iosr-JCE   National Conference on Advances in Engineering, Technology & Management (AETM'15)

Abstract: Now a days, popularity of Internet has been increasing tremendously. Also official work, bill payments, shopping etc. has become online. So, in this era Internet is a need rather than mere interest. And the new generation is using Internet very passionately by means of mobile phones, I-Pads, laptops etc. As a result of it, social interactions among people has been transfiguring online. An increasing number of users making social interactions online give a resilience enclave to social networking sites. With increase in size of social structures, the user with off beam intents also increases. So, to deal with the problem of escalating implicit behavior, a strong analysis of the nodal behavior in online social sites is required. In this paper, an attempt has been made to demonstrate the existing approaches incorporating detection of anomalous users' behavior in online social networks like Facebook, Twitter, Myspace, LiveJournal etc. Digging into a comparative study of existing methods, efforts are made to discuss flaws and pros in these approaches, which can help in the exposure of anomalous users' behavior in online social networks.
Keywords: Content Based Analysis, Implicit Behavior, Intent Recognition, Link Based Analysis, Social Network Analysis

[1] M. Lahiri and T. Y. Berger-Wolf, Mining Periodic Behavior in Dynamic Social Networks, In Eighth IEEE International Conference on Data Mining, IEEE, 2008, 373-382.
[2] C. Weinstein, W. Campbell, B. Delaney and G. O'Leary, Modeling and Detection Techniques for Counter-Terror Social Network Analysis and Intent Recognition, In Aerospace conference, IEEE, 2009, 1-16.
[3] L. Tang, X. Wang and H. Liu, Uncovering Groups Via Heterogeneous Interaction Analysis, In Ninth International Conference on Data Mining, IEEE, 2009, 503-512.
[4] M. Cheong and V. Lee, A Study on Detecting Patterns in Twitter Intra-Topic User and Message Clustering, In 20th International Conference on Pattern Recognition (ICPR), IEEE, 2010, 3125-3128.
[5] M. Yassine and H. Hajj, A Framework for Emotion Mining from Text in Online Social Networks, In International Conference on Data Mining Workshops (ICDMW), IEEE, 2010, 1136-1142.

Abstract: Mobile devices are now a day well incorporated with advanced capabilities and technologies such as the Internet, GPS, and Bluetooth etc. Mobile security has become a globally sensitive issue because of the high usage of mobile devices. But as compared to computers and computer networks, they are less protected. The user's of these devices pay less attention to the security updates. The most popular mobile operating system today in the industry is Android. However, with the growing market share of Android smart phone, the malware (malicious software) writers have begun to target the Android operating system. Recently, the most dangerous threat which is targeting Android is 'botnets'. In this paper, we present an introduction of Android botnets and an analysis of currently available families of botnets. By analyzing these samples we reveal common characteristics and behavior of these botnets which will aid in the identification of new malware on Android devices.
Keywords: Android, Application, Botnet, Malware, Mobile,

[1] A. Flo and A. Josang, Consequences of Botnets Spreading to Mobile Devices, Proc. 14th Nordic Conference on Secure IT Systems ,(NordSec), 2009, pp. 37-43.
[2] Android climbed to 79 percent of smartphone market share in 2013, but its growth has slowed. [Online] http://www.engadget.com/2014/01/29/strategy-analytics-2013-smartphone-share/
[3] L. Jae-Seo, J. HyunCheol, P. Jun-Hyung, K. Minsoo, and N. Bong-Nam, The Activity Analysis of Malicious HTTP-Based Botnets Using Degree of Periodic Repeatability, Proc. of the International Conference on Security Technology (SECTECH), 2008, pp. 83-86.
[4] Current android malware, Forensics blog, [online] 2012, http://forensics.spreitzenbarth.de/android-malware/ (Accessed: 13 March 2012).
[5] C.A. Castillo, Android malware past, present, and future, (McAfee), [online] 2010, http://www.mcafee.com/us/resources/white-papers/wpandroid-malware-past-present-future.pdf

