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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
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.



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.

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


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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.




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.

Fuzzy Logic, Neural Network, Canny, Morphology.


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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.

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


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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.

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

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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.

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


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