Adaptive Spam Email Detection using Support Vector Machines (SVMs)

Authors :- Javad Hosseinkhani, Mohsen Nematollahi, Mohammad Akhlaghpour, Horiyeh Heydari Sadegh, Zeynab Sayad Arbabi, Ehsan Ahrari

Keywords :- Spam Detection, Email Classification, Support Vector Machine (SVN)
Published Online :- 01 December 2016

  • View Full Abstract

    Today, many spam attempts to make difficulty with email connections. In this article we try to expose a way regarding spam identification based on Support Vector Machines (SVMs). Based on this method on delivery email three steps should be occur first of all a reoperation then flowing data. In operation step the user is sending an email preprocess is done by data miner system. The number of training information apply with window based solution will be selected with default, W=100, the first 100 data would be used as training category. Each delivery email input to SVM to be sorted in to 2 predetermined categories named: Non spam, and Spam. An algorithm is written that 4 different types of time window in order to SVM training is selected (100,200,500 and all the preset data or open window). The criteria for assessing include accuracy rate, recall, and precision rate. The results show that the techniques that some specialists have some criticisms to it.
  • View References

    [1]  Matsumoto, R., Zhang, D., & Lu, M. (2004, November). Some empirical results on two spam detection methods. In Information Reuse and Integration, 2004. IRI 2004. Proceedings of the 2004 IEEE International Conference on (pp. 198-203). IEEE.

    [2]  Joachims, T. (2002). Learning to classify text using support vector machines: Methods, theory and algorithms (p. 205). Kluwer Academic Publishers.

    [3]  Osuna, E., Freund, R., & Girosi, F. (1997, September). An improved training algorithm for support vector machines. In Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop (pp. 276-285). IEEE.

    [4]  Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines.

    [5]  Vapnik, V., & Kotz, S. (2006). Estimation of dependences based on empirical data. Springer.

    [6]  Moradi Koupaie, Hossein, Suhaimi Ibrahim, and Javad Hosseinkhani. “Outlier Detection in Stream Data by Machine Learning and Feature Selection Methods.” International Journal of Advanced Computer Science and Information Technology (IJACSIT) Vol 2 (2014): 17-24.

    [7]  Mozafari, B., Thakkar, H., & Zaniolo, C. (2008, April). Verifying and mining frequent patterns from large windows over data streams. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on (pp. 179-188). IEEE.

    [8]  Chu, F., & Zaniolo, C. (2004). Fast and light boosting for adaptive mining of data streams. In Advances in Knowledge Discovery and Data Mining (pp. 282-292). Springer Berlin Heidelberg.

    [9]  Wang, H., Fan, W., Yu, P. S., & Han, J. (2003, August). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 226-235). ACM.

    [10]Forman, G. (2006, August). Tackling concept drift by temporal inductive transfer. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 252-259). ACM.

    [11]Law, Y. N., Wang, H., & Zaniolo, C. (2004, August). Query languages and data models for database sequences and data streams. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30 (pp. 492-503). VLDB Endowment

    [12]Arasu, A., Babu, S., & Widom, J. (2004, January). CQL: A language for continuous queries over streams and relations. In Database Programming Languages (pp. 1-19). Springer Berlin Heidelberg.

    [13]Abadi, D. J., Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., … & Zdonik, S. (2003). Aurora: a new model and architecture for data stream management. The VLDB Journal—The International Journal on Very Large Data Bases, 12(2), 120-139.

    [14]Thakkar, H., Mozafari, B., & Zaniolo, C. (2008, March). Designing an inductive data stream management system: the stream mill experience. In Proceedings of the 2nd international workshop on Scalable stream processing system (pp. 79-88). ACM.

    [15]Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.

    [16]Tang, Z., Maclennan, J., & Kim, P. P. (2005). Building data mining solutions with ole db for dm and xml for analysis. ACM SIGMOD Record, 34(2), 80-85.

    [17]Imielinski, T., & Mannila, H. (1996). A database perspective on knowledge discovery . Communications of the ACM, 39(11), 58-64.

    [18]Thakkar, H., Mozafari, B., & Zaniolo, C. (2008, December). A data stream mining system. In Data Mining Workshops, 2008. ICDMW’08. IEEE International Conference on (pp. 987-990). IEEE.

    [19]Tseng, C. Y., & Chen, M. S. (2009, August). Incremental SVM model for spam detection on dynamic email social networks. In Computational Science and Engineering, 2009. CSE’09. International Conference on (Vol. 4, pp. 128-135). IEEE.

    [20]Hovold, J. (2005, July). Naive Bayes Spam Filtering Using Word-Position-Based Attributes. In CEAS.

