International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
An Empirical Trust Computation Method In Agent
Based System
Preeti Nutipalli1, Tammineni Ravi Kumar2
1
M.Tech Scholar, 2 Sr.Assistant Professor
1,2
Dept of Computer Science & Engineering, in Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh
Abstract:-Now a day’s security and privacy issues have
become critically important with the fast expansion of multi
agent systems(MAS).Most network applications such as grid
computing , and p2p n/w ‘s can be viewed as MAS which are
open, anonymous, and dynamic in nature. Such
characteristics of MAS introduce vulnerabilities and threats
to providing secured communication when malicious agents
appear. One feasible way to minimize the threats is to
evaluate the trust and reputation of the interacting agents
Most trust/computation models works on statistical
measures(i.e. Direct and indirect trust computations)which
are not optimal to address this issue. So, to cope with the
strategically altering behavior of malicious agents; we present
in this paper an efficient trust computation method, which is
based on classification for analyzing the nodes or user when
connects to the network.
Key Terms: Multi agent systems, trust management, trust and
computation models
from interactions with agent b and the various relationships
that may exist between them (e.g. owned by the same
organization, relationships derived from relationships
between the agents’ owners in the real life such as
friendship or relatives, relationships between a service
provider agent and its registered consumer agents). The
latter consists of observations by the society of agent b’s
past behavior (here termed its reputation). These indirect
observations are aggregated in some way to define agent
b’s past behavior based on the experiences of all the
participants in the system.[5][6][7].
Agent
Interaction
Organizational
Relationship
1 INTRODUCTION
A wide variety of networked computer systems (such as
the Grid, the Semantic Web, and peer-to-peer systems) can
be viewed as multi-agent systems (MAS) in which the
individual components act in an autonomous and flexible
manner in order to achieve their objectives [1]. An
important class of these systems is those that are open; here
defined as systems in which agents can freely join and
leave at any time and where the agents are owned by
various stakeholders with different aims and objectives.
From these two features, it can be assumed that in open
MAS: (1) the agents are likely to be unreliable and self
interested; (2) no agent can know everything about its
environment; and (3) no central authority can control all
the agents. Despite these many uncertainties, a key
component of such systems is the interactions that
necessarily have to take place between the agents.
Moreover, as the individuals only have incomplete
knowledge about their environment and their peers, trust
plays a central role in facilitating these interactions [1- 4].
Specifically, trust is here defined as the subjective
probability with which an agent a assesses that another
agent b will perform a particular action, both before a can
monitor such action and in a context in which it affects its
own action (adapted from [4]). Generally speaking, trust
can arise from two views: the individual and the society
level. The former consists of agent a’s direct experiences
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Sphere of
Visibility &
interaction
Environment
Canonical view of MAS
Given its importance, a number of computational models
of trust and reputation have been developed, but none of
them are well suited to open MAS. Specifically, given the
above characteristics, in order to work efficiently in open
MAS, a trust model needs to possess the following
properties:
1. It should take into account a variety of sources of trust
information in order to have a more precise trust measure
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
(by cross correlating several perspectives) and to cope with
the situation that some of the sources may not be available.
2. Each agent should be able to evaluate trust for itself.
Given the ‘no central authority’ nature of open MAS,
agents will typically be unwilling to rely solely on a single
centralized trust/reputation service.
3. It should be robust against possible lying from agents
(since the agents are self-interested).
2 RELATED WORK
A wide variety of trust and reputation models have been
developed in the last few years (e.g. [1], [4]
[5][7][9][10]This section reviews a selection of notable
models and shows how computational trust models have
evolved in recent years with a particular emphasis on their
applicability in open MAS Specifically that derive trust
using certificates, rules, and policies. Second, surveys the
popular trust models that follow the centralized
approaching which witness observations are reported to a
central authority. Finally notable models that follow the
decentralized approach in which no central authority is
needed for trust evaluations. From now on, for the
convenience in referring agents, we call the agent
evaluating the trustworthiness of another the evaluator, or
agent a; and the agent being evaluated by a the target
agent, or agent b.
As can be seen in the previous section, trust can come
from a number of information sources: direct experience,
witness information, rules or policies. However, due to the
openness of MAS, the level of knowledge of an agent
about its environment and its peers may vary greatly during
its life cycle. Therefore, at any given time, some
information sources may not be available, or adequate, for
deducing trust. For example, the following situations may
(independently) happen:
– An agent may never have interacted with a given target
agent and, hence, its experience
cannot be used to deduce how trustworthy the target agent
is.
– An agent may not be able to locate a witness for the
target agent (because of a lack of knowledge about the
target agent’s society) and, therefore, it cannot obtain
witness information about that agent’s behaviors.
