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Exploring Innovation| ISSN:2277-3878(Online)| Reg. No: 97794/BPL/S/2012| Published by BEIESP| Impact Factor:4.46
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Volume-5 Issue-6: Published on January 30, 2017
02
Volume-5 Issue-6: Published on January 30, 2017

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S. No

Volume-5 Issue-6, January 2017, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Bhagwat P. Dwivedi, Shiv Kumar, Babita Pathik

Paper Title:

A Survey on Intrusion Detection Technique over the Web Data

Abstract: The intrusion detection systems (IDSs) generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this work, a new data mining technique has been developed to group alarms and to produce clusters. we have monitored a paper IDS over web mining – up approach which is efficient and determined to visualized the intrusion data and optimize according to the user requirement and monitored the data efficiently, here we would like to further enhance research work on analyzing and using the entropy data as input and to use them in  technique to visualize and to optimize according to the user requirement in the web entropy visualization.

Keywords:
 Network intrusion, web mining scenario, web intrusion data, Data Mining Algorithms

References:

1.       Zhan Jiuhua Intrusion Detection System Based on Data Mining Knowledge Discovery and Data Mining, 2008. WKDD 2008.
2.       Bane Raman Raghunath Network Intrusion Detection System (NIDS)Emerging Trends in Engineering and Technology, 2008. ICETET '08.

3.       Changxin Song Design of Intrusion Detection System Based on Data Mining Algorithm 2009 International Conference on Signal Processing Systems.

4.       Wang Pu Intrusion detection system with the data mining technologies Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference.

5.       Gaikwad, D.P. Sonali Jagtap, Kunal Thakare, Vaishali Budhawant Anomaly Based Intrusion Detection System Using Artificial Neural Network and fuzzy clustering International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, 1 (9.) (2012 November).

6.       Goyal, C. Kumar GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System, Electrical Engineering and Computer Science, North West University Technical Report (2008).

7.       Gu, P. Porras, V. Yegneswaran, M. Fong, W. Lee BotHunter: detecting malware infection through IDS-driven ialog correlation Proc. of 16th USENIX Security Symp. (SS’07) (2007 Aug), pp. 12:1–12:16.

8.       G. Gu, J. Zhang, W. Lee BotSniffer: detecting botnet command and control channels in network traffic Proc. of 15th Ann. Network and Distributed Sytem Security Symp. (NDSS’08) (2008 Feb).

9.       V. Jaiganesh, P. Sumathi, S. Mangayarkarasi   An Analysis of Intrusion Detection System using back propagation neural network IEEE Computer Society Publication (2013).

10.    Buccafurri, G. Lax, D. Rosaci and D. Ursino, ‗Dealing with Semantic Heterogeneity for Improving Web Usage‘. Data Knowledge Eng. Vol. 58, Issue 3, pp. 436–465,2006.

11.    Singh A., Juneja D. and Sharma A.K., ‗Design of Ontology-Driven Agent based Focused Crawlers‘. In proceedings of 3rd International Conference on Intelligent Systems & Networks (IISN-2009),Organized by Institute of Science and Technology, Klawad, 14 -16 Feb 2009, pp. 178-Available online in ECONOMICS OF NETWORKS ABSTRACTS, Volume 2, No. 8: Jan 25, 2010.


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2.

Authors:

M. Jansirani, P. Sumitra

Paper Title:

A Novel Method for Vehicle Detection using Edge Detection and Fuzzy Logic Based Algorithm

