iosr-JCE   Volume-10 ~ Issue-3

Abstract: Typically the traffic through the network is heterogeneous and it flows from multiple utilities and applications Considering todays threats in network there is yet not a single solution to solve all the issues because the traditional methods of port-based and payload-based with machine learning algorithm suffers from dynamic ports and encrypted application.Many international network equipment manufactures like cisco, juniper also working to reduce these issues in the hardware side.Here this paper presents a new approach considering the idea based on SOTC.This method adapts the current approaches with new idea based on service-oriented traffic classification(SOTC) and it can be used as an efficient alternate to existing methods to reduce the false positive and false negative traffic and to reduce computation and memory requirements.By evaluating the results on real traffic it confirm that this method is effective in improving the accuracy of traffic classification considerably,and promise to suits for a large number of applications.Finally, it is also possible to adopt a service database built offline, possibly provided by a third party and modeled after the signature database of antivirus programs,which in term reduce the work of training procedure and overfitting of parameters in case of parameteric classifier of supervised traffic classification.

Index Terms—Network operations, traffic classification, security.

[1] Cisco: a network device manufacturer. http://www.cisco.com

[2] J. Bellardo and S. Savage. 802.11 denial-of-service attacks: Real vulnerabilities and practical solutions. In Proceedings of the 11th USENIX Security Symposium, pages 15–28, Washington D.C, USA, 2003.

[3] A.Patwardhan, J.Parker, M.Iorga, A. Joshi, T.Karygiannis and Y.Yesha ―Threshold-based Intrusion Detection in Adhoc Networks and Secure AODV‖Ad Hoc Networks Journal (ADHOCNET), June 2008

[4] S. Sen, O. Spatscheck, D. Wang. Accurate, scalable in-network identification of p2p traffic using application signatures. Proceedings of World Wide Web Conference, pp. 512-521 NY, USA, May 2004.

[5] P. Haffner, S. Sen, O. Spatscheck, D. Wang, D. 2005. ACAS: automated construction of application signatures. In Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, pp. 197-202, Philadelphia, USA, August 2005.

[6] F. Risso, A. Baldini, M. Baldi, P. Monclus, O. Morandi. Lightweight, Session-Based Traffic Classification. Proceedings of the IEEE International Conference on Communications (ICC 2008) - Advances in Networks & Internet Symposium, Beijing, China, May 2008. [7] F. Risso, A. Baldini, F. Bonomi. Extending the NetPDL Language to Support Traffic Classification. In Proceedings of IEEE Globecom 2007, Washington, D.C, USA, November 2007.

[8] G.Varghese, J.A. Fingerhut, F. Bonomi. Detecting Evasion Attacks at High Speeds without Reassembly. Proceedings of ACM SIGCOMM 2006, Pisa, Italy, September 2006.

[9] J. Erman, A. Mahanti, M. Arlitt. Traffic Classification using Clustering Algoritms. Proceedings ACM SIGCOMM Workshop on Mining Network Data (MineNet 06), Pisa, Italy, September 2006.

[10] J. Erman, A. Mahanti, M. Arlitt, C. Williamson. Identifying and Discriminating Between Web and Peer-to-Peer traffic in the Network Core. Proceedings of the 16th International World Wide Web Conference (WWW), pp. 883-892, Banff, Canada, May 2007.

