Abstract: Wireless sensor network has many small sensor nodes that work in collaborative manner to achieve a specific task. But it is deployed in unattended environment and that is why it is prone to attacks. These attacks mainly fall into two categories that is application dependent and application independent .In this paper the focus is on the node replication attack which falls under application independent attacks. In this paper a survey has been done related to node replication attack and existing techniques for solving this issue has been studied. The paper mainly focuses on the types of attacks on wireless sensor network and the two techniques centralized and distributed detection for detection of the node replication attack. Defending against this node replication attack is recently become a research topic in the security of wireless sensor network. The applications and advantages of centralized detection and distributed detection and their respective limitations has been studied.

Keywords: Wireless sensor network, node replication attack, centralized detection, distributed detection, adversary.

[1] M. Bawa, H. Garcia-Molina, A. Gionis, and R.Motwani. Estimating aggregates on a peer-to-peer network. Technical report, Stanford University, 2003.
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Abstract: The broad significance of Wireless Sensor Networks is in most emergency and disaster rescue domain. The routing process is the main challenges in the wireless sensor network due to lack of physical links. The objective of routing is to find optimum path which is used to transferring packets from source node to destination node. Routing should generate feasible routes between nodes and send traffic along the selected path and also achieve high performance. This paper presents a nearest adjacent node scheme based on shortest path routing algorithm. It is plays an important role in energy conservation. It finds the best location of nearest adjacent nodes by involving the least number of nodes in transmission of data and set large number of nodes to sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and enhance the life of the network.

Keywords: Energy Based Routing (EBR), LEACH, Nearest Adjacent Node Scheme, Shortest Path Routing Algorithm, WSNs.

[1] S Cicerone, G D'Angelo, G D.Stefano & D Frigioni "Partially dynamic efficient algorithms for distributed shortest paths" in Elsevier Journal of Theoretical Computer Science 411 (2010)
[2] M A. Youssef, M F. Younis, and K A. Arisha "A Constrained Shortest-Path Energy-Aware Routing Algorithm for Wireless Sensor Networks" in Honeywell International Inc Research Lab in 2009.
[3] Anfeng Liu & Chao Zhang "Secure and Energy-Efficient Disjoint Multi-Path Routing for WSNs" in IEEE 2011
[4] J Gao, Q Zhao, W Ren, A Swami "Dynamic Shortest Path Algorithms for Hyper graphs" in Army Research Laboratory Network Science CTA under Cooperative Agreement W911NF-09-2-0053.
[5] S Yoon, R Dutta, M L. Sichitiu "Power aware routing algorithms for wireless sensor networks" in North Carolina State University Raleigh, NC 27695-7911
[6] A Liuyz, Z Zhengz, C Zhangy, Z Cheny, and X Shenz, "Secure and Energy-Efficient Disjoint Multi-Path Routing for WSNs" in IEEE 2011.
[7] M A. Youssef, M F. Younis, K A. Arisha "A Constrained Shortest-Path Energy-Aware Routing Algorithm for Wireless Sensor Networks" in Honeywell International Inc Dec 2011.
[8] S K .Singh, M P Singh, and D K Singh "Routing Protocols in Wireless Sensor Networks –A Survey" in IJCSES Vol.1, No.2, November 2010
[9] A. Rajeswari, P. T. Kalaivaani " A Novel Energy Efficient Routing Protocols for Wireless Sensor Networks Using Spatial Correlation Based Collaborative Medium Access Control Combined with Hybrid MAC Network Protocols and Algorithms" ISSN 1943-3581 2011, Vol. 3, No. 4
[10] Z Bilgin B Khan "A Dynamic Route Optimization Mechanism for AODV in MANETs" in IEEE 2010.

Abstract: The Paper Entitle "Aisha Email System" deals with identifying the clients to send and receive mail with the same login. This utility will allow multiple clients to login under the same login name and still have personalized mail information, enabling them to send and receive mails. Each user willing to avail the services offered by the mail server application should exist as a user before he can send or receive mails. This is made possible by prompting each user to enter his user-id and password before he can send or view his mails. This Paper has Inbox, compose and address list. E-mail is one of the most common and reliable methods of communication for both personal and business purposes. It also plays an important role in each and every Web site. This role will be in the type of automated e-mails from the server after posting information from a form.

