iosr-JCE   Volume-16 ~ Issue-1 ~ Jan-Feb 2014

Abstract: In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian network.

Keywords: Firewall; IDS; IDS types ; AIDS Techniques and Bayesian concept.

[1] Anomaly Based Intrusion Detection and Artificial Intelligence -Benoît Morel, Carnegie Mellon University, United States.
[2] Intruders Detection System-Tools The Information Assurance Technology Analysis Center (IATAC) - Department of Defense (DoD)
[3] Guide to Intrusion Detection and Prevention Systems –National Institute of Standard & Technologies-US Department
[4] www.Sience Direct.com-Journals
[5] Anomaly-Based Network Intrusion Detection-Computer Science and Telecommunications Faculty, University of Granada,
[6] Deciphering Detection Techniques: Part II Anomaly-Based Intrusion Detection-By Dr.Fengmin Gong, Chief Scientist, McAfee Network Security Technologies Group

Abstract: Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be lossless recovered after embedded data is extracted while protecting the image content's confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility,

[1] T. Kalker and F.M.Willems, "Capacity bounds and code constructionsfor reversible data-hiding," in Proc. 14th Int. Conf. Digital
Signal Processing (DSP2002), 2002, pp. 71–76.
[2] W. Zhang, B. Chen, and N. Yu, "Capacity-approaching codes for reversible data hiding," in Proc 13th Information Hiding
(IH'2011), LNCS 6958, 2011, West, Electronic Imaging, Security and Watermarking of Multimedia Contents, San Jose, CA,
USA, Jan. 2002, vol. 4675, pp. 572–583. pp. 255–269, Springer-Verlag.
[3] W. Zhang, B. Chen, and N. Yu, "Improving various reversible data hiding schemes via optimal codes for binary covers," IEEE
Trans.Image Process., vol. 21, no. 6, pp. 2991–3003, Jun. 2012.
[4] J. Fridrich and M. Goljan, "Lossless data embedding for all image formats," in Proc. SPIE Proc. Photonics

Abstract: The risk of cardio vascular diseases can be identified by measuring the retinal blood vessel. The identification of wrong blood vessel may result in wrong clinical diagnosis. This proposed system addresses the problem of identifying the true vessel by vascular structure segmentation. In this proposed model the segmented vascular structure is modelled as a vessel segment graph and the true vessels are identified by using supervised classifier approach. This paper proposes a post processing step in diagnose cardiovascular diseases which can be identified by tracking a true vessel from the optimal forest in the graph given a set on constraints.

Keywords: Cardiovascular, Graph tracer, Morphology, Optimal forest, Retinal vessel.

[1] Qiangfeng Peter Lau, Mong Li Lee, Wynne Hsu, and Tien Yin Wong., "Simultaneously Identifying All True Vessels fromSegmented Retinal Images". IEEE Transactions on Biomedical Engineering 2013.
[2] T. Y. Wong et al., "Retinal vascular caliber, cardiovascular risk factors, and inflammation: the multi-ethnic study of atherosclerosis (mesa)." Invest Ophthalmol Vis Sci, vol. 47, no. 6, pp. 2341–2350, 2006.
[3] K. McGeechan et al., "Meta-analysis: retinal vessel caliber and risk for coronary heart disease." Ann Intern Med, vol. 151, no. 6, pp. 404–413, 2009.
[4] C. Y.-L. Cheung et al., "Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors," Ophthalmology, vol. 118, pp. 812–818, 2011.
[5] Shilpa Joshi, Dr P.T. Karule., "Retinal Blood Vessel Segmentation." International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 3, March 2012.

Abstract: Classification of Text Document points towards associating one or more predefined categories based on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays an important role in training the system with predefined categories. The importance of Machine learning approach has felt because of which the study has been taken up for text document classification based on the statistical event models available. The aim of this paper is to present the important techniques and methodologies that are employed for text documents classification, at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques.

Keywords: Text mining, Web mining, Documents classification, Information retrieval, Event models.

