Social Networking, 2013, 2, 42-45
http://dx.doi.org/10.4236/sn.2013.21005 Published Online January 2013 (http://www.scirp.org/journal/sn)
A Heuristic Reputation Based System to Detect Spam
Activities in a Social Networking Platform, HRSSSNP
Manoj Rameshchandra Thakur1*, Sugata Sanyal2
1Computer Science Department, Veermata Jijabai Technological Institute, Mumbai, India
2Corporate Technology Office, Tata Consultancy Services, Mumbai, India
Email: *email@example.com, firstname.lastname@example.org
Received November 12, 2012; revised December 27, 2012; accepted January 14, 2013
The introduction of the social networking platform has drastically affected the way individuals interact. Even though
most of the effects have been positive, there exist some serious threats associated with the interactions on a social net-
working website. A considerable proportio n of the crimes that occur are initiated through a social networking p latform
. Almost 33% of the crimes on the internet are initiated through a social networking website . Moreover activities
like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a re-
sult preventin g interactions with malicious intent and spam activities becomes cruc ial. This work attempts to detect the
same in a social networking platform by considering a social network as a weighted graph wherein each node, which
represents an individual in the social network , stores activities of other nodes with respect to itself in an optimized for-
mat which is referred to as localized data-set. The weights associated with the edges in the graph represent the trust re-
lationship between profiles. The weights of the edges along with the localized data-set are used to infer whether nodes
in the social network are compromised and are performing spam or malicious activities.
Keywords: Spam; Social Graph; Collaborative Filtering; Weighted Graph; Localized Data-Set; Trust Level
A considerable amount of work has been done in the area
of spam detection and trust based recommendation sys-
tems for social networking platforms. A brief overview
of these is as follows:  suggests a dynamic personal-
ized recommendation system that is based on the trust
between agents. It us es th e con cep t of feedb ack centrality
and overcomes some of the limitations of earlier recom-
mendation systems that use other trust metrics. In the
model suggested in  an agent tries to filter interactions
based on the information that it gains from its own social
network. The model suggested in  identifies the im-
pact of factors like preference heterogeneity of agents,
network density among agents, and knowledge sparse-
ness which are crucial factors for the performance of the
model. The technique suggested in paper is however dif-
ferent from the earlier two works, in that it makes use of
a weighted social graph  to view the relationship be-
tween profiles in a social networking platform . The
technique suggested in [5,6] suggests a reputation based
intrusion detection system to detect malicious and com-
promised nodes in a mobile ad-hoc network. Even
though this work is not directly related to social n etwork-
ing platforms, the approach sugg ested in [5,6] is relevant
to the problem of filtering malicio us and spam conversa-
tions among agents in a social networking platform.
A number of works have been suggested in the recent
past that address the issue of email spam activities and
suggest techniques to combat them. Email spam, even
thought not d irectly related to spam activities, is relev ant
to our work primarily because spam emails are analogous
to spam and malicious activities in a social network. 
presents a new email ranking and classification scheme
that makes use the social email interactions to infer the
spam/non-spam status of the sender of any given email.
The rank assigned to an email address based on its inter-
actions represents the reputation of the email address.
The work suggests two level of ranking: global rank
which is recipient email address agnostic and personal-
ized rank that varies based on the email address which is
receiving the email from a particular sender.  presents
a email scoring mechanism which is again based on so-
cial interactions and assigns reputation ratings to email
addresses.  attempts to discriminate spam activities
from non-spam activities by applying various machine
learning techniques. The classification is based on six
distinct features that the work identifies.  attempts to
assign the legitimacy to the sender of an email based on
the features extracted from various email interaction.
opyright © 2013 SciRes. SN
M. R. THAKUR, S. SANYAL 43
This work attempts to adopt a learning approach for de-
tecting spam activities.
Intrusion detection, which involves trying to identify
malicious and compromised nodes in a given network, is
similar to the process of identifying compromised agents
is a given social graph representing the way in which
individuals are connected.  discusses some of the se-
curity issues associated with distributed computing infra-
structures most of which apply to a social network as
well. Approaches like the ones suggested in [12-16] are
instrumental in not only addressin g the problem of intru-
sion by malicious nodes in a network but are also indi-
rectly helpful in devising similar approaches for spam
and malicious agent detection in a social network.