Abstract: Consumer's perception regarding shopping has been changed with the introduction of internet media. Retail industry has witnessed major revolution in the changing technology oriented business scenario of 21st century in India. Internet has shrunk the entire World. The rules of the game in retailing are fast changing with the introduction of Information Technology. The e-Retailing website is the front door of the online store that interacts between the e-retailer and consumers. The electronic retailing (e-Tailing, e-Retailing, internet retailing etc.) is the model of selling of retail goods using electronic media, in particular, the internet. E-Retailing is a subset of e-Commerce (Electronic Commerce). E-Retailing accounts for about 10% of the overall growth of e-Commerce market. The growth in the e-Retailing market is driven by the need to save time by urban India. It is estimated that 2.5 billion internet users, access to internet has played a significant role in growing the business markets. The Internet gives retailers an instrument for: broadening target markets, enhancing consumer relationships, extending product lines, improving cost efficiency, improving consumer communications, and delivering customized offers. Changing demographics (youthful India), changing lifestyles and exposure to the developed markets give a fillip to e-Retailing industry. One can buy anything from stereos to iPod's without stepping out through internet media. E-Retailers serve 24 hours x 7 days in a hassle free manner to consumers. Along with advantages of e-Retailing some major issues are associated with e-Retailing such as lack of personal touch; cyber crime; bargaining is not possible and e-illiteracy among rural India. But with all, we can say that Prospect of e-Retailing market is bright in India. Consumer's cognizance; internet literacy of consumer and wider use of internet with cyber security are some of the noteworthy factors which are vital for the sustainable development and growth of e-Retailing in India.

Key Words: Consumer Satisfaction, e-Retailing, e-Tailing, Information Technology, Online Retailing

[1] Vrechopoulos, A.; Siomkos, G., & Doukidis, G. (2001): ―Internet shopping adoption by Greek consumers‖, European Journal of Innovation Management, Vol. IV, No. 3, pp. 142-152

[2] Srinivasan, S., Anderson, R., & Kishore, P. (2002), ―Customer Loyalty in e-commerce: an Exploration of its Antecedents and Consequences, Journal of Retailing, Vol.LXXVIII, No.1, pp. 41-50.

[3] Zeithaml, V.A. (2002): ―Service excellent in electronic channels‖, Managing Service Quality, Vol. XII, No.3, pp.135-138

[4] Wolfinbarger, M. & Gilly, M (2003) ―etailQ: Dimensionalzing, Measuring and Predicting etail Quality‖, Journal of Retailing, Vol.LXXIX, No.3, pp.183-198.

[5] Mohanty, A.K.& Panda, J. (2008), Retailing in India: Challenges and Opportunities, The Orissa Journal of Commerce, Vol. XXIX, No.2, Bhubaneswar, July, pp. 69-79.

[6] Goswami, Shubham & Mathur, Meera (2011), Retail goes Online- An Indian Perspective‖, IJMT, Volume XIX, Number 2, July - December 2011, pp. 1-11.

[7] Manish, Dwivedi; Kumawat, Mahesh & Verma Sanjeev (2012), ― Online Retailing in India : Opportunities and Challenges‖, International Journal of Engineering and Management Sciences, Vol.III, No.3, December, pp.336-338
[8] [Online] Determinants of Shopper Behaviour in E-Retailing : An Empirical Analysis www.cob.unt.edu/slides/paswan/e-Retailing /India.pdf, [Accessed 1 February, 2013]
[9] [Online] E-RETAILING , www.thehindubusinessline.in/praxis/pr0301/03010440.pdf [Accessed on 1 February, 2013]
[10] [Online etail is retail is etail, trendwatching.com/.../pdf/2012-05%20ETAIL%20EVOLUTION.pdf [Accessed on 2 February, 2013]

Abstract: Micro array technology is one of the important biotechnological means that allows recording the expression levels of thousands of genes simultaneously within a number of different samples. An important application of micro array gene expression data is to classify samples according to their gene expression profiles. The gene expression dataset can be represented by an expression table, where each row corresponds to one particular gene, each column to a sample. The relevance of each attribute (attribute represents the gene expression conversion into numerical values) with respect to the class label and the redundancy between two attributes in terms of mutual information are calculated using supervised similarity measure. The proposed system uses supervised attribute clustering algorithm which determines the relevance of each attribute and growing the cluster around each relevant attribute by adding one attribute after the other. Min-hash algorithm is used to reduce the redundancy between the genes and also reduce the cluster size. The performance of the system can be improved by reducing the redundancy of genes.

