iosr-JCE   International Conference on Advances in Engineering & Technology – 2014 (ICAET-2014)

Abstract: Frequent link failures are caused in MANET due to node's mobility and use of unreliable wireless channels. Due to this, multipath routing protocols become an important issue. However, the inter-path interference limits the gain of multipath routing in MANET. In this paper, I propose a Node disjoint minimum interference multi-path (ND-MIM) routing protocol for MANETs based on AODV protocol. The main goal of the propose method is to determine all node-disjoint routes from source to destination with minimum routing overhead. When the route is broken, the data is transmitted continuously through other route .Simultaneously in selecting node-disjoint path, the protocol takes also into account the energy and distance of intermediate node in the path for extending the network lifetime.

Keywords- AODV, energy efficient, interference avoidance, MANET, multipath routing, Node-disjoint.

[1] Shunli Ding , Liping Liu: " A node-disjoint multipath routing protocol based on AODV": 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science

[2] Chhagan Lal, V.Laxmi, M.S.Gaur, "A Node-Disjoint Multipath Routing Method based on AODV protocol for MANETs": 2012 26th IEEE International Conference on Advanced Information Networking and Applications.

[3] Yang Wenjing, Xinyu Yang, Guozheng Lu, Wei Yu, "An Interference Avoidance Multipath Routing protocol based on greedy forwarding in MANETS" IEEE International conference on Wireless Communications, Networking and Information Security (WCNIS), June 25-27, 2010. [4] Jailani Kadir, Osman Ghazali, Mohamed Firdhous, Suhaidi Hassan, "Node Selection Based On Energy Consumption in Mobile Ad Hoc Networks": InterNetWorks Research Group, School of Computing, University Utara Malaysia, Malaysia , Sep 15, 2011.

[5] Ashok Kumar, Vinod Kumar, Narottam Chand," Energy Efficient Clustering & Cluster Head Rotation Scheme for Wireless Sensor Networks": International Journal of Advanced Computer Science and Applications, Vol. 3, No. 5.

[6] Xuefei Li, Laurie Cuthbert, "On-demand Node-Disjoint Multipath Routing in Wireless Ad hoc Networks": Department of Electronic Engineering, Queen Mary, University of London [7] Chang-Woo Ahn, Sang-Hwa Chung, Tae-Hun Kim, Su-Young Kang, "A Node-Disjoint Multipath Routing Protocol Based on AODV in Mobile Ad-hoc Networks", International Conference on Information Technology, IEEE, 2010.

[8] Tsung-Chuan Huang, Sheng-Yu Huang and Lung Tang, "AODV-Based Backup Routing Scheme in Mobile Ad Hoc Networks": 2010 International Conference on Communications and Mobile Computing. [9] Zangeneh, S. Mohammadi, "New Multipath Node-Disjoint Routing Based on AODV Protocol": World Academy of Science, Engineering and Technology 76 2011.

Abstract: In this paper, we study Transport Control Protocol (TCP) in-cast congestion control. In cast may severely degrade their performances by increasing response time also we study among TCP throughput, round trip time (RTT) and receive window. Our idea is to design an ICTCP (In cast congestion Control for TCP) scheme at the receiver side. In particular, our method adjusts TCP receive window proactively before packet drops occur. The implementation and techniques demonstrate that we achieve al-most zero timeout and high good put for TCP in cast. In this paper, we discuss a cross layer congestion control technique of TCP. In cast congestion happens in high-bandwidth and low-latency networks when multiple synchronized servers send data to the same receiver in parallel. For many important data-centre applications such as Map Reduce and Search, this many-to-one traffic pattern is common. Hence TCP in cast congestion may severely degrade their performances, e.g., by increasing response time. In this paper, we study TCP in cast in detail by focusing on the relationships between TCP throughputs, round-trip time (RTT). In particular, our method adjusts the TCP receive window proactively before packet loss occurs. The implementation and experiments in our test bed demonstrate that we achieve almost zero timeouts and high good put for TCP in cast.

KEYWORDS: Transport Control Protocol (TCP), Congestion, Data center networks, incast congestion, round-trip time (RTT).

[1] A. Phanishayee, E. Krevat, V. Vasudevan, D. Andersen, G. Ganger, G.Gibson, and S. Seshan, ―Measurement and analysis of TCP throughput collapse in cluster-based storage systems,‖ in Proc. USENIX FAST, 2008, Article no. 12.

