iosr-JCE   Volume-17 ~ Issue-1 ~ Jan-Feb 2015

Abstract:Brain tumor extraction in magnetic resonance imaging (MRI) has becoming an emergent research area in the field of medical imaging system. Extraction involves detection, localization, tracking, enhancement and recognition of the tumor from the MR brain images. Brain tumor extraction helps in finding the exact size and location of tumor. The watershed transform is a popular and has interesting properties that make it useful for many image segmentation applications. The intuitive description of this transform is quite simple, can be parallelized and always produces a complete division of the medical images. One of the important drawbacks associated to the watershed transform is the over segmentation that commonly results in brain images. We present an improvement to the watershed transform in this paper for the extraction of brain tumor based on segmentation and morphological operator. The tumor may be benign, pre-malignant or malignant and it needs medical support for further classification.

Keywords: Brain Tumor, Morphological Operators, MRI, Segmentation, Watershed

[1]. V. Grau, A. U. J. Mewes, M. Alcañiz, Improved Watershed Transform for Medical Image Segmentation Using Prior Information, IEEE Transactions On Medical Imaging, Vol. 23(4), 2004,447-458.
[2]. A. Mustaqeem, A. Javed, T. Fatima, An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation, International Journal of Image, Graphics and Signal Processing,Vol.10(3), 2012, 34-39.
[3]. T. Logeswari, M. Karnan, An improved implementation of brain tumor detection using segmentation based on soft computing, Journal of Cancer Research and Experimental Oncology, Vol. 2(1), 2010, 06-14.
[4]. M. Brummer, R. Mersereau, R. Eisner, and R. Lewine, Automatic detection of brain contours in MRI data sets, IEEE Trans. Med. Imag.,Vol. 1(4), 1993,153–166.
[5]. M.S. Atkins and B.T. Mackiewich, Fully Automatic Segmentation of the Brain in MRI, IEEE Tran. On medical imaging, Vol. 17 (1 ), 1998, 98-107.
[6]. A. Hammers, Automatic anatomical brain MRI segmentation combining label propagation and decision fusion, NeuroImage, Vol. 33(1), 2006, 115–126..

Abstract: Digital image compression techniques minimize the size in bytes of a graphics file without degrading the quality of the image to an acceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. The proposed paper summarizes the different compression techniques such as Singular Value Decomposition, Block Truncation Coding, Discrete Cosine Transform and Gaussian Pyramid on the basis of Peak Signal to Noise Ratio, Mean Squared Error, Bit Rate to evaluate the image quality for both gray and RGB images. The comparison of these compression techniques are classified according to different biometric images.

Keywords: Biometric, Block Truncation Coding, Discrete Cosine Transform, threshold, Gaussian Pyramid, Singular Value Decomposition, Compression..

[1]. Pardeep Singh, etal., A Comparative Study: Block Truncation Coding, Wavelet, Embedded Zerotree and Fractal Image Compression on Color Image, Vol 3, Issue-2, pp.10-21, 2012.
[2]. David Solomon, Data compression The Complete Reference 2nd edition, Springer 2001, Newyork.
[3]. Kiran Bindu, etal., A Comparative Study of Image Compression Algorithms, IJRCS, Vol 2, Issue 5,pp 37-42,2012.
[4]. V. CKloma, The singular value decomposition: its computation and some applications, IEEE Transactions in automatic control, Vol. 25, pp. 164-176, 1980.
[5]. Mulcahy, Colm & Rossi, John: Atlast: A Fresh Approach to Singular Value Decomposition.
[6]. Delp E.J, O.R. Mitchell,"Image Compression using Block Truncation Coding", IEEE Trans. Communications, Vol. 27 pp 1335-1342, September 1979.