    [21]Metsis, V., Androutsopoulos, I., & Paliouras, G. (2006, July). Spam filtering with naive bayes- which naive bayes?. In CEAS (pp. 27-28).

    [22]Blanzieri, E., & Bryl, A. (2007). Evaluation of the highest probability SVM nearest neighbor classifier with variable relative error cost.

    [23]Li, S., Kwok, J. T., Zhu, H., & Wang, Y. (2003). Texture classification using the support vector machines. Pattern recognition, 36(12), 2883-2893

    [24]Schneider, K. (2004). Brightmail url filtering. In Spam Conference

    [25]Dredze, M., Gevaryahu, R., & Elias-Bachrach, A. (2007, August). Learning Fast Classifiers for Image Spam. In CEAS.

    [26]Clayton, R. (2007, August). Email traffic: a quantitative snapshot. In CEAS.

    [27]Oscar, P., & Roychowdbury, V. P. (2005). Leveraging social networks to fight spam. IEEE Computer, 38(4), 61-68.

    [28]Chirita, P. A., Diederich, J., & Nejdl, W. (2005, October). MailRank: using ranking for spam detection. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 373-380). ACM.

    [29]Taylor, B. (2006, July). Sender Reputation in a Large Webmail Service. InCEAS.

    [30]Hershkop, S., & Stolfo, S. J. (2005, August). Combining email models for false positive reduction. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 98-107). ACM.

    [31]Cormack, G. V., Smucker, M. D., & Clarke, C. L. (2011). Efficient and effective spam filtering and re-ranking for large web datasets. Information retrieval, 14(5), 441-465.

    [32] Segal, R. (2007, August). Combining Global and Personal Anti-Spam Filtering. In CEAS.

    [33]Castillo, C., & Davison, B. D. (2011). Adversarial web search. Foundations and trends in Information Retrieval, 4(5), 377-486.

    [34]Moradi, F., Olovsson, T., & Tsigas, P. (2012, April). Towards modeling legitimate and unsolicited email traffic using social network properties. InProceedings of the Fifth Workshop on Social Network Systems (p. 9). ACM.

    [35]Golbeck, J., & Hendler, J. A. (2004, July). Reputation Network Analysis for Email Filtering. In CEAS.

Off-Body Performance in a WBAN over an Underground Mine

Authors :- Moulay El Azhari, Mourad Nedil, Yassine Salih-Alj, Larbi Talbi, Ismail Ben Mabrouk, Khalida Ghanem

Keywords :- Channel Capacity, Off-body Channel, SISO, WBAN
Published Online :- 01 December 2016

  • View Full Abstract

    The impact of a peculiar confined environment (i.e., underground mine) on the characteristics of the WBAN (Wireless Body Area Network) channel is evaluated in this article. Channel characteristics of WBAN are different from those of other wireless channels, especially in the presence of shadowing obstacles at Non-Line-of-Sight (NLOS). Moreover, directive antennas could be used for off-body communications as a mean to minimize the interaction with human body. Hence, in this study, the off-body performance of a singleinput single-output (SISO) system was evaluated in Non-Line-of-Sight (NLOS) situations and compared with Line of Sight (LOS) measurements, at the 2.45 GHz band, for two antenna setups. Experimental results of the Rician k-factor, RMS delay spread, and path loss are obtained and discussed from measurements conducted at an underground mine gallery with Patch and Omnidirectional antennas. The results show that the average value of the Rician k-factor generally increases with distance, due to an increased multipath energy losses compared to the LOS energy loss. Moreover, the path loss exponent decreases at NLOS while the path loss values are increased due to human shadowing obstacle. The channel capacity is decreased at NLOS and as we increase the distance, due to the path loss effect. Directivity did not have a significant impact on the channel parameters.
  • View References