[1] Liu, H. and Motoda, ., "Feature Extraction, constraction and selection: A Data Mining Perpective.", Boston, Massachusetts(MA): Kluwer Academic Publishers.
[2] Wang, Y., and Wang X.J., " A New Approach to feature selection in Text Classification", Proceedings of 4th International Conference on Machine Learning and Cybernetics, IEEE- 2005, Vol.6, pp. 3814-3819, 2005.
[3] Lee, L.W., and Chen, S.M., "New Methods for Text Categorization Based on a New Feature Selection Method a and New Similarity Measure Between Documents", IEA/AEI,France 2006.
[4] Montanes,E., Ferandez, J., Diaz, I., Combarro, E.F and Ranilla, J., " Measures of Rule Quality for Feature Selection in Text Categorization", 5th international Symposium on Intelligent data analysis , Germeny-2003, Springer-Verlag 2003, Vol2810, pp.589-598, 2003.
[5] Manomaisupat, P., and Abmad k., " Feature Selection for text Categorization Using Self Orgnizing Map", 2nd International Conference on Neural Network and Brain, 2005,IEEE press Vol 3, pp.1875-1880, 2005.
[6] Zi-Qiang Wang, Xia Sun, De-Xian Zhang, Xin Li "An Optimal Svm-Based Text Classification Algorithm" Fifth International Conference on Machine Learning and Cybernetics, Dalian,pp. 13-16 , 2006.

Abstract: Currently, there are two techniques used for large-scale gene-expression profiling; microarray and RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a high-throughput technology for gene expression analysis and becomes an effective tool for biomedical research. Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein microarrays, which enable researchers to investigate the expression state of a large number of genes. Data clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, self-organizing map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms are compared based on their clustering model.

[1] Abu Abbas O. "Comparisons Between Data Clustering Algorithms", The International Arab Journal of Information Technology, Vol.5. No. 3, July 2008.
[2] Song M., Wang H.," Detecting Low Complexity Clusters by Skewness and Kurtosis in Data Stream Clustering" , Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics. Florida: Proceedings of AIM, 2006. pp,1-8.
[3] Smyth G.K. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments", Statistical Applications in Genetics and Molecular Biology, 3, No.1, article 3, 2004.
[4] Venet D., " Matarray: a Matlab toolbox for microarray data", "Bioinformatics Application Notes", Vol.19, No. 5, pp.659-660, 2003.
[5] Chen G., Jaradat S., Banerjeen N., Tanaka T., Ko M., and Zhang M., "Evaluation and Comparison of Clustering Algorithms in Analyzing ES cell Gene Expression Data, "Statistica Sinica", Vol. 12, pp 241-262, 2002.
[6] Yeung K.Y., Haynor D.R., and Ruzzo W.L.,"Validating Clustering for gene expression Data", Bioinformatics, Vol. 17, No.4, pp 309-318, 2001.

Abstract: Image fusion is a promising process in the field of medical image processing, the idea behind is to improve the content of medical image by combining two or more multimodal medical images. In this paper a novel fusion framework based on singular value decomposition - based image fusion algorithm is proposed. SVD is an image adaptive transform, it transforms the matrix of the given image into product USVT, which allows to refactor a digital image into three matrices called tensors. The proposed algorithm picks out informative image patches of source images to constitute the fused image by processing the divided subtensors rather than the whole tensor and a novel sigmoid-function-like coefficient-combining scheme is applied to construct the fused result. Experimental results show that the proposed algorithm is an alternative image fusion approach.