Abstract: Image segmentation plays an important role in image analysis. Image segmentation is useful in many applications like medical, face recognition, crop disease detection, and geographical object detection in map. Image segmentation is performed by clustering method. Clustering method is divided into Crisp and Fuzzy clustering methods. FCM is famous method used in fuzzy clustering to improve result of image segmentation. FCM does not work properly in noisy and nonlinear separable image, to overcome this drawback, KFCM method for image segmentation can be used. In KFCM method, Gaussian kernel function is used to convert nonlinear separable data into linear separable data and high dimensional data and then apply FCM on this data. KFCM is improving result of noisy image segmentation. KFCM improves accuracy rate but does not focus on neighbor pixel. NMKFCM method incorporates neighborhood pixel information into objective function and improves result of image segmentation. New proposed algorithm is effective and efficient than other fuzzy clustering algorithms and it has better performance in noisy and noiseless images. In noisy image, find automatically required number of cluster with the help of Hill-climbing algorithm.

Keyword: Component: Clustering, Fuzzy clustering, FCM, Hill-climbing algorithm, KFCM, NMKFCM

[1]. Chun-Yan Yu,Ying Li ,Ai-lian,Jing-hong liu ,"A novel modified kernel fuzzy c- means Clustering algorithms on Image segmentation", 14th IEEE International conference.978-0-7695-4477,2011.
[2]. Songcan Chan and Daoqiang Zhang, "Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure", IEEE transactions On Systems, MAN And Cybernetics-Part B: Cybernetics, Vol-34, No.4, August 2004.
[3]. L.A.Zadeh , "Fuzzy Sets ",Information And Control,8,pp.338-353,1965.
[4]. Elnomery Zanaty and Sultan Aljahdali,"Improving Fuzzy Algorithms for Automatic Magnetic Resonance Image Segmentation", The International Arab Journal of Information Technology, Vol.7, No.3, pp.271-279, July 2009.
[5]. Prabhjot Kaur,Pallavi Gupta,Poonam Sharma, "Review and Comparison of kernel Based Fuzzy Image Segmentation Techniques", I.J. Intelligent Systems and Applications,7,pp.50-60, 2012.

Abstract: The concept changes in continuously evolving data streams are termed as concept drifts. It is required to address the problems caused due to concept drift and adapt according to the concept changes. This can be achieved by designing supervised or unsupervised techniques in such a way, that concept changes are considered, and useful knowledge is extracted. This paper discusses various techniques to manage concept drifts. The synthetic and real datasets with different concept drifts and the applications are discussed.
Keywords: Drift detectors, ensemble classifiers, data stream mining, and bagging.

[1]. P. M. Goncalves, Silas G.T. de Carvalho Santos, Roberto S.M. Barros, Davi C.L. Vieira, (2014) "Review: A comparative study on concept drift detectors", A International Journal: Expert Systems with Applications,8144–8156.
[2]. L. L. Minku and Xin Yao(2011), "DDD: A New Ensemble Approach For Dealing With Concept Drift", IEEE TKDE, Vol. 24, pp. 619 - 633.
[3]. M. G. Kelly, D. J. Hand, and N. M. Adams(1999), "The Impact of Changing Populations on Classifier Performance", In Proc. of the 5th ACM SIGKDD Int. Conf. on Knowl. Disc. and Dat. Mining (KDD). ACM, 367–371.
[4]. J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, A. Bouchachia(2014), "A Survey on Concept Drift Adaptation ", ACM Computing Surveys, Vol. 46, No. 4, Article 44.
[5]. Mouss, H., Mouss, D., Mouss, N., Sefouhi, L.(2004), "Test of Page-Hinkley, an Approach for Fault Detection in an Agro-Alimentary Production System", 5th Asian Control Conference, IEEE Computer Society, vol. 2, pp. 815--818.
[6]. S. Delany, P. Cunningham, A. Tsymbal, and L. Coyle. (2005),"A Case-based Technique for Tracking Concept Drift in Spam filtering", Knowledge-Based Sys. 18, 4–5 , 187–195.

Abstract: The performances of a transfer function characteristic of RLC-circuit is investigated and modeled in this paper. The ever increasing demand for electronics has led to the continuous search for the most readily available means of providing better performance of the system. The method of analyzing RLC circuit is never constant and since the resistor (R), inductor (L), and the capacitor (C) are use in every electronic system, a proper understanding of system is necessary to know what happen to system when any of parameter is alter. The design methodology employed in this work involves: the modeling of the equation of the RLC circuit, developing appropriate algorithms to imitate the real life behavior of an RLC circuit, Appling matlab codes in the m-file and carrying out analysis of the designed RLC circuit in the Matlab GUI. Result obtained showed good proper behavior of the system when the input parameters were varied.