Key words: Micro array, Gene expression, Mutual Information, Attribute Clustering, Supervised methods

[1] Kaijun Wang, Jie Zheng, Junying Zhang, Member, IEEE, and Jiyang Dong "Estimating the Number of Clusters via System Evolution for Cluster Analysis of Gene Expression Data" IEEE transactions on information technology in biomedicine, vol. 13, no. 5, September 2009.
[2] Patrick C. H. Ma, Keith C. C. Chan, Xin Yao, Fellow, IEEE, and David K. Y. Chiu "An Evolutionary Clustering Algorithm for Gene Expression Microarray Data Analysis" IEEE trans on Evolutionary Computation, vol. 10, no. 3, June 2006.
[3] L.Wang,F.Chu, and W.Xie,"Accurate Cancer Classification Using Expressions of Very Few Genes," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 1, pp. 40-53, Jan.-Mar. 2007.
[4] Shuanhu Wu, Alan Wee-Chung Liew, Member, IEEE, Hong Yan, Senior Member, IEEE, and Mengsu Yang, "Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning" IEEE Trans on information technology in bio-medical, vol. 8, no. 1, march 2004.
[5] D. JianCluster Analysis for Gene g, C. Tang, and A. Zhang, "Expression Data: A Survey," IEEE Trans. Knowledge and Data Eng., vol. 16, no. 11, pp. 1370-1386, Nov. 2004.
[6] W.-H. Au, K.C.C. Chan, A.K.C. Wong, and Y. Wang, "Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 2, no. 2, pp. 83-101, Apr.-June 2005.
[7] W. Haiying, Z. Huiru, and A. Francisco, "Poisson-Based Self- Organizing Feature Maps and Hierarchical Clustering for Serial Analysis of Gene Expression Data," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 2,pp.163-175,Apr.-June 2007.
[8] J. Li, H. Su, H. Chen, and B.W. Futscher, "Optimal Search-Based Gene Subset Selection for Gene Array Cancer Classification," IEEE Trans. Information Technology in Biomedicine, vol. 11, no. 4, pp. 398-405, July 2007.
[9] P. Maji, "f-Information Measures for Efficient Selection of Discriminative Genes from Microarray Data," IEEE Trans. Biomedical Eng., vol. 56, no. 4
[10] T. Hastie, R. Tibshirani,D. Botstein and brown "Supervised Harvesting of Expression Trees," Genome Biology

Abstract: The article introduced the importance of invasion recognition, as well as the traditional invasion recognition's type and the limitation. Also, according to the invasion recognition's general process and data mining characteristic, it establishes a data mining-based model of network invasion recognition which is designed for its flaw. As a result, the missing report reduces greatly; the examination rate enhances; and network system's security strengthened. Finally, the article lists several hot topics which need to be further studied. Keyword: Invasion recognition; Data Mining; Information Security

[1] Julisch K. Data mining for invasion recognition: A critical review. IBM Research, Zurich Research Laboratory.

[2] E1-Sayed M, Ruiz C, Rundensteiner E A. FSMiner: efficient and incremental mining of frequent sequence patterns in Web logs. ACMWIDM'04, Washington DC, November 2004:12-13.

[3] Lee W, Stolfo S, J Mok K W. Data mining approaches for invasion recognition. Proceedings of the 7th USENIX Security Symposium, 1998.

[4] Cai Y, Clutter D, Pape G, et al. MAID: mining alarming incidents from Data Streams.ACM-SIGMOD Int Conf Management of Data (SIGMOD04), New York: ACM Press, 2004, 919-920.

[5] Chatzigiannakis V, Androutidakis, G, Maglaris B. A distributed invasion recognition prototype using security agents. HP Open View University Association, 2004.

[6] Kumar S, Spafford, E H.A pattern matching model for misuse invasion recognition. Proceedings of the 17th National computer Security Conference, 1994. 1277

[7] Boyer R.Moore J S.A fast string searching algorithm. Communication of the ACM, 1971,20(10):762-772.

[8] Jiawei Han, Micheline Kamber. Data mining concepts and techniques. Beijing: Mechanical industry publishing house 1-23, 70-94,152-168,188-196.