    REFERENCES[1] S.-H. Han and S. K. Park, “Performance Analysis of Wireless Body Area Network in Indoor Off-body
    Communication,” in IEEE Transactions on Consumer Electronics, Vol. 57, No. 2, pp. 335-338, May
    [2] S.I.M. Sheikh, W. Abu-Al-Saud, and A. B. Numan, “Directive Stacked Patch Antenna for UWB
    Applications,” International Journal of Antennas and Propagation, Vol. 2013, No. 389571, pp. 1-6, Nov.
    [3] M.E. El-Azhari, M. Nedil, Y. S, Alj, I. Benmabrouk, L. Talbi, and K. Ghanem, “Off-body LOS and NLOS
    channel characterization in a mine environment,” in Electrical and Information Technologies (ICEIT),
    2015 International Conference on , vol., no., pp.114-118, 25-27 March 2015.
    [4] M.E. El-Azhari, M. Nedil, I. Benmabrouk, and L. Talbi, “Off-body channel characterization at 2.45 GHz
    in underground mine environment,” Proc. IEEE Antennas and Propagation Society Int. Symp.
    (APSURSI), pp.251-252, Jul. 6–11, 2014.
    [5] Opening Report for the TG6 session in Aug 2008, IEEE 802.15 P08-0576-00-0006.
    [6] I. Khan, P. S. Hall, A. A. Serra, A. R. Guraliuc, and P. Nepa, “Diversity Performance Analysis for On-
    Body Communication Channels at 2.45 GHz,” IEEE Trans. Antennas Propag., vol. 57, no. 4, pp. 956-
    963, Apr. 2009.
    [7] I. Ben Mabrouk, L. Talbi, M. Nedil and K. Hettak, “MIMO-UWB Channel Characterization Within an
    Underground Mine Gallery,” IEEE Transactions on Antennas and Propagation, Vol. 60, No. 10, pp.
    4866-4874, Oct. 2012.
    [8] Y. Salih-Alj, C. Despins and S. Affes, “Design considerations for an UWB computationally-efficient fast
    acquisition system for indoor line-of-sight ranging applications,” IEEE Transactions on Wireless
    Communications, Vol. 10, No. 8, pp. 2776-2784, Aug. 2011.
    [9] P. Van Torre, L. Vallozzi, H.Rogier, M. Moeneclaey and J. Verhaevert, “Reliable MIMO communication
    between firefighters equipped with wearable antennas and a base station using space-time codes,”
    Proc. 5th European Conference on Antennas and Propagation (EUCAP), pp. 2690-2694, Apr. 2011.
    [10] I. Khan, P.S. Hall, A.A. Serra, A.R. Guraliuc and P. Nepa, “Diversity Performance Analysis for On-
    Body Communication Channels at 2.45 GHz,”IEEE Trans. Antennas Propagation, Vol. 57, No. 4, pp.
    956-963, Apr. 2009.
    [11] C. Tepedelenlioglu, A. Abdi, and G. Giannakis, “The ricean k factor: estimation and performance
    analysis,” Wireless Communications, IEEE Transactions on, vol. 2, no. 4, pp. 799–810, 2003
    [12] Sarris, I. and A. R. Nix, “Ricean K-factor measurements in a home and an office environment in the 60
    GHz band,” Mobile and Wireless Communications Summit, 2007.
    [13] J. Hamie, “Contributions to Cooperative Localization Techniques within Mobile Wireless Body Area
    Networks,” PhD Manuscript, University of Nice-Sophia Antipolis, Nov. 2013
    [14] R. Rosni and R. D’Errico, “ Off-body channel modelling at 2.45 ghz for two different antennas”, In
    Antennas and Propagation (EUCAP), Proceedings of the 6th European Conference on, pp 3378-3382,
    [15] B. Denis, N. Amiot, B. Uguen, A. Guizar, C. Goursaud, A. Ouni, and C. Chaudet, “Qualitative analysis
    of RSSI behavior in cooperative wireless body area networks for mobility detection and navigation
    applications,” in 21st IEEE International Conference on Electronics Circuits and Systems (ICECS) on ,
    pp.834-837, 7-10 Dec. 2014.
    [16] T. S. Rappaport, “Mobile Radiop Propagation: Small scale Fading and Multipath,” in Wireless
    communications: principle and practice, 2nd ed, Prentice Hall, 2001.
    [17] H. Inanoglu, “Multiple-Input Multiple-Output System Capacity: Antenna and Propagation
    Aspects,” IEEE Antennas and Propagation Magazine, Vol. 55, No. 1, pp. 253-273, Feb. 2013.

Proposing a Mechanism to Improve Web Usage Mining Automatically using Semantic Repository of the Data

Authors :- Azizollah Ghazi and Javad Hosseinkhani
Keywords :- Web Mining Application, User Profile, Source of Lexical Meaning, Automated Modeling
Published Online :- 22 September 2016