[2] V. Vasudevan, A. Phanishayee, H. Shah, E. Krevat, D. Andersen, G. Ganger, G. Gibson, and B.Mueller, ―Safe and effective fine-grained TCP retransmissions for datacenter communication,‖ in Proc. ACMSIGCOMM, 2009, pp. 303–314.

[3] S. Kandula, S. Sengupta, A. Greenberg, P. Patel, and R. Chaiken, ―The nature of data center traffic: Measurements & analysis,‖ in Proc. IMC, 2009, pp. 202–208. [4] J. Dean and S. Ghemawat, ―MapReduce: Simplified data processing on large clusters,‖ in Proc. OSDI, 2004, p. 10. [5] M. Alizadeh, A. Greenberg, D.Maltz, J. Padhye, P. Patel, B.Prabhakar, S. Sengupta, and M. Sridharan, ―Data center TCP (DCTCP),‖ in Proc. SIGCOMM, 2010, pp. 63–74.

[6] D. Nagle, D. Serenyi, and A. Matthews, ―The Panasas ActiveScale storage cluster: Delivering scalable high bandwidth storage,‖ in Proc.SC, 2004, p. 53. 14.

[7] E. Krevat, V. Vasudevan, A. Phanishayee, D. Andersen, G. Ganger, G. Gibson, and S. Seshan, ―On application-level approaches to avoiding TCP throughput collapse in cluster-based storage systems,‖ in Proc.Supercomput., 2007, pp. 1–4. [8] C. Guo, H.Wu,K.Tan,L. Shi,Y.Zhang, and S. Lu, ―DCell:Ascalable and fault tolerant network structure for data centers,‖ in Proc. ACMSIGCOMM, 2008, pp. 75–86

. [9] M. Al-Fares, A. Loukissas, and A. Vahdat, ―A scalable, commodity data center network architecture,‖ in Proc. ACMSIGCOMM, 2008, pp. 63–74.

[10] C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu, ―BCube: A high performance, server-centric network architecture for modular data centers,‖ in Proc. ACM SIGCOMM, 2009, pp. 63–74.

Abstract: Internet is the global system which growing very fast. It is most reliable and efficient one so that the use of internet is increasing in people's day to day life. Because of increasing social networking more and more people interact with each other and share their views, emotions, experiences, feedback and opinion about anything. Feedback is the important part for selling or purchasing any product. But it is very difficult for customer to read thousands of reviews at a time which create confusion. So data mining plays an important role to mine opinion and to summarize all reviews of customer. Most of the existing methods of opinion mining show the customer reviews in the form of positive and negative comments. But it is not efficient for customers because customer will not decide whether to buy a product or not. The proposed approach mines the opinions of customers according to product features. The proposed approach not only gives the method for rating products but also gives rating according to features. This approach also compares the product according to the rating which helps customer to take decision regarding product purchasing.

Keywords -Feature mining, Opinion mining, Sentiment, Sentiment Classification, Summarization

[1] ZHU Jian , XU Chen, WANG Han-shi, Sentiment classification using the theory of ANNs, The Journal of China Universities of Posts and Telecommunications, July 2010, 17(Suppl.): 58–62 .
[2] Long-Sheng Chen , Cheng-Hsiang Liu, Hui-Ju Chiu , A neural network based approach for sentiment classification in the blogosphere, Journal of Informatics 5 (2011) 313–322.
[3] Ana-Maria P., Oren E., Extracting Product Features and Opinions from Reviews. In Proceeding of HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing Pages 339-346.
[4] Nozomi K., Kentaro I., Yuji M. Extracting Aspect Evaluation and Aspect-of Relations in Opinion Mining. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLPCoNLL).
[5] Minqing H., Bing L. Mining Opinion Features in Customer Reviews. In Proceedings of AAAI'04 Proceedings of the 19th national conference on Artificial intelligence Pages 755-760.
[6] Nikolay A., Anindya G., Panagiotis G. I. Show me the Money! Deriving the Pricing Power of Product Features by Mining Consumer Reviews. In Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2007).
[7] Satoshi M., Kenji Y., Kenji T., Toshikazu F. Mining Product Reputations on the Web. In the Proceedings of KDD '02 Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 341-349.
[8] Kushal Dave, Steve Lawrence, David M. Pennock. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In the Proceedings of WWW '03 Proceedings of the12th international conference on World Wide Web. Pages 519 528.