2. The Social Graph
A social graph may be defined as a graph that represents
the way individual are related to each other on the inter-
net . Even though it represents the relationship be-
tween individuals it doesn’t manifest in any way th e trust
level among individuals. Two individuals might be re-
lated but might no t have a high trust level. The ab ility to
represent the trust level in a social graph can impart a
powerful tool to detect and prevent unwanted interaction.
For example, if A is not related to B but wants to interact
with B. B will try to obtain relevant information from an
individual C with whom B has a high trust level and
based on the inputs B will decide whether to allow A to
interact or not. The suggested approach attempts to de-
rive this trust level among profiles based on previous
interactions and the relationship type and represent the
trust level as weights corresponding to the edges that
represent the relationship between profiles.
3. Collaborative Filtering and
Unwanted/Malicious Activity Detection
In a given social networking platform, the following
holds, “If Profile A is victimized by a malicious interac-
tion by C then the chances of profile B being victimized
by profile C is high”. It is this relationship that has in-
spired the use of collaborative filtering  for the sug-
gested approach. The suggested approach adds an addi-
tional constraint that in order for profile B to detect
whether profile C is trying to initiate a mali cious interac-
tion it will only try to take recommend ation from prof iles
which it trusts i.e. profiles with high trus t level.
4. Weighted Social Graph
The suggested approach views the social networking
plat-form as a weighted social graph . Each node re-
presents a profile (an individual), an edge represents a
relationship between profiles and the weight correspond-
ing to the edge represents the trust level among profiles.
Each profile has a localized dataset associated with it th at
holds a table with the following format:
Profile Id (say X) < incoming activity with X > : <
outgoing activity with X >
We refer to the localized dataset as LD, such that LD
(X) represents the localized dataset entry of profile Y for
profile X, such that the incoming activity with X is rep-
resented as I(X) and the outgoing activity with X as O(X).
, LD XIXO X
The overall view of the weighted social graph can be
depicted as shown in Figure 1.
5. Localized Data-Set and EDGE Weight
Corresponding to each profile, the localized data-set re-
fers to a table with the following entries:
The first column represents profile Ids to which the
given profile (Y) is connected i.e. has a relationship
The second column has entries in the following for-
, LD XIXO X
Incoming activity, I(X): it represents the activities
which were initiated by X in which the considered profile
was the destination. These interactions include activities
like message sent from X to the considered profile, friend
request from X to the considered profile, comments on a
photo of the considered profile etc. It must be noted that
the type of interaction as mentioned earlier may vary
based on the social networking website considered.
Outgoing activity, O(X): it represents the activities
which were initiated by the considered profile wherein
profile X was the destination. These interactions include
activities like message sent to X, friend request sent to X,
Figure 1. Weighted social graph.
Copyright © 2013 SciRes. SN
M. R. THAKUR, S. SANYAL
comments on a photo of profile X etc. It must be noted
that the type of interaction as mentioned earlier may vary
based on the social networking website considered.
In real life interactions, the trust among individuals in-
creases over time as the interactions among the individu-
als increase. These interactions are however bi-direc-
tional i.e. they include interactions which are initiated by
both the involved individuals. The suggested approach
employs this concept to calculate the trust levels among
profiles. The trust level of profile X for a connected pro-
file Y is represented by T (X, Y) and T (X, Y) vice versa.
In order to calculate O(Y) and I(Y), the entries in the
localized data-set of X are considered corresponding to
profile Y. A similar procedure is used to calculate O(X)
and I(X). A value for [I(X)/O(X)] that is close to 1 repre-
sents a high trust level of profile X with respect to profile
Y. The value of I(X) increasing at a rate higher than that
of O(X) represents a spam activity initiated by profile X
and vice versa. It must be noted that the suggested tech-
nique will allow first few spam messages, if any, after
which as the value T(X, Y) increases the possibility of a
spam increases and after the upper threshold the interac-
tion will be blocked and marked as spam interactions.