[9] Wayne A. Jansen. Invasion recognition with Mobile Agents Computer Commucications.2002 (25):96-99.

Abstract: Knowledge management (KM) become important for organization to take advantage on the information produced and can be brought to bear on present decision. Software Development (SD) is a process that requires lots of knowledge. Developers must know what should be done while developing software, and what to do when changes occurs and how those changes can affect other modules of the system. Knowledge management system (KMS) can support the processes of knowledge creation, storage or retrieval, transfer and application. KMS in SD could help the organization to make tacit knowledge into explicit and therefore decrease the dependency on employees' cognition. This paper is to apply KMS architecture in SD environment to analyze the problems faced by software developers during the software development process and how various companies apply the tools of KM to improve the work situation for software developers and managers.

[1] T. Dingsoyr, " An evaluation of Research on Experience Factory ," Proc. Of the workshop on Learning Software Organisations at the International conference on Product- Focused Software Process Improvement, 2000.

[2] I. Sommerville, Software Engineering, Addison Wesley, 1996.

[3] "Chaos", Dennis, Massachusets, The Standish Group Report 1995.

[4] B. Joy and K. Kennedy, "Information Technology Research : Investing in our Future ," Report from the President'sInformation Technology Advisory Committee, 1999.

[5] R. L. Glass, "Talk About a Software Crisis – Not!" The Journal of Systems and Software, 2000.

[6] T. Dingsoyr, " A lifecycle process for experience databases," Proc. Of the ICCBR'99 workshops : Challenges for case-based reasoning :", 1999.
[7] R. Bergemann and M. Goker, "Knowledge Management of Software Engineering Lessons Learned," Proc. Of the 10th International conference on Knowledge Engineering, 1998.

Abstract: Malware detection or antivirus software has been recently provided as a service in the cloud. A cloud antivirus provider hosts a number of virtual machines each running the same or different antivirus engines on potentially different sets of workloads (files). From the provider's perspective, the problem of optimally allocating physical resources to these virtual machines is crucial to the efficiency of the infrastructure. This paper proposes a search-based optimization approach for solving the resource allocation problem in cloud-based antivirus deployments. An elaborate cost model of the file scanning process in antivirus programs is instrumental to the proposed approach. The general architecture is presented and discussed, and a preliminary experimental investigation into the antivirus cost model is described. The cost model depends on many factors, such as total file size, size of code segment, and count and type of embedded files within the executable. However, not a single parameter of these can be reliably used alone to predict file scanning time.

Keywords: Cloud computing, virtualization, antivirus, pattern matching

[1] R.Qi Zhang, Lu Cheng, "Cloud computing: state of the art and research challenges," in J. Internet and Applications. The Brazilian Computer Society 2010, pp. 7-18.

[2] Virtualization [online]. Available at http://www.vmware.com/virtualization/. Last checked on April 14, 2013. [

3] S. K. Shah and N. I. R., "Exploring Reliability of cloud antivirus solution," in New Jersey Institute of Technology.

[4] J. Oberheide, E. Cooke, and F. Jahanian, "CloudAV: N-version antivirus in the network cloud," in Proceedings of the 17th conference on Security Symposium SS‟08, Berkely, CA, USA: USENIX Association, 2008, pp. 91-106.

[5] R. Rose, "Survey of system virtualization techniques," Tech. Rep. 2004.

[6] Clam antivirus [online]. Available at http://www.clamav.net. Last checked on April 14, 2013.

[7] R. S. Boyer and J. S. Moore, "A fast string searching algorithm," Commun. ACM, vol. 20, no. 10, pp. 762-772, Oct. 1977.

[8] A. V. Aho and M. J. Corasick, "Efficient string matching: an aid to bibliographic search," Commun. ACM, vol. 18, no. 6, pp.333-340, Jun. 1975.

