39/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
40/47
Computational Cost of Algorithm SDFC
Computation Traditional Work SDFCMAC 1 4(T + 1)
= (T + 1) subMACs 2 FMACs a pair
Aggregation 1 T + 1
= 1 by aggregator+ T by monitors
Encryption/ 1 T + 2Decryption = 1 encryption by Au
+ T decryptions by monitors
+1 decryption by A
f
Only the first MAC computation consumes much resource.
Data transmission requires much more energy than data
computing in wireless sensor networks.
40/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
41/47
Communication Cost of Algorithm SDFC
DADD the amount (in bytes) of data transmission using ADD of two FMACsDtradAuth the amount (in bytes) of data transmission using the traditional scheme of a MAC
Ltos the length (in bytes) of an authenticated and encrypted data packet
the number of data packets generated by legitimate nodes
the number of false data packets injected by up to T compromised nodes
Hd the average number of hops between two consecutive data aggregators
H the average number of hops that a data packet travels in the network
DADD = (Ltos + 4)(H+ Hd) + T(Ltos + 4)( + ) +4T
T + 1( + )
DtradAuth = LtosH( + )
data transmission by a data aggregator
data transmission by T monitors
subMACs transmission by T monitors
41/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://goforward/http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
42/47
Communication Cost of Algorithm SDFC
DADD the amount (in bytes) of data transmission using ADD of two FMACsDtradAuth the amount (in bytes) of data transmission using the traditional scheme of a MAC
Ltos the length (in bytes) of an authenticated and encrypted data packet
the number of data packets generated by legitimate nodes
the number of false data packets injected by up to T compromised nodes
Hd the average number of hops between two consecutive data aggregators
H the average number of hops that a data packet travels in the network
DADD = (Ltos + 4)(H+ Hd) + T(Ltos + 4)( + ) +4T
T + 1( + )
DtradAuth = LtosH( + )
data transmission by a data aggregator
data transmission by T monitors
subMACs transmission by T monitors
42/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
43/47
Communication Cost of Algorithm SDFC
DADD the amount (in bytes) of data transmission using ADD of two FMACsDtradAuth the amount (in bytes) of data transmission using the traditional scheme of a MAC
Ltos the length (in bytes) of an authenticated and encrypted data packet
the number of data packets generated by legitimate nodes
the number of false data packets injected by up to T compromised nodes
Hd the average number of hops between two consecutive data aggregators
H the average number of hops that a data packet travels in the network
DADD = (Ltos + 4)(H+ Hd) + T(Ltos + 4)( + ) +4T
T + 1( + )
DtradAuth = LtosH( + )
data transmission by a data aggregator
data transmission by T monitors
subMACs transmission by T monitors
43/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
44/47
Communication Cost of Algorithm SDFC
DADD the amount (in bytes) of data transmission using ADD of two FMACsDtradAuth the amount (in bytes) of data transmission using the traditional scheme of a MAC
Ltos the length (in bytes) of an authenticated and encrypted data packet
the number of data packets generated by legitimate nodes
the number of false data packets injected by up to T compromised nodes
Hd the average number of hops between two consecutive data aggregators
H the average number of hops that a data packet travels in the network
DADD = (Ltos + 4)(H+ Hd) + T(Ltos + 4)( + ) +4T
T + 1( + )
DtradAuth = LtosH( + )
data transmission by a data aggregator
data transmission by T monitors
subMACs transmission by T monitors
44/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
45/47
Cost Comparison
Ltos = 41, H = 50, Hd 12 and / 0.2
Comparing (a) and (b), DADD more mildly increases than
DtradAuth.
(c) shows that the value of T trades off between security
and computation overhead in the network.
(c) also illustrates the impact of data aggregation.
45/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
46/47
Outline
Introduction
Assumptions and Limitations
Data Aggregation and Authentication Protocol (DAA)
Step 1: Monitoring Node Selection for an AggregatorStep 2: Sensor Node Pairing
Step 3: Integration of Secure Data Aggregation and False
Data Detection
Performance Analysis
Conclusion
46/47
Introduction Assumptions and Limitations DAA Performance Analysis Conclusion
http://find/http://goback/ -
7/31/2019 22. Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks
47/47
Contributions and Future Work
Contributions
False data detection during data aggregation Integration of data confidentiality and false data detection
Less communication overhead (by fixing the size of eachFMAC)
Future work
Security and efficiency improvement in networks whereevery sensor enables data forwarding and aggregation at
the same time
47/47
http://find/http://goback/