[1] Albert Levi and Mahmut Özcan(2012)Practical and Secure E-Mail System (PractiSES) BBXX0937X37JJ37
[2] J.S. Allen(2011) Mailing System 979-0; 979-1
[3] M.S. Sanga(2012) Post by Email ISB/0001837X373/09.

Abstract: Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another. On a basic level, MT performs simple substitution of words in one natural language for words in another, but that alone usually cannot produce a good translation of a text, because recognition of whole phrases and their closest counterparts in the target language is needed. The paper focuses on Example Based Machine Translation (EBMT) system that translates sentences from English to Hindi. Development of a machine translation (MT) system typically demands a large volume of computational resources. For example, rule based MT systems require extraction of syntactic and semantic knowledge in the form of rules, statistics-based MT systems require huge parallel corpus containing sentences in the source languages and their translations in target language. Requirement of such computational resources is much less in respect of EBMT. This makes development of EBMT systems for English to Hindi translation feasible, where availability of large-scale computational resources is still scarce. Example based machine translation relies on the database for its translation. The frequency of word occurrence is important for translation.
Keywords: Machine Translation, Problems, Process of MT, Approaches.

[1.] Hutchins W. John and Harold L. Somers (1992). An Introduction to Machine Translation. London: Academic Press.
[2.] Hutchins and Lovtsky, in press.
[3.] Hutchins, J. 1986. Machine Translation:Past, Present, Future, Ellis Horwood/Wiley, Chichester/New York.
[4.] Sergei Nirenburg and YorickWilks, Machine Translation
[5.] D. D. Rao, "Machine Translation A Gentle Introduction", RESONANCE, July 1998.
[6.] "Statistical machine translation", [Online]. Available, http://en.wikipedia.org/wiki/Statistical_machine_translation
[7.] IndranilSaha et.al. (2004). Example-Based Technique for Disambiguating Phrasal Verbs in English to Hindi Translation. Technical Report KBCS Division CDAC Mumbai.

Abstract: In theoretical computer science and mathematics , the theory of computation is the branch that deals with how efficiently problems can be solved on a model of computation, using an algorithm. The field is divided into three major branches: automata theory, computability theory, and computational complexity theory. In order to perform a rigorous study of computation, computer scientists work with a mathematical abstraction of computers called a model of computation. There are several models in use such as Lambda calculus, Combinatory logic, mu-recursive functions , but the most commonly examined is the Turing machine. Computer scientists study the Turing machine because it is simple to formulate, can be analyzed and used to prove results, and because it represents what many consider the most powerful possible "reasonable" model of computation . It might seem that the potentially infinite memory capacity is an unrealizable attribute, but any decidable problem solved by a Turing machine will always require only a finite amount of memory. So in principle, any problem that can be solved (decided) by a Turing machine can be solved by a computer that has a bounded amount of memory.

[1]. vars Peterson, 1988, The Mathematical Tourist: Snapshots of Modern Mathematics, W. H. Freeman and Company, New York.

[2]. Martin Davis editor, 1965, The Undecidable: Basic Papers on Undecidable Propositons, Unsolvable Problems and Computable Functions, Raven Press, New York, no ISBN, no card catalog number.

[3]. Alan Turing, 1937, On Computable Numbers, with an Application to the Entscheidungsproblem, pp. 116ff, with brief commentary by Davis on page 115.

[4]. Alan Turing, 1937, On Computable Numbers, with an Application to the Entscheidungsproblem. A Correction,

Abstract: Classification is one of the most important task in application areas of artificial neural networks (ANN).Training neural networks is a complex task in the supervised learning field of research. The main difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training function for the classification task. We compared the performances of three types of training algorithms in feed forward neural network for brain hematoma classification. In this work we have selected Gradient Descent based backpropagation, Gradient Descent with momentum, Resilence backpropogation algorithms. Under conjugate based algorithms, Scaled Conjugate back propagation, Conjugate Gradient backpropagation with Polak-Riebreupdates(CGP) and Conjugate Gradient backpropagation with Fletcher-Reeves updates (CGF).The last category is Quasi Newton based algorithm, under this BFGS, Levenberg-Marquardt algorithms are selected. Proposed work compared training algorithm on the basis of mean square error, accuracy, rate of convergence and correctness of the classification. Our conclusion about the training functions is based on the simulation results.
Keywords: Artificial Neural Network, Back propagation, Gradient Descent, Levenberg-Marquardt.