  • View Full Abstract

    Users encounter with some troubles finding the information they need to access easily at the right time because on the one hand they need to examine the relevance of each page with their needs and on the other hand must assess reliability of pages. In recent decades retrieval systems and search engines have been created to fix this problem which index the content of web pages and pages relevant to the user query will be returned. Burdensome of information in current web is a major problem. Personalization systems were provided to deal with this problem which compatible the content and services of a web site based on interests and behavior of people. An essential element in any web personalization system is its user model. The content of web page can be used to create more precise models of the user , but approaches based on key words does not have a deep understanding of website . Nevertheless, manually creating a hierarchy of concepts is time-consuming and costly. On the other hand the public literal meaning resources are suffering from low coverage of specific phrases for domains. In this article we’re going to resolve both of these defects. Our main achievement is providing a mechanism to improve the user views automatically in Web site using a comprehensive lexical meaning source. We are using today’s largest encyclopedia Wikipedia as a rich source for meanings to improve automated modeling manufacture of user’s interests. The proposed architecture includes a number of components that include: pre-primary processing, mining concepts website domain, extracting keywords from web site, creator of keywords vector and key words mapping to concepts. Another important achievement is using the structure of website to limit specific concepts of the domain.
  • View References

    [1] Birgani, Anoosh Mansouri, Javad Hosseinkhani, Moham, and Mohammad Akhlaghpour. “Proposing an

    Algorithm in order to Detect Communities in Social Networks using Multi-Objective Evolutionary Algorithm.” (2016).

    [2] S. S. Anand and B. Mobasher, “Intelligent Techniques for Web Personalization”, LNAI 3169, Springer-Verlag ,2015, 1–37.

    [3] B. Mobasher, R. Cooley and J. Srivastava, “Automatic Personalization based on Web Usage Mining”, Communications of the ACM, 2000, vol. 43, 142-151.

    [4] P. Achananuparp, H. Han, O. Nasraoui and R. Johnson, “Semantically Enhanced User Modeling”, Proceedings of the 2016 ACM Symposium on Applied Computing (Seoul, Korea, March 11 – 15, 2016).

    [5] R. Cooley, B. Mobasher and J. Srivastava, “Grouping Web Page references into transactions for mining World Wide Web browsing patterns”, Technical Report TR 97-021, Department of Computer Science, University of Minnesota, 1997.

    [6] P. N. Tan and V. Kumar, “Discovery of Web Robot Sessions Based on their Navigational Patterns”, Data Mining and Knowledge Discovery, 6:1, 2012, 9-35.

    [7] D. Pierrakos, G. Paliouras, C. Papatheodorou and C. D. Spyropoulos, “Web Usage Mining as a Tool for Personalization: A Survey”, User Modeling and User-Adapted Interaction, 13: 311-372, 2013.

Increased diagnostic accuracy in social networking communities using clustering method

Authors :- Farzaneh Javid and Javad Hosseinkhani

Keywords :- Web Mining , Social Networks, Clustering, Link Analysis and Accuracy
Published Online :- 01 September 2016

  • View Full Abstract

    Clustering is one of the best ways to work with data provided. Clustering is one of the learning branches unsupervised and is an automated process in which samples are divided into categories whose members are similar to each other. A hierarchical clustering is a kind of traditional algorithm which follows the structure of society in social networks and on the basis of similarity or strength of the connection between each node; the entire network is divided into several subgroups. The advantage of community detection methods is based on the hierarchical clustering in which methods data is not necessary to be available already about the optimum number of communities and algorithms will be able to determine the optimum number of communities. The proposed method is a method based on hierarchical clustering which is able to identify overlapping communities. The main purpose of the community detection is to put similar nodes in a cluster with each other. Therefore, the nodes in a cluster share the same characteristics and the relationship between nodes within each cluster is denser from the connection of nodes between different clusters. The proposed method has been used for clustering the concept of the centrality of interstitial ridge. As well as to identify nodes that belong to two different societies, the criterion of the centrality of a node is used. Also the performance of the method proposed was compared with two methods previously proposed in terms of two criterions, Modularity and NMI, to detect communities. Simulation results showed the proper functioning of the proposed method.

  • View References

    1. [1]  Aggarwal, C.C., 2011.”An introduction to social network data analytics.” Springer.
    2. [2]  Hosseinkhani, J., Chuprat, S., and Taherdoost, H., (2012a). Criminal Network Mining by Web Structure and Content Mining. 11th WSEAS International Conference on Information Security and Privacy (ISP ’12), Prague, Czech Republic September 24-26.
    3. [3]  Yoshida, K., 2014. “Memory Management for Big Data Mining – Cache Hit Rate Estimation of LessFU.” Procedia Technology, 17: p. 114-121.
    4. [4]  Freeman, L.C., 2014. “The development of social network analysis.: Empirical Press Vancouver.”
    5. [5]  Alhajj, R. and J. Rokne, Graph-Theory, 2014, “in Encyclopedia of Social Network Analysis and Mining”., Springer New York. p. 661-661.
    6. [6]  Newman, M., 2009, “Networks: an introduction. Oxford University Press.”
    7. [7]  Opsahl, T., F. Agneessens, and J. Skvoretz, 2010. “Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks,” 32(3): p. 245-251.
    8. [8]  Newman, M., 2008.” The mathematics of networks. The new palgrave encyclopedia of economics,” 2: p. 1-12.
    9. [9]  Al Falahi, K., N. Mavridis, and Y. Atif, 2012.” Social Networks and Recommender Systems: A World of Current and Future Synergies, in Computational Social Networks. “Springer. p. 445-465.
    10. [10]  Dangalchev, C., 2006. “Residual closeness in networks. Physica A: Statistical Mechanics and its Applications.” 365(2): p. 556-564.
    [11]Zhao, P. and C.-Q. Zhang, 2011. “A new clustering method and its application in social networks. Pattern Recognition Letters, “32(15): p. 2109-2118.

