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Comparative Analysis of Detection of DDoS Attacks in IEEE 802.15.4 Low Rate Wireless Personal Area Network

Procedia engineering 2012年 38卷 3855-3863页

作者： C. Balarengadurai S. Saraswathi Research Scholar Department of Computer Science & Engineering Manonmaniam Sundarnar University Thirunelveli 627 012 India Associate Professor Department of Information Technology Pondicherry Engineering College Puducherry 605 014 India

IEEE 802.15.4 Low rate wireless personal area networks (LR-WPANs) offer device level wireless connectivity. They bring to light a host of new applications as well as enhance existing applications. Due to their low cost, low power consumption and self-organization features, LR-WPANs are ideal for applications such as public security, battle field monitoring, inventory tracking as well as home and office automation. Nevertheless, one critical issue, security, needs to be solved before LR-WPANs are commonly accepted. Pursuing security in LR-WPANs is a challenging task. On one hand, wireless communications are inherently susceptible to interception and interference. On the other hand, most devices in LR-WPANs are resource-constrained and lack physical safeguards. DDoS attacks are a major security problem in wireless networks. DDoS is that lots of clients simultaneously send service requests to certain server on the internet thro wireless network such that this server is too busy to provide normal services for others. Attackers using legitimate packets and often changing package information, so that traditional detection methods based on feature descriptions are difficult to detect it. To date, various approaches have been proposed to detect DDoS attacks, but with limited success when they are used in the real world. This paper mainly focus on comparative analysis of DDoS attacks, detection and mitigation techniques are generally used. Beside this, we investigate about the approaches proposed that are considered efficient to survive in DDoS attacks. Finally, we conclude by highlighting the potential areas which can be targeted to optimized in minimizing the effects of DDoS attacks

关键词： IEEE 802.15.4 DDoS PHY Layer MAC Layer Attacks Detection

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Distributed Voronoi partitioning for multi-robot systems with limited range sensors

Distributed Voronoi partitioning for multi-robot systems wit...

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IEEE/RSJ International Conference on Intelligent Robotics and Systems

作者： K.R. Guruprasad Prithviraj Dasgupta Assistant Professor at the Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India. Associate Professor at the Department of Computer Science University of Nebraska at Omaha NE USA.

ISBN: (纸本)9781467317375

We consider the problem of distributed partitioning of an environment by a set of robots so that each robot performs its operations in the region within the corresponding cell. Voronoi partitioning is one of the most attractive techniques that has been used to solve this problem. It has been used in several distributed multi-robotic system and sensor network applications, such as sensor coverage, search and rescue, and coverage path planning. For a truly distributed implementation of such problems, each robot should be able to compute the corresponding Voronoi cell in a distributed manner. Further, in a practical application, the robots' sensors may have limited range, thus each robot may operate within a portion of its Voronoi cell constrained by the sensor range. We describe a distributed algorithm for computation of this range constrained Voronoi cell where each robot independently constructs chords corresponding to other robots that are within a distance of twice its sensor circle radius. A robot then uses a simple and fast technique to remove inessential chords to calculate the vertices of its Voronoi cell. We prove completeness and correctness of the proposed algorithm, and also provide the upper and lower bounds on the computational complexity of our algorithm. The theoretical results are validated with the help of experiments to show that for different values of sensor ranges, our proposed algorithm incurs a time complexity that is significantly lower than that of the existing full Voronoi partition computation algorithm. The maximum number of steps required by our algorithm is also shown to be within a constant times the lower bound given by the number of neighbors of each node.

关键词： correctness rescue sensor network Robot Rescue accuracy class Sensor networks multi-robot systems Sensors complexity classes Distribute operating range Lower bound range sensor partitions Path planning

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Personalization of Automobile News Documents Using Genetic Algorithm Model

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Procedia engineering 2012年 38卷 2930-2938页

作者： S. Akhilan S.R. Balasundaram Research Scholar Department of Computer Science and Engineering National Institute of TechnologyTamil Nadu Tiruchirappalli620 015INDIA Associate Professor Department of Computer ApplicationsNational Institute of TechnologyTamil Nadu Tiruchirappalli620 015 INDIA

Recent technologies make it possible to know about anything that happens in any part of the world to be made available to any person at any place and at any time. In this direction the services provided by Yahoo, CNN, CNET etc. are enormous that cater to the needs for a greater part of the globe to acquire timely and informative news about anything. Though search engines and with various news providers, present web scenario is capable of providing information in variety of ways, it is a fact that not all news are relevant for all people. That is, user satisfaction as well as user preferences must be taken into account while providing news items. In this regard, the need for personalization arises whereby users can obtain relevant information of their interests through technologies. Personalized news provisions make the readers to get information of their choice, thereby the time involved in searching the news of their taste is greatly minimized. This paper focuses on the design of a personalized news provider (PNP) system for searching automobile news documents as per the user's interests using traditional and classical optimization method.

关键词： News items user profile information retrieval filtering techniques automobile news personalization selection method genetic algorithm

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Computational Methods for Objective Assessment of Conjunctival Vascularity

Computational Methods for Objective Assessment of Conjunctiv...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Reza Derakhshani Sashi K. Saripalle Plamen Doynov an Associate Professor at the Department of Computer Science Electrical Engineering University of Missouri at Kansas City Kansas City MO 64110-2499 graduate student at the Department of Computer Science Electrical Engineering University of Missouri at Kansas City Kansas City MO 64110-2499

ISBN: (纸本)9781424441198

Assessment of vascularity of conjunctiva has many diagnostic and prognostic applications, thus creation of computational methods for its fast and objective assessment is of importance. Here we provide two different methods for estimation of conjunctiva's vascularity from color digital images, with our best results showing a correlation coefficient of 0.89 between the predicted and ground truth values using a committee of artificial neural networks.