[1] Atam P. Dhawan, H. K. Huang, DaeShik Kim, Principles and advanced methods in medical imaging and image analysis(World Scientific, 2008).
[2] L. Fu., Neural Networks in Computer Intelligence (Tata McGraw-Hill, 2003).
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[5] M. K. S. Alsmadi, K. B. Omar, S. A. Noah, "Back propagation algorithm: The best algorithm among the multi-layer perceptron algorithm", International Journal of Computer Science and Network Security, vol., 9(4), 2009, pp. 378 – 383.
[6] Bhavna Sharma, Prof K. Venugopalan, "Performance comparison of standard segmentation techniques for brain CT images", International Journal of Computer Engineering and Technology (IJCET), vol. 3(1), Jan-June 2012, pp.126-134.
[7] Bhavna Sharma, Prof K. Venugopalan, "Classification of hematomas in brain CT images using neural network", Proceedings of IEEE Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT-2014) (Accepted).
[8] S. Ali and K. A. Smith, "On learning algorithm selection for classification",Applied Soft Computing, (6), 2006,pp.119–138.
[9] M. Beale, M. Hagan, H. Demut, Neural Network Toolbox User's Guide, 2010.
[10] A.D. Anastasiadis, G.D. Magoulas, and M.N. Vrahatis, "New globally convergent training scheme based on the resilient propagation algorithm",Neurocomputing, 64, 2005, pp.253–270.

Abstract: The most common method used for authentication is Textual passwords. But textual passwords are in risk to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are helpless to shoulder surfing. To address this problem, text can be combined with images or colors to generate session passwords for authentication. Session passwords can be used only once and every time a new password is generated. In this paper, two techniques are proposed to generate session passwords using text and colors which are resistant to shoulder surfing. These methods are suitable for Personal Digital Assistants.
Keywords: Authentication; dictionary attack; shoulder surfing; session passwords; pair-based authentication scheme; hybrid textual authentication scheme; draw-a- secret.

[1] R. Dhamija, and A. Perrig. "DéjàVu: A User Study Using Images for Authentication". In 9th USENIX Security Symposium, 2000.
[2] H. Zhao and X. Li, "S3PAS: A Scalable Shoulder-Surfing Resistant Textual-Graphical Password Authentication Scheme," in 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW 07), vol. 2. Canada, 2007, pp. 467-472.
[3] Haichang Gao, Zhongjie Ren, Xiuling Chang, Xiyang Liu Uwe Aickelin, "A New Graphical Password Scheme Resistant to Shoulder-Surfing.
[4] M Sreelatha, M Shashi, M Anirudh, MD Sultan Ahamer,V Manoj Kumar "Authentication Schemes for Session Passwords using Color and Images", International Journal of Network Security & Its Applications (IJNSA),Vol.3, No.3,May2011.
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[7] Real User Corporation: Passfaces. www.passfaces.com.
[8] W. Jansen, "Authenticating Mobile Device User through Image Selection," in Data Security, 2004.
[9] W. Jansen, "Authenticating Users on Handheld Devices "in Proceedings of Canadian Information Technology Security Symposium, 2003.
[10] S. Man, D. Hong, and M. Mathews, "A shoulder surfing resistant graphical password scheme," in Proceedings of International conference on security and management. Las Vegas, NV, 2003.

Abstract: The advent of anonymizing networks assured that users could access internet services with complete privacy avoiding any possible hindrance. This arrangement where series of routers form a network, hide the user's IP address from the server. However malfeasance of few malpractitioners has left this system with a loophole where users make use of this anonymity to deface popular websites. Administrators who cannot practically block a user using IP address are forced to shut all possible nodes that lead to exit. Thus deny access to both behaving and non-behavingusers altogether. And so end up blocking users with no compromise to their anonymity. Hence we propose a system which is undogmatic with different servers. Thus we aim at giving the administrator the right to block the malicious user without hindering the anonymity of the rest.
Keywords: anonym zing networks, blacklisting, symmetric cryptography, Tor, pseudonym, nymble ticket, Subnet-based blocking, Rate-limiting, Non-frame ability, Anonymous authentication, backward unlinkability, subjective blacklisting, rate-limited anonymous connections, revocation auditability.