    [12] Jain, A.K., 2015. “Data clustering: 50 years beyond K-means. Pattern Recognition Letters,” 31(8): p. 651-666.

A New Cloud Infrastructure Approach For Higher Education Institutions
Authors :- Nouhad Amaneddine
Keywords :- Virtualization, Cloud, Cloud Computing, Distributed Computing, IaaS
Published Online :- 01 June 2016

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    Information Technology (IT) is playing a vital role in the modern educational system especially in higher education sectors. Higher education institutions are challenged to adopt latest IT resources due to the increasing competitive pressures from the competitors leading to offer new services required by these institutions. The implementation of physical networking infrastructure with advanced equipments in computer laboratories is difficult to achieve using the accessible resources in most of the higher education institutions. One of the distributed computing solutions “Service Oriented Architecture (SOA)” technologies creates possibilities for the purpose of establishing new learning methodologies and will reach to a more deep rooted education. The emergence of cloud and virtual computing has grown globally and solved the problem of the limited resources by reducing direct expenses of IT. In this paper, we evaluate the possibility of enhancing students’ performance, particularly, students working on their graduation projects by using cloud infrastructure implementation. In addition, this paper proposes a new approach to provide a solution for virtual labs using a Private Cloud. This will include various features and services of a Typical Cloud environment without any change to the existing basic services in the institution and will add flexibility to include any services in the network without having any extra effort of Network Management.
  • View References

    [1] Avery, P. (2009). “Research indicates increase in cloud computing”, IT Business Edge, August 26, available at:¼35256
    [2] A Complete History of Cloud Computing .(2012, January). Retrieved October 5, 2013, from SalesForce:
    [3] Bret, M. (2009). “In Clouds Shall We Trust?” IEEE Security & Privacy, Vol. 7, Issue 5.
    [4] Mell, P., Grance, T. (2011). The NIST definition of cloud computing. Gaithersburg, MD: National Institution of Standards and Technology (NIST).
    [5] Cisco Cloud Computing – Data Center Strategy, architecture, and Solutions, Cisco Systems, (2009).
    [6] Saju M., (2012). Implementation of Cloud Computing in Education – A Revolution, International Journal of Computer Theory and Engineering, Vol. 4, No. 3, June 2012
    [7] Katzan Jr., H. (2010). “On the Privacy of Cloud Computing,” International Journal of Management and Information Systems, Vol. 14, No. 2, pp. 247-255.
    [8] Kazmi, SI, Pandey,J., Hayat MS, Nagarale, A. (2014), “Using private Cloud to Elastically Extend Site Resources”, Proceeding of the International Conference on Applied Information and Communication Technology (ICACIT-14), pp. 743-748.
    [9] Girish L S , Dr. H S Guruprasad , (2014). Building Private Cloud using OpenStack, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, Issue 3, May – June 2014, ISSN 2278-6856.
    [10] Microsoft, (2013). Microsoft Private Cloud:Evaluation Guide. Microsoft.
    [11] Singh, B., 1 October 2011. Cloud Deployment Models – Private, Community,
    Public, Hybrid with Examples. Techno-Pulse. Available at:
    example.html [Accessed May 2016].
    [12] Sudip C., (2010). An Enterprise Private Cloud Architecture and
    Implementation roadmap, IT@Intel White Paper. Intel.
    [13] The Economics of the AWS Cloud vs. Owned IT Infrastructure, 2009. Amazon web services.
    [14] Anurag S., (2013). Cloud Computing and Its Vision 2015, International Journal of Computer and Communication Engineering, Vol. 2, No. 4.
    [15] Raj K., (2015). Research on Cloud Computing Security Threats using Data Transmission, International Journal of Advanced Research in Computer Science and Software Engineering Volume 5, Issue 1, January 2015 ISSN: 2277 128X
    [16] Philipp L., (2016). Patterns in the Chaos – A Study of Performance Variation and Predictability in Pubilc IaaS Clouds. ACM Transaction on Internet Technology, Vol. 16, Issue 3, June 2016.
    [17] Nelson, D. L., & Cox, M. M. (2005). Principles of biochemistry (4th ed.). New York: Freeman.
    [18] Ferres, K. (2001). Idiot box: Television, urban myths and ethical scenarios. In I. Craven (Ed.), Australian cinema in the 1990s (pp. 175-188). London, England: Frank Cass.
    [19] Senden, T. J., Moock, K. H., Gerald, J. F., Burch, W. M., Bowitt, R. J., Ling, C. D., et al. (1997). The physical and chemical nature of techniques. Journal of Nuclear Medicine, 38(10), 1327-33.
    [20] Shobhadevi, Y. J., & Bidarakoppa, G. S. (1994). Possession phenomena: As a coping behavior. In G. Davidson (Ed.), Applying psychology: Lessons from Asia-Oceania (pp. 83-95). Carlton, Vic., Australia: Australian Psychological Society.
    [21] Rose, S. L. (2006). Essays on almost common value auctions (Doctoral dissertation, Ohio State University). Retrieved from
    [22] Smith, B. An approach to graphs of linear forms (Unpublished work style), unpublished.
    [23] Wang, J. Fundamentals Methods to map organizations’ strategy, Journal of Management, submitted for publication.
    [24] Nicole, R. Technology acceptance review, Journal of. Information management, in press.
    [25] Saunders. (1997). Dorland’s illustrated medical dictionary. (28th ed.). Philadelphia.