Keywords: RLC-circuit, matlab, gui, transfer function

[1]. Alexander. C. K. and M. O. Sadiku, fundamental of Electric circuits, 4th Edition, Yrok: McGraw-Hill, 2004.
[2]. Attia, J. O(1990) Electronic and Circuit Analysis using Matlab, Boca Raton: CRC Press LLC, 1999
[3]. Circuit Analysis I with MATLAB Applications, ISBN 978−0−9709511−2−0
[4]. Karris, S.T (2003): Signals and Systems with MATLAB Applications, HTTP:// www.orchardpublication.com
[5]. Matlab (2009): Overview of the MATLAB Environment. Matlab Getting Stated Guide, the Mathwork, Inc
[6]. Mathwork, Inc.(2009):MATLAB 7.9, Mathwork Inc. Natic, MA.

Abstract: Wireless Technologies are very useful due to their use. Mobile Ad-hoc Network (MANET) has become an essential technology in field of research In MANET, Clustering is an important research area, it offers several advantages like it improves stability and decrees the overhead of the network that increases the efficiency of the network. Clustering increases system capacity by reusing available resources. This Survey paper analyzes number of Cluster head Selection Techniques that are widely used for partitioning mobile host into distinct virtual groups. Each Clustering Technique uses various parameters for selecting Cluster head in cluster. Cluster head is work as leader in cluster and maintain the whole network information which decreases the computation cost and routing overhead of the network in MANET. Each technique is used on the bases of their parameters. Each approach has its own pros and cons.

Keywords: Mobile Ad-hoc Network, Clustering, Cluster head (CH), Cluster Gateway, Cluster Member

[1]. Jane Y. Yu and Peter H, J. Chong "A Survey of clustering schemas for Mobile Ad Hoc Network" Presented at IEEE Communications Surveys & Tutorials, First Quarter 2005.
[2]. Dr. Mohammad Bokhari and Hatem S, Hamatta "A Review of Clustering Algorithms as Applied in MANETs" Presented at IJARCSSE, November-2012.
[3]. Ratish Agrawal and Dr. Mahesh Motwani "Survey of Clustering Algorithms for MANETs"published at International Journal on Computer Science and Engineering, Volume 1, Issue 2, February-2009.
[4]. Sheetal Mehta, Priyanka Sharma and Ketan Kotecha "A Survey on Various Cluster Head Election Algorithms for MANET" presented at International Conference on Concurrent trends in technology, "NUiCONE – 2011.
[5]. G. Chen, F. Nocetti, J. Gonzalez, "Connectivity based K-hop clustering in wireless networks". Proceedings of the 35th Annual Hawaii International Conference on System Sciences. Vol.7, pp. 188.3, 2002
[6]. Narendra Singh Yadav, Bhaskar P. Deosarkar, and R.P.Yadav "A Low Control Overhead Cluster Maintenance Scheme for Mobile Ad hoc Networks" published at International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May-2009.

Abstract: Many applications executing on cluster architectures consisting of several number of computers create several problems with reliability. The problems are system crashes, code failures and network problems etc. This paper using Fault tolerant mechanisms to recover the data in cluster computing. The two Rollback Recovery protocols[5] are Theft Induced check pointing protocol (TIC) and systematic event logging protocol TIC protocol is used to recover the information during crash, the information is send to client (receiver) by using SEL protocol. It is proven that the complexity of protocol is very small and the information lost by crashed process is recovered here.

[1]. Ivan Stojnenvic, "Hand book of wireless Networks and Mobile computing".
[2]. Elsevier, "Computer Architecture".
[3]. Arun K.Pujari, "Data Mining techniques".
[4]. D.K. Pradhan, "Fault tolerant computer system design".
[5]. R. Baldoni, "A communicative Individual check pointing protect that ensures Rollback- Dependency tractability".
[6]. L. Alvisi and K. Mazullo, "Message logging: pessimistic, optimistic, casual and optimal".
[7]. V. Strumpen, "Potable and fault- tolerant S/w systems".
[8]. D.curen," A survey of simulation in sensor networks"University Of Binghamaton project report for subject CS580.