Abstract:  Vehicles moving on road are of importance because problems like traffic congestion, economic waste, jamming on the underpasses and over-bridges (if the vehicle passing through is not of the permissible size) are associated with them. These problems can be dealt with by using various morphological processes based image processing techniques to detect the vehicles. In this thesis, the images of moving and still vehicles have been taken and an algorithm is used for vehicle detection which is based on image processing techniques and classification of vehicles in the form of natural description based on fuzzy logic such as classification based on area and circumference using Fuzzy Logic. To perform classification, fuzzification of area and circumference is done and each vehicle type (e.g. small, medium and big) is assigned a measurement range of values by designing fuzzy rules and finally defuzzification is done.  Edge detection is considered to be fundamental step in the field of image processing and computer vision. There are 3 types of discontinuities in a digital image: point, line, edge. The most common way is to use spatial masks which have properties to detect these discontinuities. More than isolated points and lines detecting edges are important because they form an important part of image segmentation. Edge detection is basically a method of segmenting an image into regions based on discontinuity, enhancing the presence of these discontinuities in the image allows us to improve the perceived image quality under certain conditions. Edge detection makes use of differential operators to detect changes in the gradients of the grey or color levels in the image. Edge detection is divided into two main categories: first-order edge detection, example for first order edge detection are Sobel, Robert, Perwitt and second-order edge detection, example for second order edge detection are Laplacian and Canny. Image edge is often buried by noise, so it‘s necessary to research edge detection algorithm. Since traditional edge detection like Sobel, Perwitt, Robert operator are sensitive noise, to overcome that problem, some new algorithm is applied in edge detection such as Canny, Morphology, Neural network and Fuzzy logic. This is to be implemented in MATLAB.  Fuzzy logic is one of the new methods and it was based on set theory.  Fuzzy logic based algorithm is very efficient and flexible to detect the edges of vehicle in an input image by scanning it through the 2*2 mask. The main benefit of fuzzy set theory is able to model the ambiguity and the uncertainty. In the proposed method trapezoidal and triangular membership function of mamdani type FIS is used for four inputs containing two fuzzy set and one output containing one fuzzy set. The 2*2 masks is slide over entire vehicle image, and then pixels values of masks are examined through various ten rules which are defined in FIS rule editor. Based on these set of rules the output of fuzzy is decided that particular pixel is edge or not. For getting better results Gaussian filtering is used. Experimental result shows the ability of the proposed method in finding the thin edges of vehicle image.

Keywords:
Fuzzy Logic, Neural Network, Canny, Morphology.


References:

1.    Gupte, S.; Masoud, O.; Martin, R.F.K.; Papanikolopoulos, N.P, “Detection and Classification of Vehicles”, IEEE Transactions on Intelligent Transportation Systems, 3, No.1, Mar 2002.
2.    Hossain M. Julius, Dewan M. Ali Akber and CHAE Oksam, “Moving Object Detection for Real Time Video Surveillance: An Edge Based Approach”, IEICE Transactions on Communications, 90, No. 12.

3.    Gonzales C. Rafael, Woods E. Richard, “Digital Image Processing”, 1998, Second Edition, Prentice Hall Publications pp. 567–634.

4.    Weihua Wang, “Reach on Sobel Operator for Vehicle Recognition”, in proc. IEEE International Joint Conference on Artificial Intelligence 2009, July 2009, California, USA.

5.    Alper PAHSA, Ankara University, Computer Eng. Dept.,” Morphological Image Processing with Fuzzy Logic “.

6.    Nedeljkovic, “Image Classification based on Fuzzy Logic“, Map Soft Ltd, Zahumska 26 11000 Belgrade, Serbia and Montenegro.


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3.

Authors:

Jyoti Pawar, G.P. Chakote

Paper Title:

User-Defined Privacy Grid System for Continuous Location-Based Services

Abstract: Location-based services (LBS) require users to continually report their location to a potentially unreliable server to obtain services based on location, which may expose them to confidentiality risks. Unfortunately, existing privacy techniques have several limitations, such as the requirement of a fully reliable third party offering limited privacy and high communication overhead. In this paper, we propose a user-defined privacy grid system called a dynamic grid system (DGS); The first holistic system that meets four essential requirements for the preservation of instant and continuous privacy LBS. (1) The system requires only a trusted third party responsible for the proper execution of the matching operations. This semi-reliable third party has no information about a user's location. (2) Secure confidentiality and continued site confidentiality are warranted in our defined opponent models. (3) The cost of communication for the user does not depend on the level of confidentiality desired by the user, it depends only on the number of relevant points of interest near the user. (4) Although we only focus on range and k-neighbor-neighbor queries in this work, our system can be extended to support spatial queries without modifying the algorithms executed by the semi-reliable third party and The database server, the search area required for a spatial query can be extracted into spatial regions. The experimental results show that our DGS is more efficient than the state-of-the-art privacy technology for continuous LBS.

Keywords:
 Location Based Service (LBS), Dynamic Grid System (DGS), Confidentiality, Privacy Technologys.