Social Mobile Authentication in Pervasive Environment

Authors :- Mohamed Es Fih and Suhaimi Ibrahim
Keywords :- Authentication, mobile commerce, mobile banking, mobile payment
Published Online :- 01 June 2016

  • View Full Abstract

    There is a common understanding in the mobile phone industry that smartphones are now the majority type of phone in use in the market. This understanding has to be confronted to the reality on the ground especially in developing countries. Although smartphones make the majority of the current sales, there are facts that demonstrate that smartphones are not yet the predominant device. Studies have shown that Android phones are used as dumb-phones which in effect or practice makes the phones as good as featured phone. Other references give us statistics as to the sales of smartphones vs. featured phones in the last 3 years and taken into consideration that most phones are sold with a 1 to 2 years contract, and the fact that many people tend to keep the same phone for more than 2 years this study concludes that it would be wrong to build a mobile marketing strategy purely on smartphones. The important information from these facts is not a discussion about whether there are more smart phones than other types of phones but rather a more constructive discussion: How can we leverage the presence of smart phones users to enhance the authentication of non smart phone users? Beyond the devices, such type of authentication would require a social relationship between the 2 users in order to allow the authentication. This is called a “Social authentication method”. Different researchers have proposed various schemes of social authentication, some based on a token number, others on a Bluetooth connexions between the 2 users to friend’s face recognition for Facebook social authentication method. In this paper we will analyze the current mobile phone authentication context as well as the current social authentication proposals. We will then introduce a novel way of social authentication which we will detail.
  • View References

    [1] MuthucumaruMaheswaran Bijan Soleymani. Social authentication protocol for mobile phones. IEEE, 2009.
    [2] John Brainard. Fourth-factor authentication: Somebody you know. CCS 06:Proceedings of the 13th ACM. Conference on Computer and Communications Security, pages 168–178, 2006b.
    [3] Google’s dirty secret: Android phones are basicallyused as dumbphones. Technical report, Business Insider, 2014. URL
    [4] Kolodgy C. Biometrics: You are your own key. InfoWorld, 2001.
    [5] Pew Research Center. Social networking fact sheet, 2014.
    [6] Dong et al. Secure friend discovery in mobile social networks. Technical report, IEEEE, 2011.
    [7] Zhang et al. Predicting social ties in mobile phone networks. Technical report, IEEE, 2010.
    [8] Simon Kemp. Digital, social and mobile worldwide in 2015. January 2015. URL
    [9] Marco Rosay Lars Backstrom, Paolo Boldiy. Four degrees of separation.WebSci 12 Proceedings of the 4th Annual ACM Web Science Conference, pages Pages 33–42, 2012.
    [10] A.G. Miklas. Exploiting social interactions in mobile systems. In UbiComp2007: Ubiquitous Computing: 9th International Conference, 2007.
    [11] Mobithinking, 2014. URL
    [12] B. SathishBabuPallapaVenkataram. An authentication scheme for ubiquitous commerce: A cognitive agents based approach. IEEE, 2008.
    [13] Rosenwald. Smartphones get more sophisticated, but their owners not. WashingtonPost, 2014.
    [14] BrahimaSanou. Ict facts and figures. The International Telecommunication Union ITU, 2015.
    [15] Robert W. Reeder Stuart Schechter, Serge Egelman. It’s not what you know,but who you know. a social approach to last-resort authentication. CHI09: Proceeding of the twenty-seventh

