International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number6–Nov2014
A Manageable Framework for Preserving Exclusive Data in Social
Networks
1
A.v.swathi,2C.P.V.N.J Mohan Rao
1
Final M.Tech Student, 2Professor
1,2
Dept of Computer Science and Engineering, Avanthi Institute of Engineering & Technology - Narsipatnam.
Abstract: In social networks there are so many threats such
as impersonation, data leakage. Most of the social networks
are more worried about private data leakage. Some of them
as intruders by deploying the security attacks like denial of
service or hack the information. So we introduced a
framework which contains agents on every Peer and they
monitor every Peer to maintain confidentiality, anonymous
etc. The main goals of these methods are to maintain
privacy of private data. So we additionally introduced a
classifier to predict that connected Peer is secure or then
proceed to share data.
I.INTRODUCTION
Internet privacy refers as the right of self-privacy
regards to the storing and provision to other parties and
showing of information to oneself through the Internet.
Web privacy is a part of system privacy and the privacy
affects have been communicated from the starting of large
scale computer sharing. Privacy can necessitate either
Personally Finding Information (P-II) or non-PII
information consists of site visitor behavior on a website.
P-II is any information that can be used to find an
individual. Consider an example age and address only
could find an individual is without directly imparting their
name and there are two factors are isolated enough to
typically find a particular person.
People with only a general affect for web privacy
need not attain total anonymous. The web users may or
may not protect their personal information using controlled
disclosure. The revelation of IP addresses and the nonpersonally-identifiable profiling and it is same information
would become acceptable trade-offs for the satisfaction and
users could otherwise lose using the workarounds need to
compress such details rigorously. On the other hand and
some of the people desire much stronger privacy. In that
situation they may try to attain Internet anonymous to
ensure privacy and use of the Internet without giving any
information to other members the ability to link the web
bustle to personally able to find information of the web
user. To keep their information private the people need to
be very careful with the information what they submit.
Filling of forms and buying in merchant sites that becomes
trace and because the data was not private and present days
the companies are used to send spam and advertising on
same products.
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There are also so many government companies
that protect user privacy and anonymous issues on the
Internet. There is an article by the FTC, a number of
indicators were brought to alert that helps user in internet
to avoid possible personal information theft and other
cyber-attacks. The process of blocking the usage of online
is to wary and respected emails consists spam messages of
financial details and those are creating and manipulating
strong passwords.
Sharing things on the Internet can be danger of
attacks. There are some information shared on web is
permanent and depending on service and privacy methods
of some services offered online. It consists of comments on
blogs and pictures. It is merged into cyberspace and once it
is shared to anyone can find it and access it. Some
employers started research a possible employee by
searching online for the details of their online hiking
patterns as possibly affecting the output of the success of
the candidate.
Companies are showing interest to watch what
web sites people visit and then use that information for
sample by sending advertising based on individual surfing
history. There are several ways in which people can
broadcast their information for once by use of channel and
by sending the bank and credit card information to different
websites. And directly remark the behavior such as
browsing logs and search queries of the social website
profile can be automatically processed to refer more
unwanted details about an individual such as political and
religious.[8]
Those affected about web privacy frequently
reproduce a number of privacy risks and events that can
accommodate the privacy which may be confront through
web use.[9] These ranges are from the collection of
statistics on users to more viral acts such as the distributes
spyware and deploying of various forms of software faults.
There are several social websites try to protect the self-data
of their payers. Consider an example the security settings
are available to all registered users and they can block
particular users from viewing their profile data they can
choose their friends and they can terminate who has access
to pictures and videos. The security settings are also
available on other social networking sites such as Google
Plus and Twitter as well as Facebook. The user can apply
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number6–Nov2014
such settings at the time of providing the personal
information on the internet.
Children also using the web including social
networks in which risk their privacy and a cause for
increasing affect among parents. They must be informed
that about all these risks. Consider an example on Twitter
threats include shortlisted links that lead them to harmful
places. In their e-mail inbox and there are some threats that
includes email scams and attachments that get them to
install malware software and reveal their personal
information. When using a smartphone and the threats
include geo-location meaning that phone can detect where
they are located and share that information in online for all
to view. All users can protect themselves by updating the
virus security which is using security settings and
downloading the patches installing a firewall and
controlling cookie programs using encryption method
refuse the browser hijackers.
removing the user details of the Peers before hosting the
actual graph does not always provides privacy. Another
one is to observe that the structural similarity of Peers
neighborhood in the graph explains the the extent to which
an individual in the network can be distinguished. This
structural data is closely linked to the degrees of the Peers
and their neighbors.