Keywords: Coefficient-combining strategy, image fusion, sigmoid function, singular value decomposition

[1] Junli Liang, Yang He, Ding Liu and Xianju Zeng (2012) 'Image Fusion Using Higher Order Singular Value Decomposition', IEEE
Transactions On Image Processing, Volume 21, No.5, pp 2898-2909.
[2] Amarjot Kaur and Sunil Khullar (2013) 'Image Fusion using HIS, PCA and Wavelet Technique', International Journal of
Computer Science and Communication Engineering, Volume 2, No. 2, pp. 92-94.
[3] V.P.S. Naidu (2011) 'Image Fusion Technique using Multi-resolution Singular Value Decomposition', Defence Science Journal,
Volume 61, No. 5 pp. 479-484.
[4] G. Bergqvist and E.G Larsson (2010) 'The higher-order singular value decomposition Theory and application', IEEE Signal
Processing Magazine, volume 27, No. 3, pp. 151–154.
[5] Li H., S. Manjunath and S. Mitra (1995) 'Multi sensor image fusion using the wavelet transform', International journal of
Graphical Models and Image Processing, volume 57, No.3, pp. 235–245.
[6] Cyn Dwith., Vivek Angoth and Amarjoth Singh (2013) 'Wavelet Based Image Fusion for Detection of Brain Tumor', International
Journal of Image, graphics and Signal Processing, Volume 1, pp.25-31.
[7] J. Canny (1986) 'A Computational Approach to Edge Detection', IEEE Transactions Pattern Analysis Intelligence Volume 8, pp.
679-714.

Abstract: In this paper an effective employee attendance management system using fingerprint is introduced. It is used to managed the attendance of employees in any organization. All organizations and institutions are established to achieve specific objectives or goals. The identification and authentication of employee is very necessary for achieving any objective or goal. To identify and authenticate the identity of an individual employee by their names, ID numbers and signatures only are not enough, because any one can misuse other's identity and this type of problem occur very often. Fingerprint can be applied for recognizing any person, because human fingerprints are unique to each person and can be regarded as some sort of signature, certifying the person's identity. This method of employee identification and authentication will improve the attendance of employees thereby improving security, productivity and skill which will in turn improve the progress of organizations.

Keywords: Employee, Attendance, Fingerprint Matching.

[1]. Hemlata, P. and Pallavi, A. International Journal of Electrical, Electronics and ComputerEngineering 1: 37-40, 2012
[2]. Henry, F. Study of fingerprint identification. McGraw-Hill Books. Inc: USA, 2009
[3]. Francis, G. Fingerprint recognition McGraw-Hill Books. Inc: USA, 2009
[4]. Griaule, B. American Libraries, Journal of fingerprint in United State of America, 354(3):
[5]. 23-25, 2008

Abstract: This paper presents image compression technique using hybrid transform. Concept of hybrid wavelet transform can be extended to generate hybrid transform. In hybrid wavelet transform first few rows represent global features of an image and remaining rows represent local features of an image. In Hybrid wavelet matrix rows contributing to global characteristics can be varied. In the limiting case by taking kronecker product of to orthogonal component transforms, hybrid transform is generated where all rows of transform matrix represent global features and no local features are present. This hybrid transform matrix is then applied on color image. High frequency contents of transformed image are eliminated and only low frequency contents are retained to get compressed image.

[1] Ramanjaneyulu. K, Abdul Rahim.B, FahimuddinShaik, " Effect of Wavelet Based Image Compression Methods on Enhanced Medical Imagery" , International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 3, Issue 9, September 2013, pp.113-117.
[2] N. Ahmed, T. Natarajan and K. R. Rao, "Discrete Cosine Transform", IEEE Transaction Computers, C-23, January 1974, pp. 90-93.
[3] H.B.Kekre, Sudeep D. Thepade, Juhi Jain, NamanAgrawal, "Performance Comparison of IRIS Recognition Techniques using Wavelet Pyramids of Walsh, Haar and Kekre Wavelet Transforms", International Journal of Computer Applications (IJCA), Number 2, Article 4, March 2011, http://www.ijcaonline.org/proceedings/icwet/number2/2070-aca386.
[4] H.B.Kekre, Sudeep D. Thepade, AkshayMaloo, "Face Recognition using Texture Features Extracted from Walshlet Pyramid", ACEEE International Journal on Recent Trends in Engineering and Technology (IJRTET), Volume 5, Issue 1,www.searchdl.org/journal/IJRTET,2010.
[5] H. B. Kekre, Dhirendra Mishra, "Image Retrieval using DST and DST Wavelet Sectorization", International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 2, No. 6, 2011, pp. 91-97.
[6] H.B. Kekre,A.Athawale, P.N.Halarnkar, V. K. Banura,"Performance comparison of DCT and Walsh Transform for Steganography", ACM, 2010.