A Dynamic Model for Evaluating Web Services Regarding Run-time Parameters
Authors :- Hadi Barani Baravati and Javad Hosseinkhani
Keywords :- Security, Evaluating Web Services, SOA, Run-Time Parameters
Published Online :- 01 March 2016

  • View Full Abstract

    Trust is one of the most important factors for making decision. By using trust, making decision and transactions go easy. Every day internet becomes big, and many web services are published. Therefore Service Oriented Architecture is proposed to improve publishing and discovering services process. In this architecture, clients prefer to trust to service providers and then select desire services. Reason of needing trust in this architecture is, in some situations maybe exists many services for specific functionality. So client should consider non-functional properties. Therefore in this project we propose architecture for assessing web services trust. Because trust is based on information, first trust parameters are considered in this thesis. Then we proposed architecture for assessing web service trust. Based on this architecture, parameters, services and service providers trust algorithms are proposed.
  • View References

    [1] P. Y. a. M. P. Singh“ ,Engineering Self-Organizing Referral Networks for Trustworthy Service Selection ”,IEEE Transactions on Systems, Man, and Cybernetics ,p. 396-407.2015.
    [2] L. K. a. G. Y. R. Song“ ,Trust in E-services: Technologies, Practices and Challenges ”,IGI Global.2007
    [3] K. K. R. Z. a. E. F. P. Resnick“ ,Reputation systems ”,Communications of the ACM ,12 ,pp. 45-48, 2000
    [4] Anderson, Steve, Jeff Bohren, Toufic Boubez, Marc Chanliau, Giovanni Della-Libera, Brendan Dixon, Praerit Garg et al. “Web services trust language (ws-trust).” (2004).
    [5] e. a. M. Ahsant“ ,Dynamic Trust Federation in Grids4 ”,th Int. Conf. on Trust Management
    [6] G. S. a. S. LoPresti“ ,Web Service Trust: Towards a Dynamic Assessment Framework ,” در International Conference on Availability, Reliability and Security (ARES’09)2009 ,
    [7] T. G. a. B. Z. N. Guo“ ,A Trusted Web Services Assessment Model Based on Six Dimensional QoS Framework and End-to-End Monitoring ”,International Conference on Service Operations and Logistics, IEEE/SOLI 2008
    [8] D. L. a. C. Yang“ ,A Trust Evaluation Model for Web Service Selection ”,Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI).2010.
    [9] W. Z. a. V. Varadharajan“ ,Trust Management for Web Services ”,International Conference on Web Services (ICWS’08), IEEE.2008.
    [10] M. P. a. M. A. M. C. Z. M. Aljazzaf“ ,Trust Metrics for Services and Service Providers ”,The Sixth International Conference on Internet and Web Applications and Services (ICIW) ,2014
    [11] H. W. a. M. Zhang“ ,A Web Services Trusty Evaluation Model Based on Feedback Credibility ”, International Conference on Intelligence Science and Information Engineering (ISIE).2011.
    [12] J. M. D. B. H. Elshaafi“ ,Trustworthiness Monitoring of Dynamic Service Compositions6 ”,th International Workshop on Enhanced Web Service Technologies, ACM ,2015.

Proposing an Algorithm in order to Detect Communities in Social Networks using Multi-Objective Evolutionary Algorithm
Authors :- Anoosh Mansouri Birgani and Javad Hosseinkhani
Keywords :- Social Network, Social Discovery, multi-objective optimization, centralized node.
Published Online :- 03 March 2016

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    Recently Social networks, a new generation of websites that are in the spotlight these days were Internet users. Such sites are active on the assembly line and each batch of Internet users with certain characteristics come together. Social networks and social media so they know that it is possible to achieve a new way of communicating and sharing content on the Internet have created. Social network concept that the design in virtual space, in real space as well as a sense of community. Social Networking like any social network is made up of community and human relationships in society. The proposed method includes precise mathematical methods and is approximate and innovative, adaptable and efficient. The nature of the problem is due to the nature of intelligent behavior and genetic algorithm based on random behavior of its elements, Genetic algorithm optimization combined with modularity concept that is one of swarm intelligence is used in this research. Therefore, in this study we used a genetic multi-objective optimization algorithm to achieve an increase in modularity. This approach by two criteria, coefficient Do was silhouette index and the quality of the proposed method is determined.
  • View References