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Abstract: This paper highlights the role of web mining in building a web retrieval system to extract hidden knowledge for the Semantic Web by using association rule mining techniques to find a frequent itemset for the Al-Imam University portal. An experimental method is used in this paper. The proposed system builds an information system in which web (data) mining in Semantic-Web technology is applied using the correlation base association rule algorithm. The steps those are necessary for building ontology for an academic portal by using the OntoStudio tool. Using the OntoPortal system will be a successful way to build a semantic portal using a large itemset as the result of web mining to build and classify ontologies for a semantic portal.
Keywords: web mining, association rule mining, Semantic Web.

[1] Agarwal, R. C., Aggarwal, C. C. & Prasad V. V. V. 2000. A tree projection algorithm for generation of frequent itemsets. Retrieved 8 January 2014, from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.103.9466&rep=rep1&type=pdf
[2] Agrawal, D. & Aggarwal, C. C., On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the Twentieth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (pp. 247–255), 2001. New York, NY: Association for Computing Machinery.
[3] Chakravarthy, A. Mining the Semantic Web. Workshop at the 13th European Conference on Machine Learning (Ecml'02)/6th European Conference on Principles and Practice of Knowledge Discovery in Databases (Pkdd'02), 4(2), 2005.
[4] Berendt, B., Hotho, A., Stumme, G, Semantic Web mining and the representation, analysis, and evolution of web space. In Proceedings of RAWS 2005 Workshop (pp. 1–16). Prague-Točná, Czech Republic.
[5] Chen, M.-S., Jan, J., Yu, P. S., Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883, 1996.
[6] Franklin. 2014, Retrieved from www.ntu.edu.sg/home/ascpfu/future.html
[7] Gartner Group. Magic quadrant for customer data mining. Retrieved from http://dml.cs.byv.edu/~cgc/docs/mldm_tools/Reading/Gartner2007.pdf
[8] Han, J., Kamber, M., Data mining: concepts and techniques. San Francisco, CA: Morgan-Kaufmann Academic Press, 2001.
[9] Hegland, M., Algorithms for association rules. Lecture Notes in Computer Science, 2600, 226–234, 2003.
[10] Jiang, Q., Web usage mining: Process and application. Presentation for CSE 8331, 2003.

Abstract: Process mining methods have in recent years enabled the development of more sophisticated Process models which represent and detect a broader range of student behaviors than was previously possible. This paper summarizes key Process mining perspectives that have supported student modeling efforts, discussing also the specific constructs that have been modeled with the use of educational process mining and key upcoming directions that are needed for educational process mining research to reach its full potential. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs readily available in today's information systems. This paper is designed to give a view on the capabilities of process mining techniques in the context of higher education system which involves and deals with administrative and academic tasks like enrolment of students in a particular case, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheet of students, prediction about students performance, identify the drop outs, and students who need special attention and allow the teacher to provide appropriate advising /counseling and so on.
Keywords: event logs, educational process mining, perspectives.

[1] Minaei-Bidgoli B., Punch W.F., Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System,
GECCO 2003 Conference, Springer-Verlag, Vol2, Chicago, USA; July 2003. pp.2252-2263.
[2] Mostow J., Beck J., Cen H., Cuneo A., Gouvea E., and Heiner C., An educational data mining tool to browse tutor-student
interactions: Time will tell! Proceedings of the Workshop on Educational Data Mining, Pittsburgh, USA; 2005. pp.15-22.
[3] Pimentel E.P., Omar N., Towards a model for organizing and measuring knowledge upgrade in education with data mining, The
2005 IEEE International Conference on Information Reuse and Integration, Las Vegas, USA; August 15-17, 2005
[4] Rodrigues, J.P.C., Barrulas, M. J. (2003), Towards Web-Based Information and Knowledge Management in Higher Education
Institutions, Lecture Notes in Computer Science, Volume 2720, Sep 2003, pp. 188-197
[5] Rusu, L., Breşfelean, V.P., Management prototype for universities. Annals of the Tiberiu Popoviciu Seminar, Supplement:
International Workshop in Collaborative systems,Volume 4, 2006, Mediamira Science Publisher, Cluj-Napoca, Romania, pp. 287-
295
[6] Vanda me J.P., Meskens N., Superby J.F., Predicting Academic Performance by Data Mining Methods, Education Economics,
Volume 15, Issue 4 December 2007 , pp.405 – 419
[7] Van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L. Workflow Mining: Discovering Process Models from Event Logs. IEEE
Transactions on Knowledge and Data. Engineering 16(9), p.1128–1142, 2004.
[8] Vasile Paul Bresfelean Data Mining Applications in Higher Education and Academic intelligence Management 27. June 2008
Online at http://mpra.ub.uni-muenchen.de/21235/ MPRA Paper No. 21235, posted 13. March 2010 10:55 UTC
[9] Rozinat, A., van der Aalst, W.M.P. Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems
33(1), p. 64-95.
[10] Vladimir Rubin, Christian W. G¨unther1, Wil M.P. van der Aalst, Ekkart Kindler, Boudewijn F. van Dongen, and Wilhelm Sch¨afer
Process Mining framework for Software Processes.