Abstract: The field of cloud computing has reached to the new heights of technical Developmentand also speeding up the growth of thecomputational services in organization. Evenafter transferring to the cloud becoming analluring trend from a financial approach, thereare several other aspects that must be takeninto consideration by all organization beforethey decide to implement cloud services. Thecloud services are used as and when requiredfor the users. With the tremendous growth inthe cloud environment there are major issuesthat everyone should take into considerationlike data security and protection against access control! . Because of this many organization are moving towards the cloud. There isnumber of threats which cause possible harm or used to exploit important data. A threat can be either intentional or accidental. Risk is estimated based on statistical assumptions, and those are changing overtime. There is no absolute security. In this paper we present security issues in context of the data issue & security issue provided by cloud services. The aim of this paper is to provide a better understanding of the security issues& risks in in different services provided by cloud computing.

Keywords: Cloud computing, cloud computing services, security issue.

[1] Issue in technology innovation(No.3 Oct-10) Allan A. Friedman and Darrell M. West.

[2] Harjit Singh Lamba and Gurdev Singh, ―Cloud Computing-Future Framework for emanagementof NGO's‖, IJoAT, ISSN 0976-4860, Vol 2, No 3, Department Of Computer Science, Eternal University, Baru Sahib, HP, India, July 2011.

[3] Dr. Gurdev Singh, ShanuSood, Amit Sharma, ―CM- Measurement Facets for CloudPerformance‖, IJCA, , Lecturer, Computer science & Engineering, Eternal University, BaruSahib (India), Volume 23 No.3, June 2011.

[4] Joachim Schaper, 2010, ―Cloud Services‖, 4th IEEE International Conference on DEST,Germany.

[5] International Journal of Computing & Business Research ISSN (Online): 2229-6166 [6] Kevin Hamlen, Murat Kantarcioglu, Latifur Khan, BhavaniThuraisingham, Securit Issues for Cloud Computing, International Journal of Information Security and Privacy, 4(2),39-51, University of Texas, USA, April-June 2010.

[7]Prince Jain, Security Issues and their Solution in Cloud Computing,International Journal of Computing & Business Research ISSN (Online): 2229-6166 [8]MervatAdibBamiah* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 9, Issue No. 1, 087 – 090 [9] SearchCloudSecurity.com E-Guide [10]Google (Top 5 risk with Paas)

Abstract: Now a day's many of the things get controlled automatically. Everything is getting controlled using the mechanical or the automated systems. In every field machines are doing the human works. But still some area is controlled manually. For example traffic controls, road control, parking controlling. Keeping these things in mind we are trying to develop the project to automate the traffic tracking for the square. To make any project more useful and acceptable by any organization we need to provide multiple features in a single project. Keeping these things in consideration proposed system is less with multiple methodologies which can be used in traffic control system It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management.Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphesized. However, further analysis is needed in order to obtain the useful information for traffic management such as real time traffic density and number of vehicle types passing these roads. This paper presents emergency vehicle alert and traffic density calculation methods using IR and GPS

Keywords: Wireless Sensor Networks(WSN), Smart Traffic Light Control System (STLC), Smart Congestion Avoidance System (SCA)

[1] Milos Borenovic, Alexender Neskovic, Natasa Nescovic,"Vehicle positioning using gsm and cascade connected ann structure",IEEE transaction on intelligent transportation system volume 14 No.1 March 2013

[2] Jun Zheng and Abbas Jamalipour, "Introduction to Wireless Sensor Networks", Book: Wireless Sensor Networks: A Networking Perspective, Wiley-IEEE Press, 2009.

[3] Harpal Singh, Krishan Kumar, Harbans Kaur, "Intelligent Traffic Lights Based on RFID", International Journal of Computing & Business Research, Proceedings of "I-Society 2012‟

[4] Ms Promila Sinhmar, "Intelligent Traffic Light and Density Control using IR Sensors and Microcontroller", International Journal of Advanced Technology & Engineering Research (IJATER) ISSN NO: 2250-3536 VOLUME 2, ISSUE 2, MARCH 2012.