References:

1.       B. Bamba, L. Liu, P. Pesti, and T.Wang, “Supporting anonymous location queries in mobile environments with PrivacyGrid,” in WWW, 2008.
2.       C.-Y. Chow and M. F. Mokbel, “Enabling private continuous queries for revealed user locations,” in SSTD, 2007.

3.       B. Gedik and L. Liu, “Protecting location privacy with personalized kanonymity: Architecture and algorithms,” IEEE TMC, vol. 7, no. 1, pp.1–18, 2008.

4.       M. Gruteser and D. Grunwald, “Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking,” in ACM MobiSys, 2003.

5.       P. Kalnis, G. Ghinita, K. Mouratidis, and D. Papadias, “Preventing location-based identity inference in anonymous spatial queries,” IEEE TKDE, vol. 19, no. 12, pp. 1719–1733, 2007.

6.       M. F. Mokbel, C.-Y. Chow, and W. G. Aref, “The new casper: Query processing for location services without compromising privacy,” in VLDB,2006.

7.       T. Xu and Y. Cai, “Location anonymity in continuous location-based services,” in ACM GIS, 2007.

8.       “Exploring historical location data for anonymity preservation in location-based services,” in IEEE INFOCOM, 2008.

9.       G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan, “Private queries in location based services: Anonymizers are not necessary,” in ACM SIGMOD, 2008.

10.    M. Kohlweiss, S. Faust, L. Fritsch, B. Gedrojc, and B. Preneel, “Efficient oblivious augmented maps: Location-based services with a payment broker,” in PET, 2007.

11.    R. Vishwanathan and Y. Huang, “A two-level protocol to answer private location-based queries,” in ISI, 2009.

12.    J.M. Kang,M. F.Mokbel, S. Shekhar, T. Xia, and D. Zhang, “Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors,” in IEEE ICDE, 2007.

13.    S. Jensen, D. Lin, B. C. Ooi, and R. Zhang, “Effective density queries of continuously moving objects,” in IEEE ICDE, 2006.

14.    S. Wang and X. S. Wang, “AnonTwist: Nearest neighbor querying with both location privacy and k-anonymity for mobile users,” in MDM, 2009.

15.    W. B. Allshouse,W. B. Allshousea,M. K. Fitchb, K. H. Hamptonb, D. C. Gesinkc, I. A. Dohertyd, P. A. Leonebd, M. L. Serrea, andW. C. Millerb,“Geomasking sensitive health data and privacy protection: an evaluation using an E911 database,” Geocarto International, vol. 25, pp. 443–452, October 2010.

16.    Gkoulalas-Divanis, P. Kalnis, and V. S. Verykios, “Providing kanonymity in location based services,” SIGKDD Explor. Newsl., vol. 12, pp. 3–10, November 2010.

17.    Boneh and M. K. Franklin, “Identity-based encryption from the weil pairing,” in CRYPTO, 2001.

18.    Menezes, M. Qu, and S. Vanstone, “Some new key agreement protocols providing mutual implicit authentication,” in SAC, 1995.

19.    S. Yau and H. An, “Anonymous service usage and payment in servicebased systems,” in IEEE HPCC, 2011, pp. 714–720.

20.    M. Balakrishnan, I. Mohomed, and V. Ramasubramanian, “Where’s that phone?: Geolocating ip addresses on 3G networks,” in ACM SIGCOMM IMC, 2009.

21.    R. Dingledine, N. Mathewson, and P. Syverson, “Tor: the secondgeneration onion router,” in USENIX Security, 2004.

22.    Bissias, M. Liberatore, D. Jensen, and B. Levine, “Privacy vulnerabilities in encrypted HTTP streams,” in PET, 2006.

23.    P. Golle and K. Partridge, “On the anonymity of home/work location pairs,” in Pervasive Computing, 2009.

24.    IEEE, P1363-2000: Standard Specifications for Public-Key Cryptography, 2000.

25.    B. Lewko and B. Waters, “Efficient pseudorandom functions from the decisional linear assumption and weaker variants,” in ACM CCS, 2009.

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4.