Abstract: Cloud Computing is growing rapidly and clients are demanding more services with better results. Therefore, load balancing as well as task scheduling in the Cloud has become a very interesting and important research area. In this paper, we present proposed approach for improving parameters in ant colonies function. The suggested definition of a new computational paradigm, is called Ant Colony Optimization (ACO) algorithm.
Keywords: Load Balancing, Ant colony Optimization, Pheromone, CloudSim

[1] Kunjal Garala, Namrata Goswami, Prashant D. Maheta,A Performance Analysis of Load Balancing Algorithms in Cloud Environment, International Conference on Computer Communication and Informatics (ICCCI -2015),2015.
[2] Raza Abbas Haidri,C. P. Katti, P. C. Saxena,A Load Balancing Strategy for Cloud Computing Environment, IEEE conference,2014, 636-641.
[3] A.KHIYAITA,M.ZBAKH,H. EL BAKKALI,Dafir EL KETTANI,Load Balancing Cloud Computing : State of Art,IEEE,2012, 106-109.
[4] Reena Thakur and Arun Yadav,Solving the Single Processor Scheduling Problem by Using Ant Colony Optimization Technique,IEEE International Conference on Communication Systems and Network Technologies,2012, 759-763.
[5] Kwang Mong Sim and Weng Hong Sun,Ant Colony Optimization for Routing and Load-Balancing: Survey and New Directions,IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS— PART A: SYSTEMS AND HUMANS, 33(5),2003.

Abstract: Association rule mining is used to find all subsets of items which frequently occur and the relationship between them. Apriori algorithm is used to find out the frequent itemset. But this algorithm is not efficient as it scans the database number of times and takes a lot of time to give the results. In order to solve this problem a new technique is put forward in this paper. The improved algorithm first creates the index of database. It also uses a temporary table technique in the generation of frequent item-set. Also the comparison of new improved algorithm is done with the existing Apriori algorithm. For the simulation the graph is plotted with time variant and data bases. The graph depicts that the improved algorithm shows better performance as compared to the existing algorithm. The improved Apriori algorithm will effectively decrease the system overhead and reduce the running time.
Keywords: Data Mining, Association rules, frequent itemset, Apriori Algorithm.

[1]. Jiawei Han, Micheline Kambar, Jian Pei, data mining concepts and techniques( Morgan Kaufman, 2000)
[2]. Agrawal, R., Imielinski, T., and Swami, A. N., Mining association rules between sets of items in large databases, International Conference on Management of Data, May 1993. pp. 207-216.
[3]. Agrawal, R., Srikant, R., Fast Algorithms for mining association rules in large databases, International Conference on Very Large Databases, Santiago de Chile, 1994. pp 487-489.
[4]. Mamta Dhanda, Sonali Guglani and Gaurav Gupta, Mining Efficient Association Rules through Apriori Algorithm Using Attributes IJCST Vol. 2, Issue 3, 2011, pp. 2229 – 4333.
[5]. M Suman, T Anuradha, K Gowtham, A Ramakrishna, A Frequent Pattern Mining Algorithm Based On FP-Tree Structure And Apriori Algorithm, International Journal of Engineering Research and Applications (IJERA) ISSN, Vol. 2, Issue 1 2012, pp.114-116.

Abstract: Energy Efficiency for wireless Sensor Network is an issue now days due to resource constraints on sensor nodes. Actually each node need different amount of energy to transmit the data because base station is located at variable distance from nodes in the network. The hierarchical protocols like LEACH, PEGASIS and HEED provide solution to energy regarding problems. Sometimes these protocols not provide a best solution or optimal solution. In this paper we have proposed a protocol for enhancing the network lifetime by proving shortest path to nodes. To achieve this design objective we have applied PEGASIS (Power Efficient GAthering in Sensor Information System) with ACO (Ant Colony Optimization) to form a data gathering chain and an efficient shortest path to the base station. In this scheme the each node communicates with a close neighbor only and sends data to that neighbor. This helps to reduce the power consumption during transmission.
Keywords: Heterogeneous network, optimization, Power Management, Quality of service, Shortest Path.