[9] Bo-yun Zhang, Jian-ping Yin, Jin-bo Hao, Ding-xing Zhang, and Shu-lin Wang, "Using support vector machine to detect unknown computer viruses," International Journal of Computational Intelligence Research., vol. 2, no. 1, pp. 100-104, 2006.

[10] D. J. Hand, P. Smyth, and H. Mannila, "Principles of Data mining." Cambridge, MA, USA: MIT Press, 2001.

Abstract: In recent investigations related to security issues, biometrics plays an important role in recognition of individuals based on their physiological or behavioral characteristics. Gait as a biometric behavioral trait plays an important role in recent research. Gait is an unobtrusive biometric, is the process of identifying an individual by the manner in which they walk. In this paper we propose a concept of geometric for human gait recognition. We have considered dynamic features and side view of human body for gait recognition. This paper considers three dynamic parameters i.e right hand, left feet and right feet. Then a triangle is formulated between these parameters and computed area for different subjects and store it in database for recognition.

Keywords: Biometrics, Gait recognition, triangle.

[1] Michal Choras,Emerging Methods of biometrics human identification, 2nd International Conference on Innovative Computing, Information and Control, ICICIC '2007, pp365 – 365.

[2] Liang Wang,Weiming Hu, Tieniu Tan,A new attempt to Gait-based Human Identification, the 16th IEEE international conference on pattern recognition ICPR2002, 10 Dec 2002,pp 115 – 118.

[3] Mark S.Nixon,John N.Carter,Advances in automatic gait recognition, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp139 – 144.

[4] Xuelong li, Stephen J.Maybank, Shuicheng Yan,Dacheng Tao and Dong Xu ,Gait components and their applications to Gender recognition, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 2008, pp 145 – 155.

[5] Jasvinder pal singh and Sanjeev jain, Person Identification Based on Gait using Dynamic Body Parameters, IEEE International Conference on Trendz in Information Sciences & Computing (TISC) 2010,pp: 248 – 252 .

[6] Nikolaos V.Boulgouris,Dimitrious Hatzinakos and Konstantinos N.Plataniotis,Gait Recognition: A challenging signal processing technology for biometric identification ,IEEE signal Processing Magazine, November 2005, vol.22,No.6,pp78-90.

[7]. www.cbsr.ia.ac.cn

[8] http://en.wikipedia.org/wiki/Heron's_formula

[9] Dr.B.S.Grewal, higher engineering mathematics, khanna Publishers ed.40th ISBN 9788174091956. [

10] Anil K. Jain, Arun Ross, and Sharath Pankanti,Biometrics: A Tool for Information Security, IEEE Transactions on Information Forensics and Security, June 2006, pp 21-38.

Abstract: In this paper we are emphasizing on developing this project that will help the lecturers to take attendance easily, securely and is less error prone. For this, we are implementing software "Mobile Phone Based Attendance System (MPBAS)" based on Android Technology. We are inspired to work on MPBAS from the existing system E-Beat. MPBAS will help lecturers to take the attendance of students using Smart-phone. Lecturers will login to the phone application, get connected to the server and take attendance using Smart-phone. After taking the attendance in the mobile, lecturers will send it over to the server using GPRS and attendance list will be updated automatically. Lecturers will be able to edit the attendance by login on to the website. Students will be able to view their own attendance as well as curriculum details. To reduce the chances of fake attendance, the project would include Location detection using GPS. Also email would be send to the students by the lecturers, notifying them of their regular activities. Thus, the project will ease the workload of the lecturers by providing them the platform wherein they will be able to take, manage, update and see the attendance of the students with efficiency. Also the students will remain updated regarding their current status and thus can improve the performance by increasing their cumulative attendance in time.

Keywords: Android, Authorization, Authentication, Smart-phone, GPRS, GPS.