[5] Ching-Hao Lai, Chia-Chen Yu, "An Efficient Real-Time Traffic Sign Recognition System for Intelligent Vehicles with Smart Phones", 2010 International Conference on Technologies and Applications of Artificial Intelligence [6] Peyman Babaei, "Vehicles tracking and classification using traffic zones in a hybrid scheme for intersection traffic management by smart cameras", 2010 IEEE

[7] Henry X. Liu, Wenteng Ma, Heng Hu, Xinkai Wu and Guizhen Yu, "SMART-SIGNAL: Systematic Monitoring of Arterial Road Traffic Signals ", Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008

[8] Khodakaram saleemi fard, mehdi ansari "Modeling and simulation of urban traffic signals" Intenational journal of modeling and optimization,volume 3, No.2 April 2013

Abstract: Efficient analysis of data streams is becoming a key area of data mining research, as the number of applications demanding such processing increases. With recent advancement in technology, need for analysis of such unbounded streams is increasing day by day. Data mining process helps to excavate useful knowledge from rapidly generated raw data streams. In context with the continuously generated data, mining data streams is emerging challenging task in which several issues like limited space, limited time, accuracy, handling evolving data need to be considered. In this paper the main method of research is clustering which is focused to handle evolving data streams. Most of the previously proposed methods inherit the drawbacks of k means method and fail to handle the issues. A hybrid data mining approach encompassing windowing, grid and density clustering and divide and merge method is proposed. A dynamic data stream clustering algorithm (DDS) is used in which a dynamic density threshold is designed to accommodate the changing density of grids with time in data stream. At last divide and merge approach is used to handle varying data points and further refine the quality of result obtained. Experimental results on large data set demonstrate the utility of this approach.

Keywords -Concept drift, Clustering, Data mining, Data streams, Threshold.

[1] Yi-Hong Lu, Yan Huang, "Mining data streams using clustering" ,Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, IEEE, Guangzhou, August 2005,pp 2079-2083.
[2] Kapil Wankhade, Tasneem Hasan, Ravindra Thool, " A Survey: Approaches for Handling Evolving Data streams", In proceedings of International Conference on Communication Systems and Network Technologies,IEEE,INDIA,2013,pp 621-625.
[3] K.Prasanna Lakshmi, Dr.C.R.K.Reddy, "A Survey on different trends in Data Streams",Proceeding of International Conference on Networking and Information Technology, IEEE, 2010, pp 451-455.
[4] Amr Magdy, Noha A. Yousri, and Nagwa M. El-Makky, "Discovering Clusters with Arbitrary Shapes and Densities in Data Streams ", In proceedings of 10th International Conference on Machine Learning and Applications, IEEE, 2011, pp 279-282.
[5] S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan, "Clustering data streams," Foundations of Computer Science, Annual IEEE Symposium, 2000, pp 359-366.
[6] L. O'callaghan, N. Mishra, A. Meyerson, S. Guha, and R. Motwani, " Streaming-data algorithms for high-quality clustering", Proceedings of the 18th International Conference on Data Engineering (ICDE.02), IEEE, 2002. pp 685–694.
[7] C. Aggarwal, J. Han, J. Wang, and P. Yu, "A framework for clustering evolving data streams", In Proceedings of the 29th international conference on Very large data bases, 2003, Volume 29, pp 81–92.
[8] Edwin Lughofer, "Dynamic Evolving Cluster Models using On-line Split-and-Merge Operations", In proceedings of 10th International Conference on Machine Learning and Applications, IEEE, 2011, pp 20-26.

Abstract: Intrusion Detection System (IDS) is becoming a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in an interconnected network thereby securing the network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. Most of the previously proposed methods suffer from the drawback of k-means method with low detection rate and high false alarm rate. This paper presents a hybrid data mining approach for IDS encompassing feature selection, filtering, clustering, divide and merge and clustering ensemble. The main research method is clustering analysis with the aim to improve the detection rate and decrease the false alarm rate. A method for calculating the number of the cluster centroid and choosing the appropriate initial cluster centroidis proposed in this paper. The IDS with clustering ensemble is introduced for the effective identification of attacks. The KDD CUP 1999 data set is used to test the performance of the model. Experimental results shows that the system achieves high detection rate and low false alarm rate as compared to others existing methods.