Abstract: An Ad hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or standard support services. MANET can be defined using unstable network infrastructure, self-organizing network topology and independent node mobility. This becomes obtainable due to their routing techniques; in other terms, routing is a backbone for MANET. However, due to network load routing performance of MANET is degraded thus, some optimization on network routing strategy is required.

[1]. E.M. Belding-Royer, C.-K. Toh, A review of current routing protocols for ad-hoc mobile wireless networks, IEEE Personal Communications Magazine (April 1999) 46–55.
[2]. Anju Sharma, Madhavi Sinha, "A Differential Evaluation Algorithm For Routing Optimization In Mobile Ad Hoc Networks", The Birla Institute Of Technology, Jaipur Mesra, Ranchi Campus Jaipur, Rajasthan, International Journal Of Computer Science And Network (IJCSN),Volume 1, Issue 4, Www.Ijcsn.Org ISSN 2277-5420,Pages 109-115, August 2012.
[3]. C.Mala, A.Anurag Mahesh ,R.Aravind ,R.Rajgopal ,Narendran Rajagopalan , B.Nithya , " Simulated Study Of Qos Multicast Routing Using Genetic Algorithm", National Institute Of Technology,Tiruchirappalli. World Applied Programming, Vol (2), Issue (5),pages 342-348,Issn: 2222-2510©2011 Wap Journal. Www.Waprogramming.Com, May 2012.
[4]. Jihar Doshi, Prahlad Kilambi, "Safar: An Adaptive Bandwidth-Efficient Routing Protocol For Mobile Ad Hoc Networks", Sri Venkateswara College Of Engineering, University Of Madras, Pennalur, Sriperumbudur, 602105Jigar@Doshi.Com, Prahlad@Acm.Org. S. Pierre, M. Barbeau, and E. Kranakis (Eds.): Adhoc-Now 2003, Lncs 2865, Pp. 12–24, 2003._C Springer-Verlag Berlin Heidelberg 2003.

Abstract: Estimation of signal to noise ratio (SNR) of received signal and to transmit the signal effectively for the modern communication system. The performance of existing non-data-aided (NDA) SNR estimation methods are substantially degraded for high level modulation scheme such as M-ary amplitude and phase shift keying (APSK) or quadrature amplitude modulation (QAM).In this paper SNR estimation proposed method which uses zero point auto-correlation of received signal per block and auto/cross- correlation of decision feedback signal in orthogonal frequency division multiplexing (OFDM) system. Proposed method can be studied into two types; Type 1 can estimate SNR by zero point auto-correlation of decision feedback signal based on the second moment property. Type 2 uses both zero point auto-correlation and cross-correlation based on the fourth moment property. In block-by-block reception of OFDM system, these two SNR estimation methods can be possible for the practical implementation due to correlation based the estimation method and they show more stable estimation performance than the earlier SNR estimation methods.
Keywords: SNR estimation, OFDM, QAM, correlation.

[1] A. Stephenne, F. Bellili, S. Affes, "Moment-based SNR estimation over linearly-modulated wireless SIMO channels," Wireless Communications, IEEE Transactions on, vol. 9, no. 2, pp. 714 – 722, Feb. 2010..
[2] David R. Pauluzzi and Norman C. Beaulieu, "Comparison of Four SNR Estimators for QPSK Modulations," IEEE Communication Letters, vol. 4, no. 2, pp. 43.45, Feb. 2000.
[3] D.R. Pauluzzi and N.C. Beaulieu, "A Comparison of SNR Estimation Techniques for the AWGN Channel," IEEE Trans. on Comm., vol. 48, no. 10, pp. 1680-1691, Oct. 2000.
[4] Feng Rice, Bill Cowley, Bill Moran, and Mark Rice, "Cramer-Rao Lower Bounds for QAM Phase and Frequency estimation," IEEE Trans. On Comm, vol. 49, no. 9, Sept. 2001, pp. 1582-1591.
[5] G. Albertazzi, S. Cioni, G. Corazza, M. Neri, R. Pedone, P. Salmi, A. Vanelli-Coralli, and M. Villanti, "On the adaptive DVB-S2 physical layer: Design and performance," IEEE Wireless Commun. Mag., vol. 12, no. 6, pp. 62–68, Dec. 2005