– The current set of rules to determine the level of trust is
not applicable for the target
In such scenarios, trust models that use only one source of
information will fail to provide a trust value of the target
agent. For that reason, FIRE adopts a broader base of
information than has hitherto been used for providing trust-
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related information. Although the number of sources that
provide trust-related information can be greatly varied from
application to application, we consider that most of them
can be categorized into the four main sources as
follows:
– Direct experience: The evaluator uses its previous
experiences in interacting with the
target agent to determine its trustworthiness. This type of
trust is called Interaction Trust.
– Witness information: Assuming that agents are willing to
share their direct experiences,
the evaluator can collect experiences of other agents that
interacted with the target agent.
Such information will be used to derive the trustworthiness
of the target agent based on the views of its witnesses.
Hence this type of trust is called Witness Reputation.
– Role-based rules: Besides an agent’s past behaviors
(which is used in the two previous types of trust), there are
certain types of information that can be used to deduce
trust. These can be the various relationships between the
evaluator and the target agent or its knowledge about its
domain (e.g. norms, or the legal system in effect). For
example, an agent may be preset to trust any other agent
that is owned, or certified, by its owner; it may trust that
any authorized dealer will sell products complying to their
company’s standards; or it may trust another agent if it is a
member of a trustworthy group.4 Such settings or beliefs
(which are mostly domain-specific) can be captured by
rules based on the roles of the evaluator and the target
agent to assign a predetermined trustworthiness to the
target agent. Hence this type of trust is called Role-based
Trust.
– Third-party references provided by the target agents: In
the previous cases, the evaluator needs to collect the
required information itself. However, the target agent can
also actively seek the trust of the evaluator by presenting
arguments about its trustworthiness. In this paper, such
arguments are references produced by the agents that have
interacted with the target agents certifying its behaviors.
However, in contrast to witness information which needs to
be collected by the evaluator, the target agent stores and
provides such certified references on request to gain the
trust of the evaluator. Those references can be obtained by
the target agent (assuming the cooperation of its partners)
from only a few interactions, thus, they are usually readily
available. This type of trust is called Certified Reputation.
Apart from these traditional trust computation methods we
are introducing an efficient and novel approach for
calculating the trust of the agent or node in multi agent
systems.
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3 PROPOSED WORK
Now a day’s identifying the unauthorized user in network
is still an important research issue during the peer to peer
connections. Networks are protected using many firewalls
and encryption software’s. But many of them are not
sufficient and effective. Most trust computation systems for
mobile ad hoc networks are focusing on either routing
protocols or its efficiency, but it fails to address the
security issues. Some of the nodes may be selfish, for
example, by not forwarding the packets to the destination,
thereby saving the battery power. Some others may act
malicious by launching security attacks like denial of
service or hack the information. The ultimate goal of the
security solutions for wireless networks is to provide
security services, such as authentication, confidentiality,
integrity, anonymity, and availability, to mobile users. This
paper incorporates agents and data mining techniques to
prevent anomaly intrusion in mobile adhoc networks.
Home agents present in each system collect the data from
its own system and using data mining techniques to
observe the local anomalies. The Mobile agents monitoring
the neighboring nodes and collect the information from
neighboring home agents to determine the correlation
among the observed anomalous patterns before it will send
the data. This system was able to stop all of the successful
attacks in an adhoc networks and reduce the false alarm
positives.
In our approach we proposes an efficient classification
based approach for analyzing the anonymous users and
calculates the trust measures based on the training data
with the anonymous testing data. Our architecture
contributes with the following modules like Analysis agent,
Neighbor hood node, Classifier and data collection and
preprocess as follows
1) Analysis agent –Analysis agent or Home Agent is
present in the system and it monitors its own system
continuously. If an attacker sends any packet to gather
information or broadcast through this system, it calls the
classifier construction to find out the attacks. If an attack
has been made, it will filter the respective system from
the global networks.
Normal profile is created using the data collected during
the normal scenario. Attack data is collected during the
attack scenario.
4) Data pre-process - The audit data is collected in a file and
it is smoothed so that it can be used for anomaly
detection. Data pre-process is a technique to process the
information with the test train data. In the entire layer
anomaly detection systems, the above mentioned preprocessing technique is used
For the classification process we are using Bayesian
classifier for analyzing the neighbor node testing data with
the training information. Bayesian classifier is defined by a
set C of classes and a set A of attributes. A generic class
belonging to C is denoted by cj and a generic attribute
belonging to A as Ai. Consider a database D with a set of
attribute values and the class label of the case. The training
of the Naïve Bayesian Classifier consists of the estimation
of the conditional probability distribution of each attribute,
given the class.