Abstract: Trinity – An unconventional structure for automatically catch or extract the content from the website or the webpages by the source of internet. The basic applications are done by the trinity characteristics in order to gather the data in the form of sequential or linear tree structure or format. Many users will be searching for the effective and efficient device in order to perform the optimized solution without any big loss or expenditure. In this system an automatic parser is placed at the back end of the complete trinitized format or structure. Now it performs the action or task of sub-dividing the extracted web content in the form of small pieces of web content which has three main categories as prefix, suffix and separator. Once the action of gathering is completed, now the extracted content of the located data in the relevant webpages. Gradually that data will be cleaned and formatted for the calculation which results in an effective and efficient cost comparative system. In this proposed system an 'Ant Colony Optimization ' algorithm is used in order to extract the relevant content from the website. Finally the trinity will computes and executes any major estimation problem or collision of the device or system.. The Ant colony optimization provides accuracy without NP-Complete Problem.

Keywords: Automated crawling, extraction, formatting style; scrutiny; styling;

[1]. Yanhong Zhai and Bing Liu, ―Web Data Extraction Based on Partial Tree Alignment‖ IEEE Trans. Knowl. Data Eng., vol. 18, no. 10, pp. 1411–1428, Oct. 2010.
[2]. Andrew Carlson and Charles Schafer ―Bootstrapping Information Extraction from Semi-structured Web Pages‖ IN ECML PKDD 2008, Part I, LNAI 5211, pp. 195–210, 2008. c Springer-Verlag Berlin Heidelberg 2008
[3]. H. A. Sleiman and R. Corchuelo, ―An unsupervised technique to extract information from semi-structured web pages,‖ in Proc. 13th Int. Conf. WISE, Paphos, Cyprus, 2012, pp. 631–637.
[4]. FatimaAshraf,TanselO¨zyer,and Reda Alhajj ―Employing Clustering Techniques for Automatic Information Extraction From HTML Documents ''in IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 38, no. 5, September 2008
[5]. C.-H. Chang, M. Kayed, M. R. Girgis, and K. F. Shaalan, ―A survey of web information extraction systems,‖ IEEE Trans. Knowl. Data Eng., vol. 18, no. 10, pp. 1411–1428, Oct. 2006.

Abstract:Discrete cosine transform emerged as a popular mathematical tool in past decade and widely used in image compression algorithm due to its high energy compaction capacity. But this energy compaction has some limitation in blocking artifacts that overcome by another mathematical tool wavelet transform based compression which have both frequency and spatial component study at same time. In this paper we study a comparison between DCT based image compression and wavelet based image compression using CDF9/7 wavelet as used in JPEG-2000 Standards based on common encoding scheme known as Huffman encoding for both. We also analyzed a trend based on different level of wavelet transform on image size and image quality based on mean square error.
Keywords: Wavelet transform, Image Compression, Discrete Cosine Transform, Fast wavelet algorithm, Huffman encoding.

[1]. M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision (Tata McGraw Hill Education Private Limited, 2008).
[2]. B. Nilesh, S. Sachin, N. Pradip, D. Rane, Image compression using discrete wavelet transform, International Journal of Computer Technology and Electronics Engineering (IJCTEE) 3 (2013) 85-89.
[3]. C. Lui, A study of the jpeg-2000 image compression standard (May 2001).
[4]. D. F. Walnut, An Introduction to Wavelet Analysis, (Brikhauser, 2002).
[5]. R. C. Gonzalez, R. E. Wooda, S. L.Eddins, Digital Image Processing Using Matlab, (Tata McGraw Hill Education Private Limited, 1989).
[6]. Cohen-daubechies-feauveau-wavelet (April 2014). URL http://en.wikipedia.org/wiki/Cohen-Daubechies-Feauveau-wavelet.
[7]. T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, 3rd Edition, 2009.