Abstract: Recent advancement in wireless communication has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (such as health, military, home and etc.,). In earlier research, an energy-efficient cluster-based adaptive time-division multiple access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed. In this work, a new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period has been proposed. Simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented to demonstrate the superiority of the E-BMA protocols.
Keywords: Mac protocol, Energy consumption, Contention period.

[1] G.M.Shafiullah, Member, IEEE, Salahuddin A. Azad, and A. B. M. Shawkat Ali, Senior Member, IEEE," Energy Efficient Wireless MAC Protocols for Railway Monitoring Applications" IEEE transactions on intelligent transportation systems, vol. 14, no. 2, june 2013
[2] Bouabdallah, N. Inst. Nat. de Rech. en Inf. et en Autom., Rennes, France Rivero-Angeles, M.E. ; Sericola, B., "Continuous Monitoring Using Event-Driven Reporting for Cluster-Based Wireless Sensor Networks" IEEE transactions on intelligent transportation systems, vol. 58, no. 7, sep 2009.
[3] I.F.Akyildiz,W.Su,Y. Sankarasubramaniam ,and E.Cayirci,"A survey of sensor networks," IEEE Commun. Mag., vol. 40, no. 8, pp. 102–114,Aug. 2002.
[4] V. Raghunathan, C. Schurgers, S. Park, and M. B. Srivastava, "Energy-aware wireless microsensor networks," IEEE Signal Process. Mag., vol. 19, no. 2, pp. 40–50, Mar. 2002.
[5] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Wireless Commun. Trans., vol. 1, no. 4, pp. 660–670, Oct. 2002.

Abstract:In recent years, the analysis and evaluation of students‟ performance and retaining the standard of education is a very important problem in all the educational institutions. The most important goal of the paper is to analyze and evaluate the school students‟ performance by applying data mining classification algorithms in weka tool. The data mining tool has been generally accepted as a decision making tool to facilitate better resource utilization in terms of students‟ performance. The various classification algorithms could be specifically mentioned as J48, Random Forest, Multilayer Perceptron, IB1 and Decision Table are used. The results of such classification model deals with accuracy level, confusion matrices and also the execution time. Therefore conclusion could be reached that the Random Forest performance is better than that of different algorithms.
Keywords: Decision Table, IB1, J48, Multilayer Perceptron, Random Forest

[1]. Al-Radaideh, Q., Al-Shawakfa, E. and Al-Najjar, M. (2006) "Mining Student Data Using Decision Trees‟, The 2006 International Arab Conference on Information Technology (ACIT'2006) – Conference Proceedings.
[2]. Ayesha, S. , Mustafa, T. , Sattar, A. and Khan, I. (2010) "Data Mining Model for Higher Education System‟, European Journal of Scientific Research, vol. 43, no. 1, pp. 24-29.
[3]. Baradwaj, B. and Pal, S. (2011) "Mining Educational Data to Analyze Student s‟ Performance‟, International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, pp. 63-69.
[4]. Chandra, E. and Nandhini, K. (2010) "Knowledge Mining from Student Data‟, European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163.
[5]. El-Halees, A. (2008) "Mining Students Data to Analyze Learning Behavior: A Case Study‟, The 2008 international Arab Conference of Information Technology (ACIT2008) – Conference Proceedings, University of Sfax, Tunisia, Dec 15- 18.