Authors:

G. Ganesan Subramanian, V. Mohan, S. Sivamani, G. Sundaravadivel

Paper Title:

Solar Powered Street Sweeping Mechanism for Clean India

Abstract: Cleanliness is next to Godliness”- A proverb which points the most aspect of cleanliness in every proper civilization. For the last two decades, an increase in awareness towards environmental degradation due to pollution in various forms through dust particles, runoff  water, improper sanitation , waste products such as plastic products etc., In order to enhance the cleanliness of surroundings , a design of electric vehicle that can both maintain operational efficiency and stick to its task. A multi operational task of sweeping mechanism, vacuum cleaning mechanism, pick and place mechanism is being adopted to ensure the work conventionally done with different a novel method which harnessing renewable energy sources, a sweeper machine which operates in solar that could be used for Industrial sectors and for public.  A prototype setup is being arranged to do the specified task and corresponding time periods are noted down for each module which ensures the environment clean.

Keywords:
Brushless DC motor (BLDC), Motor Circuit  (MC), Sweeper circuit(SC), Vacuum circuit(VC), Pick and Place Mechanism (PPM)


References:

1.       Allison, R.A. and Chiew, F.H.S. (1995). Monitoring of Stormwater Pollution for Various Land-uses in an Urban Catchment, Proc. 2nd Int. Sym. On Urban
2.       Allison, R.A., Chiew, F.H.S. and McMahon, T.A. (1997a). Stormwater Gross Pollutants, Industry Report 97/11, Cooperative Research Centre for Catchment Hydrology, December 1997.

3.       Allison, R.A., Rooney, G., Chiew, F.H.S. and McMahon, T.A. (1997b). Field Trials of Side Entry Pit Traps for Urban Stormwater Pollution Control, Proceedings of the 9th National Local Government Engineering Conference, I.E. Aust., Melbourne, Australia.

4.       Allison, R.A., Walker, T.A., Chiew, F.H.S., O’Neill, I.C. and McMahon, T.A. (1998). From Roads to Rivers, - Gross Pollutant Removal from Urban Waterways, Cooperative Research Centre for Catchment Hydrology.

5.       Alter, W. (1995). The Changing Emphasis of Municipal Sweeping Tandem., American Sweeper, Volume 4, Number 1, pp.6.

6.       Baker R.A. (1980). Contaminants and Sediment, Ann Arbor Science, Publishers inc/the Butterworth Group, Michigan.

7.       Ball, J.E. and Abustan, I. (1995). An Investigation of Particle Size Distribution during Storm Events from an Urban Catchment, Proceedings of the Second

8.       Bannerrnan, R., Baun, K. and Bohn, vf. (1983). Evaluation of Urban Non-Point Source Pollution Management in Milwaukee County Wisconsin. Vol 1. Urban Stormwater Characteristics, Sources and Pollutant Management by Street Sweeping. Prepared for Environmental Protection agency, Chicago, IL.

9.       Bender, G.M. and Terstriep, M.L. (1984). Effectiveness of Street Sweeping in Urban Runoff Pollution Control, Sci. Total Environ, 33: 185-192.

10.    CH2M-HILL, Pitt, R., Cooper and Associates, Inc., and Consulting Engineering Services, Inc, (1982). Street Particulate Data Collection and Analyses, Prepared for
WASHOE Council of Governments, August, 1982, Vol. Control. EPA Publication, PB85-102507.

11.    Colwill, G.M., Peters, C.J. and Perry, R. (1984). Water Quality of Motorway Runoff, TRRL Supp, Rpt 823, Crowthorne, Berkshire, England.

12.    Dempsey, B.A., Tai, Y.L., Harrison, S.G. (1993). Mobilisation and Removal of Contaminants Associated with Urban Dust and Dirt, Wat. Sci. Tech. Vol. 28, No.3-5, pp. 225-230.

13.    Ellis, J.B., Revitt, D.M., Harrop, H.O. and Beckwith P.R. (1986). The Contribution of Highway Surfaces to Urban Stormwater Sediments and Metal Loadings, Sci. Total Environ, Vol. 59. Pp 339-349.

14.    Essery, C.I. (1994). Gross Pollutant Water Quality - Its measurement and the performance of remediation technologies/ management practices, Stormwater Industry Association, Best Stormwater Management Practice, NSW.

15.    Fergusson J.E and Ryan D.E. (1984). The Elemental Composition of Street Dust from Large and Small Urban Areas Related to City Type, Source and Particle Size, Sci. Total Environment, Vol.34 pp.101- 116, Elsevier Science Publishers B.V., Amsterdam - Printed in the Netherlands.


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