Keywords - Intrusion detection system, data mining, clustering, k-means, ensemble, detection rate, false alarm rate

[1] V. K. Pachghare, Parag Kulkarni, Deven M. Nikam, "Intrusion Detection System Using Self Organizing Maps", In Proceedings of IAMA 2009, IEEE, 2009.

[2] Z. Muda, W. Yassin, M.N. Sulaiman, N.I. Udzir, "Intrusion Detection based on K-Means Clustering and OneR Classification", In Proceedings of 7th International Conference on Information Assurance and Security (IAS), IEEE, 2011, pp.192-197.

[3] Z. Muda, W. Yassin, M.N. Sulaiman, N.I. Udzir, "Intrusion Detection based on K-Means Clustering and Naïve Bayes Classification", In Proceedings of 7th International Conference on IT in Asia (CITA), IEEE, 2011.

[4] Shaik Akbar, Dr.K.Nageswara Rao, Dr.J.A.Chandulal, "Intrusion Detection System Methodologies Based on Data Analysis", In International Journal of Computer Applications (0975 – 8887) Volume 5– No.2, August 2010, pp.10-20.

[5] Deepthy K Denatious, Anita John, "Survey on Data Mining Techniques to Enhance Intrusion Detection", In Proceedings of International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDIA, IEEE.

[6] Kapil Wankhade, Sadia Patka, Ravindra Thool, "An Overview of Intrusion Detection Based on Data Mining Techniques", In Proceedings of 2013 International Conference on Communication Systems and Network Technologies, IEEE,2013, pp.626-629.

[7] Yang Zhong, Hirohumi Yamaki, Hiroki Takakura, "A Grid-Based Clustering for Low-Overhead Anomaly Intrusion Detection", IEEE, 2011, pp.17-24. [8] WANG Huai-bin, YANG Hong-liang, XU Zhi-jian, YUAN Zheng, "A clustering algorithm use SOM and K-Means in Intrusion Detection" In Proceedings of 2010 International Conference on E-Business and E-Government, IEEE, 2010, pp.1281-1284.

[9] Hongwei Gao, Dingju Zhu, Xiaomin Wang, "A Parallel Clustering Ensemble Algorithm for Intrusion Detection System'' In Proceedings of 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, IEEE, 2010, pp.450-453. [10] Z. Muda, W. Yassin, M.N. Sulaiman, N.I. Udzir, "Intrusion Detection based on K-Means Clustering and Naïve Bayes Classification", In Proceedings of 7th International Conference on IT in Asia (CITA), IEEE, 2011.

Abstract: In the recent years new methods of HumanComputer Interaction (HCI) are being developed. Out of this some of them are based on interaction with machines through hand gesture, head, facial expressions, speech, touch and there are still various current topic of research. But just relying on one of them reduces the accuracy of the whole HCI and is also limiting the options available to users. Although gesture-based interaction technology is used in many areas such as robot control, navigation system, medical research, it has not fully embedded into our daily life. In today's society, one of the most popular electronic products is mobile phone. Using mobile phones is a top priority for anyone living in the world, from young to old. Therefore, we want to combine gesture-based interaction technology and mobile phones. The objective of this paper is to use two of the important modes of interaction: hand and speech to control some mobile application

KEYWORDS: HCI (Human Computer Interaction), Artificial Neural Network, Microsoft Speech SDK.

1. AnupamAgrawal, Rohit Raj and ShubhaPorwal, "Vision-based multimodal human computer interaction using hand and head gesture". IEEE, 2013.
2. Siddharth S. Rautaray " Real Time Multiple Hand Gesture Recognition System for Human Computer Interaction,"2012.
3. Shweta K. Yewale et al. "Artificial Neural Network Approach For Hand Gesture Recognition", International Journal of Engineering Science and Technology (IJEST).
4. HaithamHasan · S. Abdul-Kareem, "Static Hand Gesture Recognition using Neural Network", springer-2012

Abstract: Today's organizations (e.g. enterprise, government agencies, libraries, "Smart" Home) raise increasing needs for information sharing via on-demand information access. A Information Brokering System (IBS) is a peer-to-peer overlay network that comprises diverse data servers and brokering components helping client queries locate the data server(s). Peer-to-peer (P2P) systems are gaining increasing popularity as a scalable means to share data among a large number of autonomous nodes. We study the privacy in Privacy-Preserving Information Brokering in Distributed Information Sharing through an innovative automaton segmentation scheme and query segment encryption and data management issues for processing XML data in a p2p setting, namely indexing, replication and query routing and processing.