The researchers propose an anonymous model for
social networks and a graph achieves k-anonymous and for
every structure query over the graph and there exists at
least k Peers that match the query. The structure queries
checks the presence of neighbors of a Peer or the structure
of the sub graph in the territory of a Peer. The researchers
mostly focus on giving a set of anonymous symptoms and
studying properties of them and not on designing methods
that bond the construction of a graph that obeys their
anonymous requirements.
III.PROPOSED WORK
II. RELATED WORK
Issues of social networking sites
The appearance of the Web has caused social
profile and is a growing affect for web privacy. Web is the
system that provides the participatory information sharing
on the web and in the social networking websites like
Facebook and orkut. These social networking sites have
seen a more famous in their popularity started from many
years ago. These websites leads many people are giving
their personal information on the internet.
It has been a concept of discussion is held
responsible for the collection and distribution of personal
data. Some of them will say that it is the mistake of the
social websites because they are storing large amounts of
information. It is the users who are responsible for the
situation because of the users themselves that provide the
data in the first place. This can relate to the ever present the
issue of how people regard social media websites.
These are increasing the number of people that are
finding the risks of keeping their self-information online
and believe a website to keep it private. The information is
not completely private for long time. There is an increase
of risk because of people of below age of 20 are having
easy internet access than ever before and so they place
themselves in a position where all very easy for them to
upload information.
This is because of the fast involving of digital
media and the people’s explanation of privacy is emerging
very well and it is crucial to consider that when
communicating in online. Sometimes it may be compulsory
to take extra safeguard the situations where someone else
may have a closer view on privacy rules. In recent work
point out that the simple method of anonymous graphs by
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Our work is completely on attribute based method
which has been used to point out security problems
regarding to attacks in social networks. This paper
implements novel method such as data mining and
different agents to provide solutions for social networks.
Our Proposed work privileges the three techniques to
provide serve security solution to Self Peer, Friendly Peer
and Central Networks. The below figure shows the
architecture of the system to reduce the attacks in social
networks. The following explains that each agent’s work in
detail.
A. Self-agent
Self-agent is exists in each system and it collects data about
its own from the application layer to routing layer. Our
process provides solution in three techniques.
1. It always verifies own system and its habitat
dynamically. And it uses classifier to find out the local
anomalies.
2. Whenever the Peer wants to share the information from
the Peer F to B. It broadcasts the message to E and A.
Before it sends the message and it collects the information
of the Friendly Peers (E &B) using mobile agent. Then it
calls that the classification rules to find out the malicious
attacks with help of training data.
3. It gives similar type of solution in entire the global
networks. It has been presented completely in the following
section. .
1) Self Peer - Self Agent is exists in the system and it
verifies its own system frequently. If any attacker sends
any data packet to collect information through this system
and it calls the classifier process to find out the malicious
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number6–Nov2014
attacks. If an attack has been made then it will filter the
attacker system such as the relation to that peer or node
from the global networks.
2) Friendly Peer - Any system in the network shares any
information to some other system and it broadcast through
neighbor system. Before it transfers the message it sends
mobile agent to the Friendly Peer and collects all
information and it return back to the system and then calls
classifier rule to find out the malicious attacks. If there is
no malicious activity then it will forward the message to
Friendly Peer.
3) Data Requirements - The requirements module is
included for each anonymous user detection sub-system to
collect the values of features for corresponding layer in any
system. General profile is created using the data collected
from the agents during the normal format. Attack data is
collected during the attack process.
4) Preprocess – The verified data is collected in a file and
it can be used for anonymous user detection. Preprocessing
is a method to process the information with the test training
data. In the complete layer anomaly detection systems are
above mentioned pre-processing method is used.
B. Cross feature evaluation for classifier sub model
construction
1) Each character vector as ‘f’ in the training data set,
compute classifier as C, for each feature fi using F={f1,
f2... fi -I, fi+.-f k} - Ci is learned from the training data set
using Bayes classification algorithm. The probability P. (fl,
f2 ..., fi- i, f+1, ...,fk) is learned.