    [1] G.Palla, I.Dereyi, I.Farkas and T.Vicsek, ”Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society,” Nature, 2015, 435(7043): 814-818.
    [2] L.Danon, A.Diaaz-Guilera, J.Duch and A.Arenas, ”Comparing Community Structure Identification,” Journal of Statistical Mechanics: Theory and Experiments, 2005.
    [3] M.E.J.Newman, M.Girvan, ”Finding and Evaluating Community Structure in Networks,” Physics Review, E 2004, 69:026113.
    [4] A.Pothen, H. Sinmon, and K-P. Liou, ”Partitioning Sparse Matrices with Eigenvectors of Graphs,” SIAM J. Matrix Anal App., 1990, 11:430-452.
    [5] S.Fortunato and M.Barthelemy, ”Resolution Limit in Community Detection,” Proceedings of the National Academy of Sciences, 2007,104(1):36-41.
    [6] C. Shi, C. Zhong, Zhenyu Yan, et al., ”A Multi-Objective Optimization Approach for CommunityDetection,” CEC2010.
    [7] C. Pizzuti, ”A Multi-objective Genetic Algorithm for Community Detection in Networks,” ICTAI09 379-386. http://wwwpersonal. mejn/netdata.
    [8] T. B. S. de Oliveira and L. Zhao, “Complex Network Community Detection Based on Swarm Aggregation,” 2008, pp. 604-608.
    [9] M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences, vol. 99, p. 7821, 2002.
    [10] A. Ferligoj and V. Batagelj, “Direct multicriteria clustering algorithms,” Journal of Classification, vol. 9, pp. 43-61, 1992.
    [11] M. Tasgin, A. Herdagdelen, and H. Bingol, “Community detection in complex networks using genetic algorithms,” Arxiv preprint arXiv:0711.0491, 2007.
    [12] J. Liu and T. Liu, “Detecting community structure in complex networks using simulated annealing with k-means algorithms,” Physica A: Statistical Mechanics and its Applications, vol. 389, pp. 2300-2309, 2010.
    [13] A. Gog, D. Dumitrescu, and B. Hirsbrunner, “Community detection in complex networks using collaborative evolutionary algorithms,” Advances in Artificial Life, pp. 886-894, 2007.
    [14] J. Liu, W. Zhong, H. A. Abbass, and D. G. Green, “Separated and overlapping community detection in complex networks using multiobjective Evolutionary Algorithms,” 2010, pp. 1-7.
    [15] Charu Aggarwal and Haixun Wang, ” Managing and mining graph data,” Springer, vol. vol. 40, 2010.
    [16] D. Zhou, E. Manavoglu, J. Li, C. L. Giles, and H. Zha, “Probabilistic models for discovering ecommunities,” in Proceedings of the 15th international conference on World Wide Web, 2006, pp. 173-182.
    [17] T. Hofmann, “Probabilistic latent semantic indexing,” in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 1999, pp. 50-57.
    [18] D. M. Blei, A .Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” the Journal of machine Learning research, vol. 3, pp. 993-1022, 2003.
    [19] M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths, “Probabilistic author-topic models for information discovery,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004, pp. 306-315.
    [20] J. Zeng, S. Zhang, and C. Wu, “A framework for WWW user activity analysis based on user interest,” Knowledge-Based Systems, vol. 21, pp . 905-910 ,2008.
    [21] A. McCallum, A. Corrada-Emmanuel, and X. Wang, “Topic and role discovery in social networks,” Computer Science Department Faculty Publication Series, p. 3, 2005.
    [22] Y. Tian, R. A. Hankins, and J. M. Patel, “Efficient aggregation for graph summarization,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008, pp. 567-580.
    [23] Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS one 6(4):e18961
    [24] Meo PD, Ferrara E, Fiumara G, Provetti A (2013) Enhancing community detection using a network weighting strategy. Inf Sci 222:648–668
    [25] L. Danon, A. Díaz-Guilera, J. Duch, and A. Arenas, “Comparing community structure identification,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2005, p. P09008, 2005.
    [26] M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Physical review E, vol. 69, p. 026113, 2004.
    [27] W. W. Zachary, “An information flow model for conflict and fission in small groups,” Journal of anthropological research, pp. 452-473, 1977.
    [28] D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, “The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations,” Behavioral Ecology and Sociobiology, vol. 54, pp. 396-405, 2003.