International Journal of Communication Technology for Social Networking Services
Vol. 1, No. 1 (2013), pp.13-16
http://dx.doi.org/10.21742/ijctsns.2013.1.1.02
Soft Computing Approach to evaluate Trust Value of a Node in
VNET with HMM through Supervised Learning
Jitendra Singh Sengar
BVM College of Technology & Management, Gwalior
[email protected]
Abstract
Soft computing approach to find the trust value of the node over the VNET, This paper
present trust value evaluation of the sent message over the network to check their
whether the sender is valid or verified. Through this paper we got trust value in percent
by which it is easy to decide what appropriate action should be taken. Here we have a
trained model through which we could analyze the node authentication and their trust
value. In that model user may enrolled as authentic node or can get trust value by
showing their ID proof and authentic document to get proper identification.
Keywords: TVEP Supervised Training Model, VNET, ANN, HMM
1. Introduction
Whole idea occurs from Hidden Markov Modal, Node’s Trust Value evaluation In
VNET through supervised learning approach of artificial neural network. Three
combinational efforts are used to identify the node in VNET by which node could judge
about it authentication in VNET. When a new node enter in VNET node will capture
Through there attribute then proposed algorithm is applied to got attribute to identify
the node. Node will have to register before getting entry in any ADHOC network zone to
get UID from current ADHOC Zone Through which node could travel safely.
Proposed algorithm will Automatically assign UID & alter id to just assigning their
status ID value when a node move from one ADHOC VNET to another the node will get
the UID on the behalf of information or behavior got from the node behavior in previous
ADHOC VNET than our proposed algorithm will active their working on the behalf of
assigned unique id pattern, that pattern will completely identify and recognize through our
combinational effort of these three approach.
1.1 Hidden Mrkov Modal (HMM)
Sequence of emissions observe by HMM, but do not know the states sequence, the
model generate the emissions.
It got update from observations which come in the form of ratings after direct
experiences or recommendations requested from intermediaries.
1.2 Trust Value Definition
1.2.1. Direct Trust: Established from the previous interactions with the trustee.
1.2.2 Recommended Trust: Measureable from quantifying the trustworthiness of the
trustee in a given context and at a given time point as communicated by intermediary.
1.2.3. Reputation Trust: Trust Value could get through various ways from attribute or
combinational attribute of VNET attribute and Node attribute those are currently being
used that concept is important for communication and network protocol designers when
ISSN: 2205-863x IJCTSNS
Copyright ⓒ 2013 GV School Publication
International Journal of Communication Technology for Social Networking Services
Vol. 1, No. 1 (2013)
creating trust relationships between participating nodes. VNET ADHOC system allots
specific reputation degree of ID to ambulance or police or army vehicle.
2. Moving Pattern Control
2.1 Auto Assign Value
As we divide the network into regions consisting of grids, an optimal moving pattern
for the inter- and intra region levels must be selected. For the intra region level, we select
an extension of the town hall method. Each grid elects a commonly trusted moving
representative, and these nodes move to the capital to exchange intra region trust
information.
2.2 Supervised Learning Approach ANN
The learning approach of artificial neural network is efficiently execute in the
supervised manner i.e., the whole way through which entire result is to be occur or come
out from the possible input provided i.e., the result & the way to train a network. A
algorithm is proposed in complete supervision of trained structure.
3. Proposed Structure & Algorithm
In this proposed algorithm, node identification & node behavior could be identify on
the behalf of their trust value which is evaluated through combinational approach of
HMM and Supervised Learning , the whole pattern is drawn to identify its entire attribute.
That string pattern will work as UID of node in a particular VNET in which node is
currently available. The trained system will check the entire words of allotted specified
value from VNET, which is emitted through hidden markov model, and then the training
is done on the behalf of each word meaning and after getting the proper value at proper
place. It would help to evaluate the percent of trust value correctness if the trust value
evaluation got 100% result then assigned trust value to this node is one i.e. the node is
reputed and it will eligible to get trust certificate from VNET. The entire process of
proposed methodology is shown in above figure SFigure 1.1,
Proposed pattern: SBMP07SA6184
Figure 1. Supervised Learning Applied on Specified Assigned Pattern
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Copyright ⓒ 2013 GV School Publication
International Journal of Communication Technology for Social Networking Services
Vol. 1, No. 1 (2013)
3.1. Proposed Algorithm to Execute Entire Strategy
1. Get input string entered node in VNET.
2. That input string will be on the Network manager guideline
(Specified uid number assigned from VNET).
3. Emit each word from specified assigned pattern [i.e., SBMP07SA6184].
4. After 3rd step perform apply whole patterned string to supervised trained model to
get their trust value.
5. If TV=100% then the node is recommended or authentic.
6. End
Each associated substring has their importance to make the complete specified pattern
of VNET in which node is available. Because allotted pattern from VNET has entire basic
attribute of node through which VNET can easily maintain secure network among node.
Through this system no unwanted node can affect the system. That will reduce the chance
of accident or unauthorized node’s activity.
If the node is in VNET it will got UID from VNET manager our proposed pattern to
VNET manager is like SBMP07SA6184 that character or numeric values combination
build from attribute of the node through which node can be authorized to travel safely in
VNET.
Trained System will Evaluate Trust Value in Percent as Shown in Table 1.
Table 1. Pattern Execution in Trained Modal to Evaluate Trust Value in Percent
S. No.
1
2
Substring of pattern
S
SB
TV in %
0
10
3
4
5
SBM
SBMP
SBMP0
10
25
25
6
7
SBMP07
SBMP07S
50
50
8
9
SBMP07SA
SBMP07SA6
75
75
10
11
SBMP07SA61
SBMP07SA618
75
75
12
SBMP07SA6184
100
SBMP07SA6
SBMP07S
SBMP0
SBM
S
120
100
80
60
40
20
0
SBMP07SA6…
TV in %
TV in %
1 2 3 4 5 6 7 8 9 101112
SG. - Analytic Graphical Representation of Entire Approach
Copyright ⓒ 2013 GV School Publication
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International Journal of Communication Technology for Social Networking Services
Vol. 1, No. 1 (2013)
4. Conclusion
The work provide a more secure and better way to maintain safe & secure journey of
moving node in VNET, Here we have a supervised intelligent algorithm which trained
through emission of each word step by step from hidden markov modal. After emission of
specified and assigned patterned trust value which is identified and verified through
trained and supervised neural network for their authentication after identifying proper
authentication node could travel in VNET otherwise node will not get permission to enter
in network.
Future work the specified pattern would be more complex and would be more secure
from unauthorized access. Approach of this proposed combinational pattern work would
help to establish more secure and safe journey while travelling from one place to another.
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Copyright ⓒ 2013 GV School Publication