Abstract: A personal identification number (pin) is a widely used numeric password. The 4-digit pin numeric password is being used as authentication in many important applications such as, an ATM. An ATM is a place where the shoulder surfing attack is of great concern. There are some existing methods that provide security to the pin entry. But, those methods use only limited cognitive capabilities of the human adversary. The major disadvantage that exists here is that human adversaries can be more effective at eavesdropping and assumptions by training themselves. The proposed method called improved black white (BW) method can be more secure, as it uses bi-colored keys. Another contribution is the authentication service that uses local databases and a hash function. The hash function is mainly used to send the pin securely to the server through the public channel. An ATM application is created as an android application, where transactions can be performed in smart phones using a virtual money concept.

Keywords: personal identification number; improved black white (BW) method; virtual money; hash function; shoulder surfing attack.

[1]. V. Roth, K. Richter, And R. Freidinger, ―A Pin-Entry Method Resilient Against Shoulder Surfing,‖ In Proc. Acm Conf. Comput. Commun Security, 2004, Pp. 236– 245.
[2]. A. D. Luca, K. Hertzschuch, And H. Hussmann, ―Colorpin: Securing Pin Entry Through Indirect Input,‖ In Proc. Chi, 2010, Pp. 1103–1106.
[3]. A. Bianchi, I. Oakley, V. Kostakos, And D.-S. Kwon, ―The Phone Lock: Audio And Haptic Shoulder-Surfing Resistant Pin Entry Methods For Mobile Devices,‖ In Proc. Tei, 2011, Pp. 197–200.
[4]. W. S. Geisler And B. J. Super, ―Perceptual Organization Of Two-Dimensional Patterns,‖ Psychol. Rev., Vol.107, No. 4, Pp. 677–708, 2000.
[5]. X. Bai, W. Gu, S. Chellappan, X. Wang, D. Xuan, And B. Ma,― Pas: Predicate Based Authentication Services Against Powerful Passive Adversaries,‖ In Proc. Ieee Annu. Comput. Security Appl. Conf., Dec. 2008, Pp. 433–442.
[6]. T. Kwon, S. Shin, And S. Na, ―Covert Attentional Shoulder Surfing: Human Adversaries Are More Powerful Than Expected,‖ Ieee Trans. Syst., Man, Cybern, Syst., Pp. 1–12, To Be Published.

Abstract: We consider the problem of fixed priority scheduling of non-preemptive strict periodic tasks in conjunction with sporadic preemptive tasks. There are few studies about the scheduling problem combining these two kinds of tasks. Moreover, only few results are available on scheduling non-preemptive strict periodic tasks since their performance analysis gives low success ratios. Also, strict periodic tasks are of great importance since they are in charge for example of sensors/actuators or feedback control functions which are all critical in feedback control systems. Such tasks must have the highest priorities in order to guarantee a correct behavior of the control system. Preemptive sporadic tasks can be used for non-critical functions and have lower priorities.We first investigate the scheduling problem of non-preemptive strict periodic tasks by recalling an existing schedulability condition. This results in defining the first release times of strict periodic tasks that preserves the strict periodicity constraints.

[1]. P. Marti, J. Fuertes, G. Fohler, and K. Ramamritham. Jitter compensation for real-timecontrol systems. In Real-Time Systems Symposium, 2001. (RTSS 2001). Proceedings. 22ndIEEE, pages 39 - 48, dec. 2001.
[2]. B. Lincoln. Jitter compensation in digital control systems. In American Control Conference,2002. Proceedings of the 2002, volume 4, pages 2985 - 2990 vol.4, 2002.
[3]. M. Behnam and D. Isovic. Real-time control design for flexible scheduling using jitter margin, 2007.
[4]. P. Balbastre, I. Ripoll, and A. Crespo. Minimum deadline calculation for periodic real-timetasks in dynamic priority systems. IEEE Trans. Comput., 57:96-109, January 2008.
[5]. M. Joseph and P. K. Pandya. Finding response times in a real-time system. Comput. J.,29(5):390-395, 1986.
[6]. K. Je_ay, D. F. Stanat, and C. U. Martel. On non-preemptive scheduling of period andsporadic tasks. In Proceedings of the 12 th IEEE Symposium on Real-Time Systems, pages129-139, December 1991.