[1] Th.Arampatzis, J. Lygeros and S. Manesis, A survey of wireless sensors and wireless sensor network, IEEE international symposium on, mediterrean conference on control and automation, June 2005, 2158-9860.
[2] Jennifer Yick, Biswanath Mukhrjee, Dipak Ghosal, Wireless sensor Network, CN52 2008, 2292-2330.
[3] F.L.LEWIS. (2004). wireless sensor networks chapter 2(Automation and robotics research institute, New York 2005).
[4] Stephaine Lindsey, Cauligi Raghavendra, krishna M. Sivalingam,Data gathering algorithm in sensor networks using energy metrics, IEEE transaction on parallel and distributed systems,13(9),2002, page 924-935.
[5] Saira Banu and R. Dhanasekaran, A new Multipath routing approach for energy efficient in wireless sensor networks ,International journal of computer applications,55(11),October 2012,0975-8887.

Abstract: Load Balancing is crucial for various operations in distributed situations. Now a days, Cloud Computing is getting a lot of consideration. Users are requesting for more administrations as well as effective results. To achieve this, load balancing is necessary hence, it turned into an extremely interesting for research. In distributed environment, adequate amount of resources are required which are used in such a way that resources are not over-utilized or under-utilize in any circumstance. Many researchers suggest different approaches for load balancing. This paper shows the new approach for Dynamic Load Balancing using the concept of Agent. In this new approach, an entity known as mobile agent performs the basic task. Mobile agent is a software program which executes independently. This paper also compares the proposed protocol with the traditional scheme used for load balancing and the results concludes that the proposed approach greatly reduces the communication cost of servers, accelerates the rate of load balancing which indirectly improves the Throughput and Response Time of the cloud.
Keywords: cloud computing, mobile agent, load balancing.

1] P. Mell and T. Grance, "The nist definition of cloud computing (draft)", Tech. rep., National Institute of Standards and Technology, 2011.
[2] V.Venkatesa Kumar & K. Dinesh, "Job Scheduling Using Fuzzy Neural Network Algorithm in Cloud Environment", Bonfring International Journal of Man Machine Interface, Vol. 2, No. 1, March 2012.
[3] Alexa Huth and James Cebula."The Basics of Cloud Computing", Carnegie Mellon University. Produced for United State Computer Emergengy Readingness Team, a government organization, 2011.
[4] Young Joon Lee, Geon Yong Park, Ho Kuen Song, and Hee Yong Youn, "A Load Balancing Scheme for Distributed Simulation Based on Multi-Agent System",IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW), pp. 613-618, July 2012.
[5] Wentao Liu, "Research on Cloud Computing Security Problem and Strategy", IEEE International Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang, pp. 1216-1219, April 2012.

Abstract:Program slicing is a process to classify the program into number of parts based on various types of dependencies between program statements.This paper presents an overview of basic concept of generating static program slices and on the basis of these program slices generating and prioritization of test cases.
Keywords: Program dependency, static program slices, test cases prioritization and execution history

[1]. M.Weiser, Programmers use slices when debugging, Communications of the ACM, vol. 25, 446-452(1982)
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[5]. Andrea De Lucia, Program Slicing: Methods and Applications, IEEE international workshop,2001