Abstract: Steganography is a technique which used for securing the secret information from the illegal activity. Steganalysis is technique of finding the hidden text from the stego image. A Steganalysis based on the DCT value of the image is proposed in this paper. Steganography F5 algorithm is greater secure than other algorithm. In this paper survey on Steganalysis algorithm for attacking on F5 steganography algorithm is presented. When embedding rate is decreased from 10% to 5% then its accuracy is decreased that needs to improve and also need to decrease the processing time for that algorithm. Proposed techniques also discussed in this paper.
Keywords: Digital image, information hiding, Steganalysis, Steganography, f5, histogram, dct.

[1] Bin LiA , Junhui He, Jiwu Huang, Yun Qing Shi "A Survey on Image Steganography and Steganalysis"Journal of Information Hiding and Multimedia Signal Processing Volume 2, Number 2, April 2011.
[2] Hatim aboalsamh, hassan mathkour, sami dokheekh, mona mursi, ghazyas- sassa "An Improved Steganalysis Approach for Breaking the F5 Algorithm "WSEAS TRANSACTIONS on COMPUTERS , Issue 9, Volume 7, September 2008,pp1447-1456.
[3] Mingwei TANG, Mingyu FAN, Wen SONG, YajunDU "A steganalysis of information hiding for f5" Journal of Computational Information Systems6:vol 1(2010) pp. 55-62.
[4] Jessica Fridrich, Miroslav Goljan, Dorin Hogea "Steganalysis of JPEG Images: Breaking the F5 Algorithm" Springer Berlin Heidelberg , volume 8 ,2003, pp 310-323.
[5] Mohit Kr. Srivastava , Sharad Kr. Gupta, Sushil Kushwaha, Brishket S. Tripathi "Steganalysis Of Lsb Insertion Method In Uncommpressed Images Using Matlab" Available online from: http://www.tutorialspoint.com/white-papers/124.pdf.

Abstract: Peer to Peer communication has become very popular these days .This popularity and increase in P2P traffic has given birth to many internet traffic management problems for service providers. One of these problems is high download traffic of P2P file sharing application for long network distances ,as current applications do not pay attention to network topology while selecting peers for sharing data .To reduce this trans network traffic various solutions have been suggested but all of them lead to compromises for upload rate, or file availability ,or external infrastructure on the internet to support the solution. This papers suggest a modified version of Adaptive search radius algorithm for BitTorrent to solve the problem , at minimum compromise to efficiency of file sharing applications .This papers shows how BitTorrent protocol react to modified adaptive search radius , as it is the most favored application for peer to peer file sharing.
Key words: P2P, Peer to Peer, Adaptive Search Algorithm, BitTorrent.

[1]. IPOQUE, "Internet Study 2007: The Impact of P2P File Sharing, Voice over IP, Skype, Joost, Instant messaging, One -Click Hosting and Media Streaming such as YouTube on the Internet," www.ipoque.com/resources/internetstudies/internet-study-2007, February 16, 2009.
[2]. R. Bindal, P. Cao, W. Chan, J. Medval, G. Suwala, T. Bates, A. Zhangan," Improving traffic locality in BitTorrent via biased neighbor selection," in: Proceedings of the 26th International Conference on Distributed Computing Systems (ICDCS 2006), Lisboa, Portugal, 2006.
[3]. S. Ren, E. Tan, T. Luo, L. Guo, S. Chen, X. Zhang, "TopBT: a topology aware and infrastructure-independent BitTorrent client," in: Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM 2010), San Diego, USA, 2010.
[4]. L. Sheng, H. Wen, "Nearby neighbor selection in p2p systems to localize traffic," in: Proceedings of the Fourth International Conference on Internet and Web Applications and Services (ICIW 2009), Venice/Mestre, Italy, 2009.
[5]. Wei LI, Shanzhi CHEN, Tao YU, "UTAPS: An Underlying Topology-aware Peer Selection Algorithm in BitTorrent," in: Proceedings of 22nd International Conference on Advanced Information Networking and Applications,(ICAINA 2008)Japan,2008.