Abstract: Neural networks are an extremely powerful tool for data mining. They are especially useful in cases involving data classification where it is difficult to establish a pattern in the search space. In an era when artificial intelligence is increasingly being utilised in industrial and medical applications throughout the world, it is becoming evident that this is an emerging trend. This paper explores the idea of artificial intelligence by employing the use of a feed-forward neural network with two process layers to determine the concentration of ammonia in exhaled human breath. The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. Kidney failure is one diesease which is identified by an extremely high ammonia content in human breath. This disease is as a result of the kidneys' inability to process the body's liquid waste. The result of this is the release of urea throughout the body which is dissipated in the form of ammonia through oral breath. The neural simulation is carried out using NeuroSolutions version 5 software. The neural network correctly identified the concentration of oral ammonia as an indication of kidney failure with an accuracy of 85%.
Keywords: ammonia, BUN, kidney failure, metal oxide semiconductor, NeuroSolutions, neural network.

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Abstract: Cloud computing is the use of computing resources (hardware and software) that are delivered as a service over a network (typically the Internet). It does offer great level of flexibility but this advantage comes with a drawback. With increase in sharing of data over web there is an increase in possibility of data being subjected to malicious attacks. Attacker/Provider can extract sensitive information by analyzing the client data over a long period of time. Hence the privacy and security of the user's data is compromised. In this paper we propose an efficient distributed architecture to mitigate the risks.
Keywords: Cloud Computing, Distributed Architecture, Malicious Attacks, Security Breaches, Unauthorized access.

[1] Introduction to Cloud Computing Architecture by Sun Microsystems,Inc., June 2009
Journal Papers:
[2] Himel Dev, Tanmoy Sen, Madhusudan Basak and Mohammed Eunus Ali, "An Approach to Protect the Privacy of Cloud Data from Data Mining Based Attacks.", sccompanion, pp.1106-1115, 2012 SC Companion
[3] Anup Mathew, The Institute for Computing, Information and Cognitive Systems (ICICS), University of British Columbia,"Survey Paper on Security & Privacy Issues in Cloud Storage Systems", EECE 571B, TERM SURVEY PAPER, APRIL 2012.
[4] Jiawei Han, "Data Mining Techniques", SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Books:
[5] Jiawei Han and Micheline Kamber, Data Mining concepts and Techniques (San Francisco, CA: Morgan Kaufmann, 2006).
[6] William Ryan, Wouter De Kort, Shane Milton , Developing Windows Azure and Web Services, Microsoft Press; 1 edition (14 November 2013)
[7] Siani Pearson, "Taking Account of Privacy when Designing Cloud Computing Services", Software Engineering Challenges of Cloud Computing, 2009. CLOUD '09. ICSE Workshop
Web Sources:
[8] Frequent Itemsets- The Stanford University InfoLab ,http://infolab.stanford.edu/~ullman/mmds/ch6.pdf
[9] Introducing Windows Azure-http://www.windowsazure.com/EN-US/develop/net/fundamentals/intro-to-windows-azure/
[10] J. Salmon, "Clouded in uncertainty – the legal pitfalls of cloud computing", Computing, 24 Sept 2008.