Abstract: Several unlinkable secret handshakes schemes have been proposed in recent years. As performing the successful secret handshakes is essentially equivalent to computing a common key between two interactive members of the same group. Therefore secret handshakes scheme is a key agreement protocol between two members of the same group. So it is necessary for a secret handshakes scheme to fulfill security requirement of secret handshakes protocol as well as key agreement protocol. In this Paper, we show that the Ryu et al. unlinkable secret handshakes scheme does not provide key compromise impersonation resilience which is an important requirement in ID-based two party authenticated key exchange scheme.

Keywords: Authentication, Key Agreement, Unlinkability, Secret Handshakes, Cryptanalysis

[1]. D. Balfanz, G. Durfee, N. Shankar, D. Smetters, J. Staddon, And H.-C. Wong, Secret Handshakes From Pairing Based Key
Agreement, IEEE Symposium On Security And Privacy, 2003, 180-196.
[2]. J. Gu And Z. Xue, An Improved Efficient Secret Handshakes Scheme With Unlinkability, IEEE Communications Letters, 15(2),
2011, 259-261.
[3]. H. Huang And Z. Cao, A Novel And Efficient Unlinkable Secret Handshakes Scheme, IEEE Communications Letters, 13(5), 2009,
363-365.
[4]. E. K. Ryu, K. Y. Yoo And K. S. Ha, Efficient Unlinkable Secret Handshakes For Anonymous Communications, Journal Of
Security Engineering, 17(6), 2010, 619-626.
[5]. R. Sakai, K. Ohgishi And M. Kasahara, Cryptosystems Based On Pairing, Symposium On Cryptography And Information Security,
2000, 26-28.
[6]. R. Su, On The Security Of A Novel And Efficient Unlinkable Secret Handshakes Scheme, IEEE Communications Letters, 13(9),
2009, 712-713.

Abstract: This paper will describe a method for optimizing software testing by finding the most error prone paths in the program. This can be achieved by a meta-heuristic technique, that is by using genetic algorithm and particle swarm optimization. As exhaustive software testing is not possible where software is tested with all the possible inputs , those parts of software are also tested which are not error prone..Goal is to generate test cases using both the algorithms and then comparative study is done between Particle swarm optimization and genetic algorithm.

Keywords: Software Testing, Meta-heuristics , Genetic Algorithms, Particle Swarm Optimization, Software test cases.

1]. B. Beizer,Software testing techniques, Second Edition, Van Nostrand Reinhold, NewYork, 1990.
[2]. Nirmal Kumar Gupta and Dr. Mukesh Kumar Rohil,, Using Genetic Algorithm for Unit Testing Of Object Oriented Software, IJSSST, Vol.10, No.3.
[3]. S. Desikan and G. Ramesh, ,Software testing principles & practices, Pearson Education, 2007.
[4]. K. F. Man, ,K. S. Tang, and S. Kwong,,Genetic Algorithms: Concepts and Applications, IEEE Transaction on Industrial Electronics ,VOL. 43, NO. 5, October 1996..
[5]. Abhishek Singhal, Swati Chandna and Abhay Bansal, Optimization of Test Cases Using Genetic Algorithm, International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume 2, Issue 3, March 2012.
[6]. Sanjay Singla, Dharminder Kumar, H M Rai3 and Priti Singla, Hybrid PSO Approach to Automate Test Data Generation for Data Flow Coverage with Dominance Concepts, International Journal of Advanced Science and Technology Vol. 37, December, 2011

Abstract: Security of transferred data is a big concern for data networks users .One of the best security solutions is the IPSec Virtual Private Networking (IPSec VPN).In this paper, The Impact of additional traffic generated by IPSec protocol on the network's resources performance is discussed. Network simulation is a major part of this Paper. OPNET simulation software is used. Analyzing the impact of applying IPSec VPN to secure a flow of traffic from a remote sites connected through IPSec Tunnels to one Server is the methodology of the study, using OPNET. Three scenarios have been simulated. The results are compared by measuring the utilization of the server in the three scenarios.