Keywords - automaton segmentation, query segment encryption, privacy, Access control, information sharing.

[1] W. Bartschat, J. Burrington-Brown, S. Carey, J. Chen, S. Deming, and S. Durkin, "Surveying the RHIO landscape: A description of current RHIO models, with a focus on patient identification," Journal of AHIMA 77, pp. 64A–D, January 2006.
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[7] F. Li, B. Luo, P. Liu, D. Lee, P. Mitra, W. Lee, and C. Chu, "In-broker access control: Towards efficient end-to-end performance of information brokerage systems," in Proc. IEEE SUTC, 2006.
[8] F. Li, B. Luo, P. Liu, D. Lee, and C.-H. Chu, "Automaton segmentation: A new approach to preserve privacy in XML information brokering," in ACM CCS '07, pp. 508–518, 2007.
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Abstract: Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority Also it can find the shortest path to avoid congestion. The basic protocol, called Congestion-Aware Routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-Enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node tested

Keywords :Wireless sensor networks, routing, congestion, differentiated service shortest path.

[1] ] "Congestion Performance Improvement in Wireless Sensor Network", Junjie Xiong and Michael R. Ly 2012 IEEE.

[2] "On Reliable Broadcast in Low Duty-Cycle Wireless Sensor Networks" IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 5, MAY 2012

[3] "Reliable Sensor-to-Sink Data Transfer with Duty Cycles for Wireless Sensor Networks" IEEE, Conference on Local Computer Networks 2011.

[4] "Self-Adaptive On-Demand Geographic Routing for Mobile Ad Hoc Networks" IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 9, SEPTEMBER 2012.

[5] "A Congestion Mitigation Approach using Mobile Nodes in Wireless Sensor Networks", IEEE April 12–14, 2011

[6] "Packet Delivery Performance of Simple Cooperative Relaying in Real-World Car-to-Car Communications" IEEE WIRELESS COMMUNICATIONS LETTERS, VOL. 1, NO. 3, JUNE 2012

Abstract: Mobile Ad Hoc Network (MANET) is acollection of communication devices or nodes that wish to communicate without any fixed infrastructure and pre-determined organization of available links. Security is a major challenge for these networks due to their features of open medium, dynamically changing topologies. The black hole attack is a well known security threat in mobile ad hoc networks. However, it spuriously replies for any route request without having any active route to the specified destination. Sometimes the Black Hole Nodes cooperate with each other with the aim of dropping packets these are known as Cooperative Black Hole attack. This research work suggests the modification of Ad Hoc on Demand Distance Vector Routing Protocol. I am going to use a mechanism for detecting as well as defending against a cooperative black hole attack. This work suggest two new concepts, first one is Maintenance of Data Routing Information Table and second is cross checking of a node. This system also decreases the end to end delay and Routing overhead

Keywords -Mobile ad hoc network (MANET), Blackhole, Malicious node, Routing, AODV.

[1] Sukla Banerjee(2008). Detection/Removal of Cooperative Black and Gray Hole Attack in Mobile Ad-Hoc Networks. The World Congress on Engineering and Computer Science.

[2]Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto(2007).Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method. International Journal of Network Security, volume 5,Number 3,pp 338-346.

[3] Shalini Jain(2010). Advanced Algorithm for Detection and Prevention of Cooperative Black and Gray Hole Attacks in Mobile Ad Hoc Networks. International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 7

[4]LathaTamilselvan and V Sankarnarayana,(2008). Prevention of Black Hole Attack in MANET. Journal of Networks, Volume 3, Number 5, pp 13-20.

[5]Hesiri Weerasinghe (2007). Preventing Cooperative Black Hole Attacks in Mobile Ad Hoc Networks: Simulation Implementation and Evaluation", Proceedings of the Future Generation Communication and Networking, Volume 2, pp 362-367.