2) Calculate the average probability for every feature
vector ‘f’, and it save in a probability distribution matrix as
‘M’. A decision threshold is ‘0’ is learned from the training
data set and general profile is created using the threshold
value. If the probability value is greater than threshold
value it is conclude as normal, otherwise it is conclude as
abnormal. Anomaly detection Algorithm:Input: Preprocessed training data, preprocessed testing data
Output: Probability of anomaly
1) Read pre-processed data set file
2) Call Bayes classifier program for training the classifier
for anonymous detection
3) Read the testing data file
4) Test the classifier process with the testing data file
5) Print the classification matrix to view the actual class vs
predicted class
6) Percentage of anomaly is computed as follows
Percentage = (Number of predicted abnormal class X
100)/Total number of traces
Naïve Bayesian Classification Algorithm
Algorithm to classify malicious agent
Sample space: set of agent
H= Hypothesis that X is a node
P (H/Xi) is our confidence that Xi is an incoming node
P(H) is Prior Probability of H and it is probability that any
given training sample is an agent regardless of its anomaly
or not anomaly behavior
P(H/X) is a conditional probability and P(H) is independent
of X
Estimating probabilities
P(H), P(Xi) and P(Xi/H) may be estimated from given
training and testing data samples
P(H|Xi)=P(Xi|H)*P(H)/P(Xi)
Steps Involved:
Each training data sample is of attribute type
X= (xj) j =1(1….n), where xj is the values of X for
attribute Aj
2.
Suppose there are m decision classes Cj,
j=1(1…m).
P(Ci|X) > P(Cj|X) for 1<= j <= m, j>i
i.e. classifier assigns X to decision class Cj having highest
posterior probability conditioned on testing sample X
The decision class for which P(Cj|X) is maximum is known
as maximum posterior hypothesis of the sample.
From Bayes Theorem
3.
P(Xi) is constant and Only need be maximized.
if class initial probabilities not known prior then
we can assume all decision classes to be more equally
likely decision classes
Otherwise maximize the samples
P(Ci) = Si/S
4.
Naïve assumption for attribute independence
P(X|Cj) = P(x1,…..,xm|C) = PP(xk|C)
5.
To classify an unknown testing sample Xi,
compute each decision class Ci and Sample X is assigned
to the class
iff ( Prob(Xi|Ci)P(Ci)> P(Xi|Cj) P(Cj) ).
IV.EXPERIMENTAL ANALYSIS
Source
name
ip
/node
Destination
/node name
ip
Port no
Type
protocol
of
Number of packets
(in bytes)
Status
192.168.1.10
192.168.1.20
8081
TCP
56
Malicious
192.168.1.12
192.168.1.21
8082
TCP/IP
120
Not Malicious
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number6–Nov2014
192.168.1.11
192.168.1.20
8081
smtp
35
Malicious
192.168.1.19
192.168.1.28
8083
http
56
Malicious
192.168.1.16
192.168.1.25
8084
TCP
56
Not Malicious
Our experimental results shows more efficient
results than the traditional approach in finding anonymity
problem and maintain authentication while communicate
with other nodes, for experimental implementation we
implemented through network programming in java.
Initially source node connects to destination node through
its ip address and port number and retrieves its meta data
and forwards these sample to training dataset for analyzing
the behavior by computing posterior probability
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Chee, “HIGHT: A New Block Cipher Suitable for LowResource Device,” Proc. Eighth Int’l Workshop
Cryptographic Hardware and Embedded Systems (CHES
’06), pp. 46-59, 2006.
[9]Pogue, David (January 2011). "Don't Worry about
Who's watching". Scientific American 304 (1): 32.
doi:10.1038/scientificamerican0111-32.
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BIOGRAPHIES
Dr. C.P.V.N.J Mohan Raois Professor in
the Department of Computer Science and
Engineering,
Avanthi Institute of Engineering &
Technology - Narsipatnam. He did his PhD from Andhra
University and his research interests include Image
Processing, Networks, Information security, Data Mining
and Software Engineering. He has guided more than 50
M.Tech Projects and currently guiding four research
scholars for Ph.D. He received many honors and he has
been the member for many expert committees, member of
many professional bodies and Resource person for various
organizations.
A.v.swathi completed B.techc.s.e from
JNTU(2002-2006) from Al - Ameer
college
for
engineering
&
IT
Worked
for
Option
I Technologies,Hyderabad as SAP Trainer.
(2006-2008). Worked
as
Assistant
professor for Gayatri college of engineering(C.S.E)
Visakhapatnam(2008-2010)M.techc.s.e JNTUK (2012 2014) from Avanthi college of engineering –Narsipatnam.
[7] A. Perrig, R. Szewczyk, J. Tygar, V. Wen, and D.
Culler, “SPINS:
Security Protocols for Sensor Networks,” Wireless
Networks, vol. 8, no. 5, pp. 521-534, 2002.
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