Keywords: VPN, IPSec, Opnet, Security, ESP, AH.

[1]. Henric Johnson , Network Security , Blekinge Institute of Technology, Sweden.
[2]. W.~Diffie and E.~Hellman, {New directions in cryptography}, IEEE Transactions on Information Theory {22} (1976).
[3]. Douglas E.Comer,Computer Networks and Internets
[4]. McDysan. D.(2000),VPN applications Guide
[5]. Behrouz A. Forouzan (2007), Data Communications and Networking
[6]. J. Walrand and P. Varaiya, High-Performance Communication Networks.

Abstract: Security is a big concern for data networks users. Data encryption considered to be one of the best solutions for security issues. There are some standard encryption algorithms that used to encrypt transferred data using encryption keys. DES, 3DES, and AES are common encryption algorithms used in TCP/IP networks. Virtual Private Networking (VPN) is the one of the best security mechanisms that used encrypted virtual tunnels .In this paper the effect of encryption delay on TCP based applications is discussed. Simulation is a major part of this Paper. Increasing the encryption delay and then comparing the effect of that delay on TCP protocol through different scenarios is the methodology of the study, using OPNET. One server supporting HTTP and DB services is used. Four scenarios have been simulated. Results were compared by measuring the effect of applying different encryption delay values to the same network.

Keywords: VPN, DES,3DES, AES,OPNET, IRC, BLOWFISH

[1]. Henric Johnson , Network Security, Blekinge Institute of Technology, Sweden.
[2]. W. ~Diffie and E.~Hellman, {New directions in cryptography}, IEEE Transactions on Information Theory {22} (1976).
[3]. Douglas E.Comer, Computer Networks and Internets
[4]. McDysan. D. (2000), VPN applications Guide
[5]. Behrouz A. Forouzan (2007), Data Communications and Networking
[6]. J. Walrand and P. Varaiya, High-Performance Communication Networks.

Abstract: Network security has become a challenging area, historically only tackled by well qualified and familiar experts. Although more pupils are becoming wired, an increasing number of pupils need to understand basics of security in the network world. The paper is written from the prospective of the basic computer user and information systems administrator, illuminating the concepts needed to go through the hype in the marketplace and understand risks and to deal with them. The replica node attacks are hazardous as they allow the attacker to leverage the compromise of a few nodes exert control over much of the network. Earlier work on replica node recognition relies on set sensor locations and hence do not work in mobile sensor network. The paper proposes sequential probability ratio test for detection of mobile replica node. It provides unique id to the sensor nodes so that an adversary can not disturb the network. The proposed sequential hypothesis testing can result in a fast and effective detection of mobile replica nodes within wireless sensor network.

Keywords: Wireless sensor network (WSN), network security, attack detection, node replication, tamper resistant hardware, sequential probability ratio test (SPRT)

[1]. Parno, Bryan, Adrian Perrig, and Virgil Gligor. "Distributed detection of node replication attacks in sensor networks." In Security and Privacy, 2005 IEEE Symposium on, pp. 49-63. IEEE, 2005.
[2]. Conti, Mauro, Roberto Di Pietro, Luigi Vincenzo Mancini, and Alessandro Mei. "A randomized, efficient, and distributed protocol for the detection of node replication attacks in wireless sensor networks." In Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing, pp. 80-89.ACM, 2007.
[3]. Xing, Kai, Fang Liu, Xiuzhen Cheng, and David Hung-Chang Du. "Real-time detection of clone attacks inwireless sensor networks." In Distributed Computing Systems, 2008.ICDCS'08. The 28th International Conference on, pp. 3-10. IEEE, 2008.
[4]. Wald, Abraham. Sequential analysis. Courier Corporation, 1973.
[5]. Jung, Jaeyeon, Vern Paxson, Arthur W. Berger, and HariBalakrishnan. "Fast portscan detection using sequential hypothesis testing." In Security and Privacy, 2004.Proceedings. 2004 IEEE Symposium on, pp. 211-225. IEEE, 2004.