In our example we will consider a synthetic dataset which
consists of various anonymous and non anonymous users
node names, type of protocols and number of packets
transmitted and class labels, that is considered as our
feature set C (c1,cc,……cn) for training of system and
calculates overall probability for positive class and
negative class and then calculate the posterior probability
with respect to all features ,finally calculate the trust
probability.
Algorithm to classify malicious agent
Sample space: set of agent
H= Hypothesis that X is an agent
P(H/X) is our confidence that X is an agent
P(H) is Prior Probability of H, ie, the probability that any
given data sample is an agent regardless of its behavior
P(H/X) is based on more information, P(H) is independent
of X
2) Neighbouring node - Any system in the network
transfer any information to some other system, it
broadcast through intermediate system. Before it
transfer the message, it send mobile agent to the
neighbouring node and gather all the information and it
return back to the system and it calls classifier rule to
find out the attacks. If there is no suspicious activity,
then it will forward the message to neighbouring node.
Estimating probabilities
3) Data collection - Data collection module is included for
each anomaly detection subsystem to collect the values
of features for corresponding layer in an system.
Steps Involved:
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P(X), P(H), and P(X/H) may be estimated from
given data
Bayes Theorem
P(H|X)=P(X|H)P(H)/P(X)
1.
Each data sample is of the type
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X=(xi) i =1(1)n, where xi is the values of X for attribute Ai
2.
Suppose there are m classes Ci, i=1(1)m.
X Î Ci iff
P(Ci|X) > P(Cj|X) for 1£ j £ m, j¹i
i.e BC assigns X to class Ci having highest posterior
probability conditioned on X
The class for which P(Ci|X) is maximized is called the
maximum posterior hypothesis.
From Bayes Theorem
2.

If class prior probabilities not known, then assume
all classes to be equally likely

Figure 1
P(X) is constant. Only need be maximized.
Otherwise maximize
Figure 1 represents the training information which consists
of the characteristics of the agents when connects to the
neighborhood node, it takes the node names, destination
code it is connected, number of packets transmitted to the
node and type of protocol used to connect.
P(Ci) = Si/S
Problem: computing P(X|Ci) is unfeasible!
4.
Naïve assumption: attribute independence
P(X|Ci) = P(x1,…,xn|C) = PP(xk|C)
5.
In order to classify an unknown sample X,
evaluate for each class Ci. Sample X is assigned to the
class Ci iff P(X|Ci)P(Ci) > P(X|Cj) P(Cj) for 1£ j £ m, j¹i
While comparing the traditional measure with our novel
approach with following measures in figure2, it shows the
common trust with individual characteristics but not with
the trust measure, because the final trust measure
calculated in novel approach with the classification
mechanism it analyzes with the positive and negative
detections.
4 EXPERIMENTAL ANALYSIS
Traditional trust computation measures based on static
measures direct trust, indirect trust, recent trust, Historical
trust and expected trust all these trust measures leads to the
score of trust computation, it may not leads to optimal trust
computation.
In our novel approach we are considering the
characteristics of the agents, from the nodes we can analyze
the agent when connects to the other agent by using the
classifier instead of gathering the information from the
other sources and trusting and rely on others. Synthetic
dataset can be shown as follow
Figure 2
5 CONCLUSION
To summarize, we have identified the questions to be
addressed when trying to find a solution to the problem of
trust assessment based on reputation management in a
multiagent systems a simple, yet robust method that shows
that a solution to this problem is feasible. In this approach
data about the observed behavior about all other agents and
from all other agents is made directly available for
evaluating trustworthiness using the naïve Bayesian
classification mechanism by considering the feature sets or
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 9- Sep 2013
characteristics of the agent. This allows to compute directly
the expected outcome respectively risk involved in an
interaction with an agent, and makes the level of trust
directly dependent on the expected utility of the interaction.
Nutipalli Preeti Completed MCA from Maharaja Post
Graduate College, Vizianagram and she pursuing M.Tech
in Aditya Institute of Technology
and Management,
Tekkali. Her Interested areas are Mobile Computing,
Disributed & parallel computing.
REFERENCES
Mr. Tammineni Ravi Kumar received the
BTech in CSIT and MTech in CSE Engg
degrees from the Department of
Computer Science and Engineering at the
Jawaharlal
Nehru
Technological
University,
Hyderabad(JNTUH)
respectively. He is currently working as
Sr.Asst.Prof. in Department of Computer Science &
Engineering, in Aditya Institute of Technology and
Management, Tekkali, Andhra Pradesh, India. He has 7
years of experience in teaching Computer Science and
Engineering related subjects. His Research interests include
Software Engineering, Image Processing and Distributed &
Parallel Computing.
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