[6]Chang Wu Yu, Tung-Kuang, Wu, ReiHeng, Cheng and Shun Chao Chang(2007). A distributed and Cooperative Black Hole Node Detection and Elimination mechanism for Ad Hoc Networks. PAKDD International Workshop, Nanjing, China, pp 538-549

[7]Ravi Kumar Bansal and Anil Kumar Verma(2006). Performance Analysis of Cluster Based Routing Protocol in MANETs. MSc. thesis, Computer Science and Engineering Department Thapar Institute of Engineering and Technology (Deemend University), Patiala – 147004.

[8]AiffUmairSalleh, ZulkifliIshak, Norashidah Md. Din, and MdZainiJamaludin(2006). Trace Analyzer for NS-2, IEEE, Student Conference on Research and Development (SCOReD), Malaysia, pp. 29-32.

Abstract: Spam emails are the emails receiver does not wish to receive; it is also called unsolicited bulk email. Emails are used daily by number of user to communicate around the world. Today large volumes of spam emails are causing serious problem for Internet user and Internet service. Such as it degrades user search experience, it assists propagation of virus in network, it increases load on network traffic. It also wastes user time, and energy for legitimate emails among the spam. For avoiding spam there are so many traditional anti spam techniques includes Bayesian based filters, rule based system, IP blacklist, Heuristic based filter, White list and DNS black holes. These methods are based on content of the mail or links of the mail. In this paper, we presented our study on various existing spam detection methods and finding the effective, accurate, and reliable spam detection method.

Keywords: Bayesian Filter, Efficient Spam Detection, Ham, Spam, Spam Filter.

[1] Christina V, Karpagavalli S, Suganya G, "A Study on Email Spam Filtering Techniques", International Journal of Computer Applications (0975-8887) – Volume12-No.1, December 2010.
[2] Saadat Nazirova,"Survey on Spam Filtering Techniques", Scientific Reaserch-Vol. 3, No. 3, August 2011.
[3] Gordon V.Cormack,David R.Cheriton,"Email Spam Filtering: A Systematic Review ", Foundation and Trends in Information Retrieval-Vol. 1, No.4(2006).
[4] (GFI is Microsoft Gold certified pattern) http://www.gfi.com.
[5] Chi-yao Tseng, Pin-Chieh Sung, and Ming-Syan,"Cosdes:A Collaborative spam Detection System with a Novel E-Mail Abstraction Scheme", IEEE Transactions on Knowledge and Data Engineering, Vol-23, No. 5, May 2011.
[6] G, Ashokkumar, S.Dhineskumar,"Spam Filtering", Department of Computer Science and Engineering, Anna University, Channai-6000 025, India.
[7] David Mertz,"Spam filtering Techniques", IBM Developer Works.

Abstract: The development of Information Technology has generated enormous amount of databases and huge data in various areas. Loose coupling is adapted in Data Mining System, since it can fetch any portion or part of the data which is stored in database by more flexibility and in efficient manner. Therefore the Data mining system can be classified according to the kinds of databases and knowledge mined and also the techniques used or the application adapted. The traditional method is used to analyse data manually for patterns for the extraction of knowledge. In Banking, Health care, marketing, Science there will be a data analyst to work with data and scrutinizing the final role of decisions. This work is done by Data Mining. Data mining application can be generic or domain specific. It allows reusability of information in a feasible way and finally it makes possible to build large and scalable system. Applications of Data mining in computer security are designed to meet the needs of professionals such as researchers and practitioners in different fields. This paper gives the overview of Data mining system and few of its applications. Data mining is becoming a technology in activities as diverse as using large amount of historical data to predict the success of marketing.

KEYWORDS: Data Mining, Intrusion Detection, Predictive Data mining, clustering, e-commerce, web mining, Business intelligence