Consider the scenario where say a fake profile A is cre-
ated which sends ou t a friend request to a legitimate pro-
file B. Now in such a scenario for B, O(A) and I(A) are
both 1, since A initiated an outgoing interaction and B
replied to it. However after the first few spam messages
the value [I(A)/O(A)] will increase thus preventing A
form initiating any further spam messages.
In order to illustrate the variation in T(Y, X) and hence
the reputation of profile X with respect to profile Y we
present a graph showing the cumulative number of in-
comeing and outgoing messages by profile Y with respect
to profile X. Figure 2 represents a non-spam normal in-
teraction and Figure 3 represents a spam interaction.
It must be noted that the time unit used for storing the
number of interactions starts with the granularity level of
seconds and as and when the time proceeds the granular-
ity level of the data to be stored increases. For example
one configuration that can be used is to store the counts
on a per second basis for one minute after which store the
counts on a per minute basis for one hour and after which
store the counts on a per hour basis. The flat sections in
the line graphs above represent no interaction. In Figure
2 throughout the time span represented in the graph the
count of I(X) and O(X) is almost the same. As a result the
value of T(X, Y) is close to 1 throughout the time span,
which represents a valid non-spam interaction. In Figure
Figure 2. Graph representing non-spam interaction.
Figure 3. Graph representing spam interaction.
3 however it can be seen that after point P the difference
between I(X) and O(X) increases at a higher rate. Thus
after point P the chances of the interaction between X and
Y being a spam increases. It must be noted that the
graphs presented in Figures 2 and 3 are not based on any
actual data. The graphs have been used to merely illu-
strate the way in which the suggested technique operates.
6. Collaborative Filtering of Interactions
For a given profile if an incoming interaction is initiated
from a profile then the profile first checks if the profile is
connected. If the source profile is connected then the
interaction is accepted only if the trust level between the
two profiles is higher than a predefined threshold and the
localized data-set of each of the profiles is updated. If
however the source profile is not connected then the des-
tination profile tries to find the trust level of the source
profile with a third profile with which the destination
profile has a high trust level. For example if A tries to
interact with B then B will accept the interaction only if
the trust level between A and B is greater than a particu-
lar threshold. However if A is not connected to B then B
tries to derive or infer the trust level from a third profile
C such that the trust level between B and C is higher than
the threshold and C is connected to A.
Copyright © 2013 SciRes. SN
M. R. THAKUR, S. SANYAL
Copyright © 2013 SciRes. SN
The suggested technique thus addresses the issue of ma-
licious and spam interactions among profiles in a social
networking platform in an effective way by correlating
the scenario with the interactions in the society. The use
of the weighted social graph imparts the suggested tech-
nique the ability to not only view and understand the way
individuals are connected in a social networking platform
but also reflects the trust level among individuals which
helps to filter out malicious and unwanted spam interac-
tions. It must be noted that the suggested technique will
be unable to prevent spam and malicious interaction if
already existing legitimate profiles with high trust level
are compromised. The solution to this problem is outside
the scope of this work however a potential solution to
this problem is the N/R one time password system sug-
gested in . The problems of passwords of legitimate
profiles being disclosed by means of attacks like pass-
word guessing attacks can be addresses by the approach
suggested in .
 Social Networking Statistics, URL (last checked 14 Dec
 E. F. Walter, S. Battiston and F. Schweitzer, “Persona-
lized and Dynamic Trust in Social Networks,” Proceed-
ings of The Third ACM Conference on Recommender
Systems, Association for Computing Machinery, New
York, pp. 197-204.
 E. F. Walter, S. Battiston and F. Schweitzer, “A Model of
a Trust-Based Recommendation System on a Social
Network,” Autonomous Agents and Multi-Agent Systems,
Vol. 16, No. 1, 2008, pp. 57-74.