[1]. Nirmalya Kar and Ashim Saha ; Study of implementing automated attendance system using face recognition technique; International Journal of computer and communication engineering, Vol. 1, No. 2, July 2012 :
[2]. Zatin Singhal and Rajneesh Kumar Gujral ; Anytime Anywhere- Remote Monitoring of Attendance System based on RFID using GSM Network ; International Journal of Computer Applications (0975 – 8887) Volume 39– No.3, February 2012 37
[3]. M. Man, L.Y. Kyng 2007 "Utilizing MYKAD Touch N Go features for Student Attendance System (TITO)". Proceeding of 1st International Malaysian Educational Technology Convention 2007, Johor Bahru, Malaysia, pp.114-120.
[4]. Sidi, Jonathan, N Syahrul, Junaini, and Lau, S. Ling. 2007 ISAMS: Tracking Student Attendance using Interactive Student Attendance management System. Third Malaysian Software Engineering Conference (MySEC‟07), Selangor, Malaysia, pp. 1-5.
[5]. Z. Yongqiang, L. Ji 2006 "The Design of Wireless Fingerprint Attendance System" International Conference on Communication Technology, ICCT '06, Handan, Hebei, China, 27-30 November 2006, pp. 1-4.
[6]. Professional Android 2 Application Development, Reto Meier , ISBN: 978-0-470-56552-0, Paperback, 576 pages, March 2010.
[7]. The Busy Coder's Guide to Android Development, Jul 2008: Version 1.0,ISBN: 978-0-9816780-0-9.

Abstract: THE CURRENT SYSTEM OF MOBILE BANKING IN NIGERIA CONSISTS OF TWO WAY AUTHENTICATION (USERNAME AND PASSWORD) WHICH CAN BE FORGOTTEN OR STOLEN. THIS PAPER PROPOSES A SECURE AUTHENTICATION FOR MOBILE BANKING USING FACIAL RECOGNITION TO THE EFFECT OF IMPROVING ON THE EXISTING SYSTEM, AND THEREBY SUPPORTING THE ACTUALIZATION OF CASHLESS SOCIETY. AN OVERVIEW AND LIMITATIONS OF THE CURRENT SYSTEM ARE PRESENTED. THE HIGH LEVEL DESIGNS OF THE PROPOSED SYSTEM ARE THEN PRESENTED. THE SYSTEM IS THEN SIMULATED USING JAVA PROGRAMMING LANGUAGE AND TESTED USING SIMULATED DATABASES OF NIGERIA COMMUNICATION COMMISSIONS (NCC) AND THE FACILITATING BANK. THE SYSTEM WAS FOUND TO PERFORM WITH A MAXIMUM RESPONSE TIME OF SEVEN MINUTES, AND FALSE ACCEPTANCE RATE (FAR) OF 3%. COMBINING THIS SYSTEM WITH ONE OR MORE OTHER FORMS OF BIOMETRIC TECHNOLOGIES SUCH AS FINGER VEIN, IRIS AMONG OTHERS WILL NO DOUBT GIVE A FRAUD PROOF PLATFORM FOR MOBILE PHONE BANKING.

KEYWORDS: MOBILE BANKING, SECURITY, AUTHENTICATION, CASHLESS SOCIETY

[1] Sun, Z. W. (2009). Design And Implementation Of Mobile Payment Based On Multi-Interface Of Mobile Terminal. Zhejiang.

[2] Nagel, B. (2007). Mobile Authentication Marries Security With Covenience.

[3] http://en.wikipedia.org/wiki/Mobile_banking.

[4] Punithavathi, R. and Duraiswamy, K. (2011). Secure Authenticated Mobile Agent Based Mobile Banking System. European Journal of Scientific Research. ISSN 1450-216X Vol.57 No.3 (2011), pp.494-501.

[5] Krutz, R. L. and Vines, R. D. (2003). The CISSP Prep Guide: Gold Edition. Wiley Publishing, Inc., Indianapolis, Indiana.

[6] Capoor. (2006). Electronic Banking Journal.

[7] Gafurov, D., Helkala, K., and Sondrol, T. (2006). Biometric Gait Authentication Using Accelerometer Sensor. Journal of Computers, Vol. 1, No. 7, Oct./Nov. 2006, pp. 51-59.