Abstract: Distributed hash tables (DHTs) is an extremely attractive study theme during the part of P2P networks; such networks be fetching especially admired in functions similar to file sharing. The idea of the Distributed Hash Table is given that the technique to explore the resources (especially files) within a P2P network. A DHT protocol usually affords a solitary task to the P2P function: afford a key and find out the node (or may be nodes) which is responsible for such key [1][3]. Each and every one function (such as really recover the resource or storing the resource on the node afford for it) is offered by superior levels of the P2P function. In such article our objective is to discover the security measures and determine them on accessible routing procedures of such networks. The Chord [4] (a DHT protocol) is selected as the objective approach for a variety of reasons it resolve be enclosed in this paper.
Keywords: routing protocol, peer to peer networks, distributed hash table.

[1] Heinbockel, W., and Kwon, M.: Phyllo: A peer-to-peer overlay security framework. The First Workshop on Secure Network Protocols (NPSec), Boston, MA (2005)
[2] Ratnasamy, S., Francis, P., Handley, M., Karp, R., Shenker, S.: A scalable content addressable network. In: Proc. ACM SIGCOMM'01, San Diego, CA (2001)
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[4] Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for Internet applications. In: Proc. ACM SIGCOMM'01, San Diego, California (2001)
[5] Wallach, D.: A survey of peer-to-peer security issues, International Symposium on Software Security, Tokyo, Japan (2002).

Abstract: Based on mobile nature in MANET, there is no doubt that all routing protocols have some route errors. Usually, routing protocols try to recover a route after a route error has been happened on an exact route to destination. This kind of route recovery could make more packet loss and also more time waste. This project adds Triangular Fuzzy Numbers to prediction of route errors and make route recovery as parallel of packet sending. Indeed, any node in this project uses a route error counter and an error prediction based on Fuzzy algorithm to recover its routes before an exact route error on its route discovery has been happened.

[1] Abdullah, J., (2010), Multiobjectives GA-Based QOS Routing Protocol for Mobile Ad Hoc Network, International Journal of Grid and Distributed Computing, 3(4), 57-68.
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Abstract: To improve network security different steps has been taken as size and importance of the network has increases day by day. Then chances of a network attacks increases Network is mainly attacked by some intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions.

[1] K. Kendall, A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems, Massachusetts Institute of
Technology Master's Thesis, 1998.
[2] D. Marchette, Computer Intrusion Detection and Network Monitoring, A Statistical Viewpoint. New York, Springer, 2001.
[3] J. Mirkovic, G. Prier and P. Reiher, Attacking DDoS at the Source, 10th IEEE International Conference on Network Protocols,
November 2002
[4] C. Cheng, H.T. Kung and K. Tan, Use of Spectral Analysis in Defense Against DoS Attacks, In Proceedings of the IEEE
GLOBECOM , Taipei, Taiwan, 2002
[5] H. Burch and B. Cheswick, Tracing Anonymous Packets to Their Approximate Source, In Proceedings of the USENIX Large
Installation Systems Administration Conference, New Orleans, LA, 319-327, December 2000.
[6] A.D. Keromytis, V. Misra and D. Rubenstein, SoS: Secure Overlay Services, In Proceedings of the ACM SIGCOMM Conference,
Pittsburgh, PA, 61-72, August 2002

Abstract: Anomaly detection has been an important research topic in data mining and machine learning. Many real-world applications such as intrusion or credit card fraud detection require an effective and efficient framework to identify deviated data instances. However, most anomaly detection methods are typically implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing computation and memory requirements. In this paper, we propose multidimensional reduction principal component analysis (MdrPCA) algorithm to address this problem, and we aim at detecting the presence of outliers from a large amount of data via an online updating technique.

[1] D.M. Hawkins, Identification of Outliers. Chapman and Hall, 1980.
[2] M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, "LOF: Identifying Density-Based Local Outliers," Proc. ACM SIGMOD Int'l
Conf. Management of Data, 2000.
[3] V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, pp. 15:1-
15:58, 2009.
[4] L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A.D. Joseph, and N. Taft, "In-Network Pca and Anomaly Detection," Proc.
Advances in Neural Information Processing Systems 19, 2007.
[5] H.-P. Kriegel, M. Schubert, and A. Zimek, "Angle-Based Outlier Detection in High-Dimensional Data," Proc. 14th ACM SIGKDD
Int'l Conf. Knowledge Discovery and data Mining, 2008.