Information Technology

Now a days Public and private organizations increase their database system requirement for day-to-day business. Hence database security becomes more crucial as the scale of database is growing. A signified approach for protecting information which enforcing access control policies based on subject and object and their characteristics. There are many security models for database systems. The database security systems have developed a number of different access control policies for assuring data confidentiality, integrity and availability. In this paper we survey the concepts under access control policies for database security. We review the key access control policies such as Mandatory Access Control policy(MAC), Discretionary Access Control Policy(DAC), and Role Based Access Control Policy(RBAC) and propose a concept on RBAC policy that is instead of access control through role assigned to the users, the users are assigned by some level of access control.

The paper proposes Handwritten Character Recognition method using 2D view and Support Vector Machine (SVM). In this all the character images are Pre-processed (includes Normalization and Noise Removal), which are further used for feature extraction using two dimensional (2D) views. From each character four different views (Top, Bottom, Left, and Right) are obtained called as basic views. All basic views are not able to collect the complete information of character image. The hidden information is capture separately called as extra views. From each view 16 features are extracted and combined to obtain 80 features. These features are used to train SVM to separate different classes of characters. Handwritten Character database is used for training and testing of SVM classifier. Support Vector Machine provides a good recognition result for lower case characters and upper case characters are 82%.

Mobile ad hoc networks will appear in environments where the nodes of the networks are absent and have little or no physical protection against tampering. The wireless nodes of MANET are thus susceptible to compromise and are particularly vulnerable to denial of service (DoS) attacks launched by malicious nodes or intruders. Flooding attack is one such type of DoS attack, in which a compromised node floods the entire network by sending a large number of fake RREQs to nonexistent nodes in the network, thus resulting in network congestion. In this paper, the security of MANET AODV routing protocol is investigated by identifying the impact of flooding attack on it. A simulation study of the effects of flooding attack on the performance of the AODV routing protocol is presented using random waypoint mobility model The simulation environment is implemented by using the NS-3 network simulator. It is observed that due to the presence of such malicious nodes, average percentage of packet loss in the network, average routing overhead and average bandwidth requirement? all increases, thus degrading the performance of MANET significantly.

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The Verification of handwritten Signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: online Signature Verification. This paper addresses using ensemble approach of Genetic Algorithm for online Signature Verification. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of problem space in contrast to off-line which uses only the static features. We show that proposed ensemble of Genetic Algorithm is superior to individual approach for Signature Verification in terms of classification rate.

Bloom Filter (BF) is a simple but powerful data structure that can check membership to a static set. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the hash bitmap is sufficiently large. Spam is an irrelevant or inappropriate message sent on the internet to a large number of newsgroups or users. A spam word is a list of well-known words that often appear in spam mails. The proposed system of Bin Bloom Filter (BBF) groups the words into number of bins with different false positive rates based on the weights of the spam words for spam filtering. Simulated Annealing (SA) is stimulated by an analogy to annealing in solids. It is used to search for feasible solutions and converge to an optimal solution. In this paper SA is applied to minimize the total membership invalidation cost of BBF. The experimental results are analyzed for various sizes of bins. The results show that, the BBF using SA with different false positive rate has lower total membership invalidation cost than the standard BF.

Weather forecasting is a very fundamental application in meteorology and technologically challenging problem. The estimation of temperature values are needed for agricultural, technical and environmental applications. Meteorological dataset has historical data of all weather parameters and the temporal analysis of this dataset can help to mine meaningful knowledge. There are number of techniques available in data mining, but Association Rule Mining is one of the most popular technique to mine large amount of dataset for finding the hidden relationship between various dataset variables values and identifies correlations between them. The scope of this research is to analyze temporal rules generated to predict day to day temperature variation of a specific region Surat, India. To accomplish this, the framework is proposed for prediction of day temperature variation from seasons. From the experiments, achieved higher accuracy compare to other data mining technique and the rules which show how day variations are related. Also prepared the list of parameters which is less in number to help for the prediction instead of all parameters and thus it helps in the reduction of the dataset size.

There has been a wide variety of research going on in the field of privacy preservation in data mining. Most of the methods are implemented for static data. But the world is filled with dynamic data which grows rapidly than what we expect. No technique is better than the other ones with respect to all criteria. This paper focus on a methodology that is well suited for incremental data that preserves its privacy while also performing an efficient mining .the method does not require the entire data to be processed again for the insertion of new data. The method uses data summarization technique which is used for both incremental data and providing privacy for such data. We develop the algorithm for making the environment flexible and cost effective.

Automated extraction of popular news from the freely accessible news corpora is becoming important in today’s internet world. Because of the availability of large volumes of news wire sources, it is hectic for the human beings to search and decide whether it is popular or not. This necessitates a tool which should extract hot news in a period of time. Term weighting is a useful technique which extracts salient features from the text documents. Though, there exist different tools based on different term weighting algorithms, these are inaccurate in the extraction of hot news. In this paper, a new feature extraction algorithm for long running events based on frequency, position, scattering and topicality is proposed. Experimentation has been done on different retrospective news wire sources. Experimental results demonstrate that the proposed algorithm is suitable for extracting hot news.

Software effort estimation is an important area in the field of software engineering. If the software effort is over estimated it may lead to tight time schedules and thus quality and testing of software may be compromised. In contrast, if the software development effort is underestimated it may lead to over allocation of man power and resource. There are many models for estimating software effort. The aim of the work is to estimate software effort using various machine learning techniques like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and Decision Tree (DT). China dataset of software projects has been used in order to compare the performance results obtained from these models. The indices are Sum-Square-Error (SSE), Mean-Square-Error (MSE), Root-Mean-Square-Error (RMSE), Mean-Magnitude-Relative-Error (MMRE), Relative-Absolute-Error (RAE), Relative-Root-Square-Error (RRSE), Mean-Absolute-Error (MAE), Correlation Coefficient (CC), and PRED (25).

The security of cloud users, a few proposals have been presented recently. Core objective of using cloud is to provide Security, Scalability, Availability, Performance, and Cost effective. A Safe Cloud Storage to provide con?dentiality and fine-grained access control for data stored in the cloud. This system enables the users to enjoy a secure outsourced data services at a minimized security management overhead. Here outsources not only the data but also the security management to the cloud in a trust way. Our system is fully integrates with Encryption , storing and retrival operations. Propose a threshold proxy re-encryption scheme and it integrates with decentralized erasure code such that a secure distributed system is formulated. Analyze and suggest appropriate limitations for the number of copies of a message transmitted to storage servers and the number of storage servers queried by a key server. These restrictions allow more flexible regulation between the number of storage servers and robustness.