Abstract: An overview and a derivation of interval type-2 fussy logic system (IT2 FLS), which can handle rule's uncertainties on continuous domain, having good number of applications in real world. This work fo-cused on the performance of an IT2 FLS that involves the operations of a fuzzification, inference, and output processing. The output processing consists of Type-Reduction (TR) and defuzzification. This work made IT2 FLS much more accessible to FLS modellers, because it provides mathematical formulation for calculating the de-rivatives. Presenting extend to representation of T2 FSs on continuous domain and using it to derive formulas for operations, we developed and extended the derivation of the union of two IT2 FSs to the derivation of the intersection and union of N-IT2 FSs that is based on various concepts. The derivation of all the formulas that are related with an IT2 and these formulas depend on continuous domain with multiple rules. Each rule has multiple antecedents that are activated by a crisp number with T2 singleton fuzzification (SF). Then, we have shown how those results can be extended to T2 non-singleton fuzzification (NSF). We are derived the relation-ship between the consequent and the domain of uncertainty (DOU) of the T2 fired output FS. As well as, provide the derivation of the general form at continuous domain to calculate the different kinds of type-reduced. We have also applied an IT2 FLS to medical application of Heart Diseases (HDs) and an IT2 provide rather modest performance improvements over the T1 predictor. Finally, we made a comparison of HDs result between IT2 FLS using the IT2FLS in MATLAB and the IT2 FLS in Visual C# models with T1 FISs (Mamdani, and Takagi-Sugeno).
Keywords: Type-1 fuzzy logic system, Type-2 fuzzy sets, Type-2 fuzzy logic system, Type-2 membership func-tions, Interval type-2 fuzzy systems, Footprint of uncertainties, Type-reduction, Data base of Heart diseases.

[1]. J. Castro, O. Castillo, P. Melin, and A. Díaz, Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox, Trans. on Computer Science, 50, 2008, 104–114.
[2]. R. Hndoosh, M.S. Saroa, and S. Kumar, The derivation of interval type-2 fuzzy sets and systems on con-tinuous domain: theory and applications to heart diseases, International Journal of Science, 3(10), 2014, 35-54.
[3]. R. Hndoosh, M.S. Saroa, and S. Kumar, Fuzzy mathematical models of type-1 and type-2 for computing the parameters and its applications, International Journal of Computer Applications, 104(14), 2014, 17-28.
[4]. N. Karnik, J. Mendel, Centroid of a type-2 fuzzy sets, An International Journal of Information Sciences, 132, 2001, 195-220.
[5]. N. Karnik, J. Mendel, Operations on type-2 fuzzy sets, Fuzzy Sets and Systems, 122, 2001327–348.

Abstract: The world-wide use of internet has increased the theft to the security of information that is being transferred from one system to another over the internet. In order to provide security to the information different information hiding methods have developed. One of the most widely used method to provide information security is the use of visual cryptography methods. Visual cryptography is a different encryption technique that provides information security by hiding the important data (Secret information) in the images. In this paper we propose a method that combines the Floyd-half-tone and Region incrementing approach to perform visual cryptography. The proposed method takes in the secret image and a different image as the cover image. The cover image is converted into the CMY color model and each CMY component is then half-toned and reverse half-toned. A random-select method is designed which processes every pixel of every region of the secret image in an incrementing method and embeds the processed pixels in each of the half-toned and reverse half-toned components. The shares are then generated by combining the half-toned and reverse-half-toned components. The secret image is obtained only after all the half-toned share is combined.
Keywords: Cover-image, CMY-color model,Floyd-halftone, Pixel-processing,Region-Incrementing,Random-select, Secret-image, Share-generation.

[1]. Niraj Kumar, Prof. Sanjay Agrawal, An Efficient and Effective Lossless Symmetric Key Cryptography algorithm for an image, International Conference on Advances in Engineering and Technology Research (ICATER), IEEE transaction, 2014, 1-5.
[2]. MoayedMajidJavid, Ghani, A Survey on Cryptography Algorithms in Security of Voting System Approaches, International Conference on Computational Scieneces and its Application, IEEE transaction, 2008, 190-200.
[3]. F. Liu,C.K.Wu, X.J. Lin, Colour Visual cryptography schemes, IEEE Conference on IET Information Security, 2008, 151-165.
[4]. J Ramya, B. Parvathavarthini, An extensive review on Visual cryptography Schemes, International Conference on Control, Instrumentation, Communication and Computational Technology ICCICCT, IEEE transaction, 2014, 223-228.[5] Wang Z, Arce G.R, Di. Crsenzo, Halftone visual Cryptography using Error Diffusion, IEEE Transaction on Information Forensics and Security, 15(8), 2006, 2441-2453.
[5]. Xiaotian Wu, Wei Sun, Extended Capabilities for XOR-Based Visual Cryptography, IEEE Transaction on Information Forensic and Security, 9(10), 2014, 1592-1605.