[1] Tan Pang-Ning, Steinbach, M., Vipin Kumar. "Introduction to Data Mining", Pearson Education, New Delhi, ISBN: 978-81-317-1472-0, 3rd Edition, 2009.
[2] Larose, D. T., "Discovering Knowledge in Data: An Introduction to Data Mining", ISBN 0-471-66657-2,JohnWiley & Sons, Inc, 2005
[3] Dunham, M. H., Sridhar S., "Data Mining: Introductory and Advanced Topics", Pearson Education,New Delhi, ISBN: 81-7758-785-4, 1st Edition, 2006.
[4] Kapil Wankhade, Sadia Patka, Ravindra Thool, "An Overview of Intrusion Detection Based on Data Mining Techniques", In Proceedings of 2013 International Conference on Communication Systems andNetwork Technologies , IEEE,2013, pp.626-629.
[5] Kapil Wankhade, Sadia Patka, Ravindra Thool, "An Efficient Approach for Intrusion Detection UsingData Mining Methods", In Proceedings of 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE,2013, pp.1615-1618.
[6] Sirgo, J., Lopez, A., Janez, R., Blanco, R., Abajo, N., Tarrio, M., Perez, R., "A Data Mining Enginebased on Internet, Emerging Technologies and Factory Automation," Proceedings ETFA '03, IEEEConference, 16-19 Sept. 2003.
[7] Fayyad, U., Piatetsky-Shapiro, G., and Smyth P., "From Data Mining to Knowledge Discovery inDatabases," AI Magazine, American Association for Artificial Intelligence, 1996.
[8] Bernstein, A. and Provost, F., "An Intelligent Assistant for the Knowledge Discovery Process",Working Paper of the Center for Digital Economy Research, New York University and also presented at the IJCAI 2001 Workshop on Wrappers for Performance Enhancement in Knowledge Discovery in Databases.
[9] Baazaoui, Z., H., Faiz, S., and Ben Ghezala, H., "A Framework for Data Mining Based Multi-Agent:An Application to Spatial Data, volume 5, ISSN 1307-6884," Proceedings of World Academy of Science,Engineering and Technology, April 2005.

Abstract:This paper presents a new approach towardsautomobile safety and security. In the field of an automotive research, a method to monitor and to detect a fatigue/drowsy or a drunken driver has been studied for many years. Previous research uses sensors such as an infrared camera for pupil detection or voice to detect fatigue, or image processing to detect driver expression. Even these approaches are able to detect driver's fatigue; however, these methods are not driver adaptable nor interactive with a outside driving situation. We propose driver's fatigue approach for real -time detection of driver fatigue. The system consists of a sensors directly pointed towards the driver's face. The input to the system is a continuous stream of signals from the sensors. The system monitors the driver's eyes to detect micro-sleeps (short periods of sleep lasting 3 to 4 seconds), monitors the driver's jaw to detect jaw movement and monitors to detect driver pulse from finger using LED & LDR assembling. The system can analyze the eyes lid movement, jaw movement, variation in pulse rate from the driver compute it as well as compare signal. Accordingly, we can be obtained the driver's fatigue level based on the response signals and alert driver.

Key Words: Driver fatigue, Fatigue detection, Drivermonitoring system architecture.

[1] "A Drowsy Driver Detection and Security System" Rajat Garg, Vikrant Gupta, ieeet Agrawal Department of Electronics and Communication VIT- University, Vellore- 600014, India {rajatgarg2006, vikrantgupta2006, vineetagrawal2006} @ vit.ac.in 9781-4244-3941-6/09/$25.00 ©2009 IEEE
[2] Multi-parametric Analysis of Sensory Data Collected from Automotive Drivers forBuilding a Safety-Critical Wearable Computing System Rajiv Ranjan Singhi, Rahul Banetjee2 iElectrical and Electronics Engineering Group, 2 Computer Science & information Systems Group Birla Institute of Technology & Science Pilani, Rajasthan, India 978-1-4244-6349-7/10/$26.00 ©201 0 IEEE
[3] "Distributed Sensor for Steering Wheel Grip Force
Measurement in Driver Fatigue Detection" Federico Baronti, Francesco Lenzi. 978-3-981-5-5/ DATE09 © 2009 EDAA
[4] ROC Analysis of a Fatigue Classifier for Vehicular Drivers Mahesh M Bundele Department of Computer Science Babasaheb Naik College of Engineering Pusad-445215, (MS) India 978-1-4244-5164-7/10/$26.00 ©2010 IEEE
[5] Detection of Driver Fatigue Caused by Sleep Deprivation Ji Hyun Yang, Zhi-Hong Mao, Member, IEEE, Louis Tijerina,
Tom Pilutti, Joseph F. Coughlin, and Eric Feron IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 39, NO. 4, JULY 2009
[6] Estimating Driving Performance Based on EG Spectrum Analysis 978-1-4244-6349-7/10/$26.00 ©201 0 IEEE
[7] W.B. Horng, C.Y. Chen, Y. Chang. (2004). Driver fatigue