 Uniform Resource Locator, “Weighted Graphs.” (last
checked 02 Dec 2012)
 A. K. Trivedi, R. Arora, R. Kapoor, Sudip Sanyal and Su-
gata Sanyal, “A Semi-Distributed Reputation-Based In-
trusion Detection System for Mobile Ad hoc Networks,”
Journal of Information Assurance and Security, Vol. 1,
No. 4, 2006, pp. 265-274.
 A. K. Trivedi, R. Kapoor, R. Arora, Sudip Sanyal and
Sugata Sanyal, “RISM—Reputation Based Intrusion De-
tection System for Mobile Ad hoc Networks,” Third In-
ternational Conference on Computers and Devices for
Communications, Institute of Radio Physics and Elec-
tronics, University of Calcutta, Kolkata, 18-20 December
2006, pp. 234-237.
 P. A. Chirita, J. Diederich and W. Nejdl, “MailRank:
Using Ranking for Spam Detection,” Proceedings of the
14th ACM international conference on Information and
Knowledge Management, Bremen, 31 October-5 Novem-
 J. Golbeck and J. Hendler, “Reputation Network Analysis
for Email Filtering,” Proceedings of the 1st Conference
on Email and Anti-Spam, Mountain View, 2004.
 B. Markines, C. Cattuto and F. Menczer, “Social Spam
Detection,” Proceedings of the 5th International Work-
shop on Adversarial Information Retrieval on the Web,
Madrid, 21-21 April 2009. doi:10.1145/1531914.1531924
 H. Y. Lam and D. Y. Yeung, “A Learning Approach to
Spam Detection based on Social Networks,” Proceedings
of the Fourth Conference on Email and Anti-Spam,
Mountain View, 2007.
 R. Bhadauria and S. Sanyal, “Survey on Security Issues
in Cloud Computing and Associated Mitigation Tech-
niques,” International Journal of Computer Applications,
Vol. 47, No. 18, Foundation of Computer Science, New
York, 2012, pp. 47-66. doi:10.5120/7292-0578
 R. A. Vasudevan, A. Abraham, S. Sa nyal and D. P. Agra -
wal, “Jigsaw-Based Secure Data Transfer over Computer
Networks,” IEEE International Conference on Informa-
tion Technology: Coding and Computing, Las Vegas, Vol.
1, 2004, pp. 2-6.
 A. Abraham, R. Jain, S. Sanyal and S. Y. Han, “SCIDS:
A Soft Computing Intrusion Detection System,” In: A.
Sen, et al., Eds., 6th International Workshop on Distri-
buted Computing, Lecture Notes in Computer Science,
Vol. 3326, Springer Verlag, Berlin, pp. 252-257.
 S. Sanyal, D. Gada, R. Gogri, P. Rathod, Z. Dedhia and N.
Mody, “Security Scheme for Distributed DoS in Mobile
Ad Hoc Networks,” Technical Report, School of Tech-
nology & Computer Science, Tata Institute Of Funda-
mental Research 2004.
 S. Pal, S. Khatua, N. Chaki and S. Sanya l, “A New Trust-
ed and Collaborative Agent Based Approach for Ensuring
Cloud Security,” Annals of Faculty Engineering Hune-
doara International Journal of Engineering, Vol. 10, No.
1, 2012, pp. 71-78.
 P. Rathod, N. Mody, D. G., Rajat G., Z. Dedhia, S. San-
yal and A. Abraham, “Security Scheme for Malicious
Node Detection in Mobile Ad Hoc Networks,” In: A. Sen
et al., Eds., 6th International Workshop on Distributed
Computing, Lecture Notes in Computer Science, Vol.
3326, Springer Verlag, Berlin, 2004, pp. 541-542.
 Uniform Resource Locator, “Social Graph.” (last checked
on 11 Dec 2012)
 Uniform Resource Locator, “Recommender System.”
(last checked 10 Dec 2012)
 V. Goyal, V. Kumar, M. Singh, A. Abraham and S.
Sanyal, “CompChall: Addressing Password Guess-
ing Attacks,” IEEE International Conference on In-
formation Technology: Coding and Computing, Vol.
1, 4-6 April 2005, pp 73 9- 7 44 .