Abstract: The struts2 framework is a brand new framework that introduces many architectural refinements over the existing ones to make the web based system more efficient to use.The major features of Struts 2 are Interceptor, user interface tags, result and validation. Now a days we have a lot of data access frameworks like Hibernate, JPAs, JDBC, etc. Struts can integrate easily with the popularly used data access frameworks.Hibernate is one of them that easily integrates with other Java EE frameworks.Hibernate ORM helps your application to achieve persistence.In this paper we describe and analyze the modern frameworks such as struts2 and hibernate.Also integerating them.Here we used framework Struts2 as frontend and hibernate as backend. The integrated framework will provide a better database access with better frontend design.
Keywords: Hibernate,Interceptors,Model View Controller (MVC),Object Relational Mapping(ORM),Struts2.

[1]. Zhou Dongxing, Li Xinke, "Design and application of a web development model based on MVC and AJAX",JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY, Vol 31 No. 9 Sept.2008.
[2] Nielet D"mello1 , Larkins Carvalho2, "- The modern web application framework" Vol. 3, Issue. 3, May - June 2013
[3] G.King.,C.Bauer, "Hibernate in Action", Manning Publication Co, 2004.
[4] C.Bauer, G.King, "Java Persistence with Hibernate",Manning Publication Co, 2007
[5] A. Keller and C. Keene (1993), Persistence software: bridging object-Oriented programming and relational databases, In Proceedings of the ACM SIGMOD Int‟ l Conference on Management of Data, pp. 540-541.

Abstract: 4-G mobile communications system is offering high speed data communications technology having connectivity to all sorts of the networks including 2-G and 3-G mobile networks. Authentication of a mobile subscriber (MS) or a sub network and a main network are an important issue to check and minimize security threats or attacks. An advanced artificial intelligence based mutual authentication system applying fuzzy neural network with four entities is proposed. Voice frequency of the salutation or the selective words used by a subscriber like Hello, Good Morning etc. is taken as first entity. Second entity is chosen as thumb fingerprint matching of the calling subscriber. Then third entity is taken as face image matching of the calling subscriber. Fourth entity is granted as probability of the salutation or the greeting word from subscriber's talking habit while initializing a call. These four entities such as probability of particular range of frequencies for the salutation word, the thumb fingerprint matching, the face image matching of the subscriber by pixel cluster indexing , using particular salutation or greeting word at the time of starting a call are used with most frequently, more frequently and less frequently by the calling subscriber like uncertainty in Artificial Intelligence (AI). Now different relative grades are assigned for most frequently, more frequently and less frequently used parameters. Fuzzy operations like intersection or minimum and union or maximum are computed taking three membership functions at a time out of four membership functions to adopt fuzzy neural network. Thereafter the optimum or the final fuzzy operations are computed according to the assumed weightages. Lastly the optimized fuzzy operations are defuzzified by Composite Maxima method and the results are tested according to the invented fuzzy neural rule. If the results are satisfactory, the subscriber or the sub network and the network (the switch or the server) are mutually authenticated in 4-G mobile communications.
Keywords: Face image matching, Fuzzy neural network, Fuzzy operation, Mutual authentication, Pixel cluster indexing technique, Salutation word, Thumb fingerprint matching.

[1] T. S. Rappaport, Wireless Communication: Principles and Practice (Prentice Hall Pub Ltd, 2nd Edition, 2007).
[2] William C. Y. Lee, Wireless and Cellular Communications (McGraw Hill Publishers, 3rd Edition, 2008).
[3] P. K. Bhattacharjee, A New Era in Mobile Communications− GSM and CDMA, National Conference on Wireless and Optical Communications (WOC-07), Punjab Engineering College (D.U), Chandigarh, India, 2007, 118-126.
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[5] P. K. Bhattacharjee, and S. Roy, A Great Land Mark To Achieve 4-G Mobile Communications, International Journal of Research in Engineering and Technology (IJRET), Assam University, India, 2(2), 2013, 1-8.