[8] Povocnicu, A. (2009). Biometric Security for Cell Phones. Informatica Economica, vol. 13, no. 1/2009, pp 57-63.

[9] www2.ncc.gov.ng [

10] The Impact of Mobile Services in Nigeria. Pyramid Research, March 2010.

Abstract:For a particular emotion of the user, the System evaluates songs according to the qualification assessed by two factors which are: song's relevancy to the user's preference, and song's mental influence on the user's feeling. In this proposed system, user's emotion is not input manually by the user, but detected automatically by the machine. In order to do that, user's facial expression data is captured from the webcam and then used as inputs for emotion detecting process. The motivation behind this system is the lack of a context-aware Music Recommendation System where automatically detected user's mood plays the most important role as a contextual key. The need of such system is made obvious by the fact that digital music libraries are constantly expanding, which thus makes it remarkably difficult for listeners to recall a particular song matching their present mood.By training the system to recognize user's emotional state by facial expression, it is made possible for listeners to generate a playlist which suits with their current emotion, and of which songs are rated also by the potentially mental influence on user's emotion.

Keyword: Face recognition, Face detection, PCA, Emotion Extraction and detection, Euclidean Distance.

[1] A Regression Approach to Music Emotion RecognitionYi-Hsuan Yang, Yu-Ching Lin, Ya-Fan Su, and Homer H. Chen, Fellow, IEEE

[2] JOURNAL OF INFORMATION SCIENCE AND ENGINEERING,Facial Expression Classification Using PCA and Hierarchical Radial Basis Function Network, DAW-TUNG LIN, Department of Computer Science and Information Engineering

[3] Principal Component Analysis, A Powerful Scoring Technique, George C. J. Fernandez, University of Nevada - Reno, Reno NV 89557.

[4] Face Recognition: A Literature Survey, W. ZHAO Sarnoff CorporationR. CHELLAPPA, University of Maryland, P. J. PHILLIPS, National Institute of Standards and Technology and A. ROSENFELD,University of Maryland [

5] Face Recognition Algorithms, Proyecto Fin de Carrera, June 16, 2010, Ion Marqu´es

[6] Emotion detection in music, a survey Bram van de Laar

Abstract: Intrusion detection systems play a key role in detecting such malicious activities and enable administrators in securing network systems. Intrusion detection can detect malicious attacks that have penetrated preventative mechanisms such as firewalls, which can help provide damage assessment, response and prosecution support. This paper describes a novel approach using Hidden Markov Models (HMM) to detect Internet attacks. In this paper we describe an intrusion detection system for detection of signature based attack. These attack signatures encompass specific traffic or activity that is based on known intrusive activity. We performed single and multiple HMM model for source separation both on IP and port information of source and destination. This approach reduced the false positive rate and we made this type of source separation as our basic step for building HMM.

Keywords - Intrusion Detection, TCP/IP Packet Analysis, Detection, Hidden Markov Model, algorithm

[1 ] R Rangadurai Karthick, Vipul P. Hattiwale, Balaraman ,Ravindran,Adaptive Network Intrusion Detection System using an Hybrid Approach, 2012.

[2] Jiankun Hu and Xinghuo Yu, Hsiao-Hwa Chen,A Simple and Efficient Hidden Markov Model Scheme for Host-Based Anomaly Intrusion Detection,2009.

[3] Lawrence R. Rabiner, A Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition, Proceedings of the IEEE, 1989.

[4] Wenke Lee and Salvatore J. Stolfo and Philip K. Chan and Eleazar Eskin and Wei Fan and Matthew Miller and Shlomo Hershkop and Junxin Zhang, Real Time Data Mining-based Intrusion Detection, IEEE, 2001.

[5] Ourston, Dirk and Matzner, Sara and Stump,William and Hopkins, Bryan, Applications of Hidden Markov Models to Detecting Multi-stage Network Attacks, HICSS, 2003

Abstract: The self configuring dexterous autonomy of MANET imposes some network challenges constrained to traditional dynamic routing behaviour. So as working with different mobility and traffic patterns with normal management schemes may lead some minor pitfalls to some important network performance parameters and hence can degrade the whole network performance. Here, Our aim is to make some DSR and MAC based cross layer optimizations and testify it on different mobility and traffic scenarios so as to justify the robustness of our proposed improvement.