关键词： Computational Methods Assessment

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Dimensionality reduction based on the classifier models: Performance Issues in the prediction of Lung cancer

Dimensionality reduction based on the classifier models: Per...

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CSI International Conference on Software engineering (CONSEG)

作者： K. Balachandran R. Anitha Research scholar & Associate Professor Computer Science & Engineering Department Christ University Bangalore Karnataka India Director MCA KSR College of Technology Tirunchengodu Tamil Nadu India

Dimensionality reduction is an essential feature to reduce the complexity of the computations in the large data set environment. When handling large quantum of medical data set, as in the case like, Lung cancer prediction, based on symptoms and Risk factors, number of attributes/ dimensions pose a major challenge. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are chosen, which are based on statistical, rule based, logic based and artificial Neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Confusion matrix of the models before and after dimensionality reduction is computed. Based on the confusion matrix result the models are compared and based on the performance optimal model is chosen. It is found that Multi-layer perceptron based artificial neural network model gives better performance compared to other approaches.

关键词： Lungs Computational modeling Artificial neural networks Expert systems Breast cancer

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An ontological approach for effective knowledge engineering

An ontological approach for effective knowledge engineering

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International Conference on Software engineering and Mobile Application Modelling and Development (ICSEMA 2012)

作者： S. Vigneshwari M. Aramudhan Research Scholar Department of Computer Science and Engineering Sathyabama University Chennai Tamil Nadu India Associate Professor and Head Department of Information Technology Perunthalaivar Kamarajar Institute of Science and Technology Karaikal Tamil Nadu India

Ontologies are efficient way of getting a better idea about a particular domain. There are various forms of ontologies. Certain ontologies are reusable and are available online. Some other ontology can be developed for a specific purpose and are used to yield precise results in the field of knowledge engineering and automated tasks. If we want to gather information between different ontologies of different domains a cross ontology mining method has to be implemented for mining the semantic data. A framework has been discussed here for advanced ontology mining which can be very much useful to the semantic society.

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Binary Association Rule Mining Using Bayesian Network

Binary Association Rule Mining Using Bayesian Network

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International Conference on Information and Network Technology（ICINT 2011）

作者： Venkateswara Rao Vedula Satish Thatavarti Associate Professor Dept of Computer Science and Engineering Sri Vasavi Engineering CollegePedatadepalliTadepalligudem Final M.Tech Student Dept of Computer Science and Engineering Sri Vasavi Engineering CollegePedatadepalliTadepalligudem

The Trivial association rule mining which should be fixed in order to avoid both that early trivial rules is retained and also that interesting rules are not *** fact the situations which use the relative comparison to express association rules are more complete than the situations that generate association rules that use the absolute *** traditional concept of association rule mining will lose some information in generating association *** user does have to determine the degree of support and confidence thresholds before generating association *** our paper,we proposed new approach in finding association *** new approach uses the concept of rough set theory and Bayesian network classification to generate association *** provides a way for decision maker to get more information to generate association rules than traditional *** new approach for revving association rules has the ability to handle the certainty in the classifying process so that we can reduceinformation loss and enhance the result of data *** new algorithm can simulate the value of probability which is based on continuous data set.

关键词： Data mining,Association rule mining,frequent item set,Electronic commerce,Rough set theory,Bayesian network,Bayesian classification

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Automatic Segmentation of the Heart in CT Images

Automatic Segmentation of the Heart in CT Images

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长江2011国际医学影像物理和工程应用大会暨第六届中国医学影像物理学术年会

作者： K.Krishnaveni Monisha Krithika Mahalakshmi Ashwathi Associate Professor Department of Computer ScienceSri.S.R.N.M.CollegeSatturIndia Electronics and Communication Velammal Engineering CollegeChennaiIndia

Automatic segmentation methods are used in efficient analysis of large amount of data produced by computed tomography (CT) *** paper introduces a simple and efficient approach for the segmentation of the four chambers of the heart from CT ***,the image is preprocessed by means of sub-sampling of the image which is then binarized by applying the fuzzy optimal threshold *** boundaries of the thresholded image are smoothed by applying mean *** detection is then applied to detect the edges in the filtered image and the detected edges are then *** Hough Transform is applied to the preprocessed image to localize the chambers of the *** chambers are then segmented using the concept of connected and closed connected components *** developed segmentation method facilitates fully automatic quantitative analysis of heart in clinical care.

关键词： Computed Tomography sub-sampling Generalized Hough Transform closed connected components

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Why Did Your Project Fail?

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COMMUNICATIONS OF THE ACM 2009年第12期52卷 130-134页

作者： Cerpa, Narciso Verner, June M. Associate professor computer science department Faculty of Engineering Universidad de Talca Chile. Visiting professor of software engineering CSE University of New South Wales Sydney Australia.

The article discusses the reasons for the failure of software development projects. Several suggested reasons are mentioned, including scheduling problems, cost overruns, and constantly-changing user requirements. The expense and difficulty of developing software systems is discussed, noting that minimal understanding is gained from the results of past software development projects; project managers (PMs) must balance pressures from stakeholders such as their company's upper management, marketing department, and accounting department; and the influence of such pressures upon the cost and quality of the final software product.

关键词： computer software development PROJECT management COST control PROJECT evaluation PROJECT managers PRICE-quality relationship HIGH technology industries HIGH technology INDUSTRIAL design PRODUCT design PRODUCT management NEW product development QUALITY of products INFORMATION technology projects DESIGN & technology

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A Study On Iterative Learning Control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of Control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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