Keywords – cross- layer,MANET,MAC,optimization

[1] The Monarch Project implementation. http://www.monarch.cs.rice.edu/dsr-impl.html
[2] The Microsoft Research Mesh Connectivity Layer. http://research.microsoft.com/mesh

[3] The Click DSR Router Project. http://pecolab.colorado.edu/DSR.html (2002) The IEEE website. /
[4] OPNET, http://w3.antd.nist.gov/wctg/prd_dsrfiles.html

[5] O Piconet II mobile router, implementing an ad hoc routing protocol. http://piconet.sourceforge.net/thesis/main.html

[6] Akintola, A.& Aderounmu, A. & Owojori, A. & Adigun,M.O.(2006) Performance modelling of UDP over IP-Based wireline and Wireless Networks.

[7] The network simulator. http://www.isi.edu/nsnam/ns/

[8] Bryan‟s ns2 DSR faq.http://www.geocities.com/b_j_hogan/

[9] Nahm, K, & Helmy, A. & Kuo, J. TCP over multihop 802.11 networks: Issues and performance enhancement.

[10] Dube, R. & Rasi, C & Wang, K. & Tripathi, K. (1997) Signal stability-based adaptive routing for ad hoc mobile networks.

Abstract: The overall success of a software project depends on the quality of its software requirements specifications (SRS). Hence, it is very important to get the requirements correct from the onset of the project. This research paper proposes a web-based system to perform SRS quality analysis using Case-Based Reasoning (CBR) and Artificial Neural Network (ANN). CBR is an AI technique that learns and deduces solutions based on past cases or experiences that are stored in a case base. However, when the case base becomes very large, the "Retrieve" phase of CBR becomes very tedious. So, in this research, we use ANN to improve the retrieval phase within the CBR. ANN measures the similarity of the new case against all existing cases in the case base. This results in a more efficient method of performing quality analysis for a given SRS document.

Keywords – Software Requirements Specifications (SRS), Quality Analysis, Case-Based Reasoning (CBR), Artificial Neural Network (ANN), Similarity Measurement

[[1] A. Aamodt, and E. Plaza, Case-based reasoning: foundational issues, methodological variations, and system approaches, AI Communications 7(1), 1994, 39–59.
[2] A.C. Gillies, Software quality: theory and management (London: Chapman & Hall, 1992).
[3] IEEE-Std-830-1998, IEEE Recommended practice for software requirements specification, doi:10.1109/IEEESTD.1998.88286.
[4] H.M. Jani, S.A. Mostafa, Implementing case-based reasoning technique to software requirements specifications quality analysis, International Journal of Advancements in Computing Technology (IJACT), 3(1), 2011, 23-31.
[5] H.M. Jani, Applying case-based reasoning to software requirements specifications quality analysis system, Proc. 2nd International Conference on Software Engineering and Data Mining (SEDM), IEEE/AICIT, 2010, 140-144.
[6] S. Fowler, and V. Stanwick, Web application design handbook: best practices for web-based software (San Francisco, CA: Morgan Kaufmann Publishers, 2004).
[7] A. Leff, J.T. Rayfield, Web application development using the Model/View/Controller design pattern, Proc. 5th IEEE Int. Enterprise Distributed Object Computing Conference, 2001, 118-127.
[8] E. Armengol, S. Ontanon, and E. Plaza, Explaining similarity in CBR, ECCBR 2004 Workshop Proceedings, 2004, 155-164.
[9] T. Warren, L.Z. Zhang, and C. Mount, Similarity measures for retrieval in case-based reasoning systems, Applied Artificial Intelligence, 12(4), 1998, 267–288.
[10] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, (2nd Edition, New York: John Wiley and Sons, 2001).