Configuration management (CM) is a field of management that focuses on establishing and maintaining consistency of a system or product's performance and its functional and physical attributes with its requirements, design, and operational information throughout its life. CM can be defined as the management of security features and assurances through control of changes made to hardware, software, firmware, documentation, test, test fixtures, and test documentation throughout the life cycle of an information system. CM for information assurance, sometimes referred to as Secure Configuration Management, relies upon performance, functional, and physical attributes of IT platforms and products and their environments to determine the appropriate security features and assurances that are used to measure a system configuration state. For example, configuration requirements may be different for a network firewall that functions as part of an organization's Internet boundary versus one that functions as an internal local network firewall. In this paper we studies and discuss about the basic needs of Software Configuration management, the role of preventive Maintenance and predictive maintenance.
Data mining has been recognized as a new area for database research. In the area of knowledge discovery, association rule mining focuses on finding a set of all subsets of items, that frequently occur in database records and then extracting the interesting patterns found among them. Recent advancement in biotechnology has produced a massive amount of raw biological data which are accumulating at an exponential rate. Tuberculosis remains one of the leading causes of morbidity and mortality of mankind throughout the world. Mammalian cell entry (mce) gene is crucial in conferring virulence to Mycobacterium tuberculosis. This paper focuses on finding association between Mycobacterium tuberculosis and Mycobacterium leprae based on presence of mammalian cell entry gene. This gives useful insight for microbiologists in identifying and clustering the organisms.
This study investigates the role of the client isolation technology Public Secure Packet Forwarding (PSPF) in defending 802.11 wireless (Wi-Fi) clients, connected to a public wireless access point, from Address Resolution Protocol (ARP) cache poisoning attacks, or ARP spoofing. Exploitation of wireless attack vectors such as these have been on the rise and some have made national and international news. Although client isolation technologies are common place in most wireless access points, they are rarely enabled by default. Since an average user generally has a limited understanding of IP networking concepts, it is rarely enabled during access point configurations. Isolating wireless clients from one another on unencrypted wireless networks is a simple and potentially effective way of protection. The purpose of this research is to determine if a commonly available and easily implementable wireless client isolation security technology, such as PSPF, is an effective method for defending wireless clients against attacks.
ICT plays an indispensable role in the overall development of rural areas, especially in developing economies. There is an urgent need to bring the rural areas into the mainstream by providing them the research and findings which until now only the top level planners to some extent are using. The analysis of the Resource envelops of various department shows the gaps where a planner can pay attention and thus minimize the disparity of allocation. Outlier detection is a primary step in many data-mining applications. Outlier detection for data mining is often based on distance measures, clustering and spatial methods. In many data analysis tasks a large number of variables are being recorded or sampled. One of the first steps towards obtaining a coherent analysis is the detection of outlaying observations. Although outliers are often considered as an error or noise, they may carry important information. Detected outliers are candidates for aberrant data that may otherwise adversely lead to model misspecification, biased parameter estimation and incorrect results. It is therefore important to identify them prior to modeling and analysis.
Iris location estimation has been studied in numerous works in the literature. Previous research shows satisfactory result. However, in presence of non frontal faces, eye locators are not adequate to accurately locate the center of the eyes. The Iris location estimation techniques are able to deal with these conditions, hence they may be suited to enhance the accuracy.In this paper, a new method is proposed to obtain enhanced Iris location estimation. This method has three steps (1) enhance the accuracy of Iris location estimations (2) extend the operative range of the Iris locators with LBP and (3) improve the accuracy of the Iris location. These enhanced estimations are used to obtain a novel visual Iris estimation system.
In recent years, we have witnessed an increasing interest in deploying wireless sensor networks (WSNs) for real-life applications. However, before WSNs become a commodity, several challenging issues remain to be resolved. Object-tracking sensor network (OTSN)-based applications are widely viewed as being among the most interesting applications of WSNs. OTSN is mainly used to track certain objects in a monitored area and to report their location to the application’s users. However, OTSNs are well known for their energy consumption when compared with other WSN applications. In this paper, we propose a prediction-based tracking technique using sequential patterns (PTSPs) de-signed to achieve signi?cant reductions in the energy dissipated by the OTSNs while maintaining acceptable missing rate levels. PTSP is tested against basic tracking techniques to determine the appropriateness of PTSP under various circumstances. Our experimental results have shown that PTSP outperforms all the other basic tracking techniques and exhibits signi?cant amounts of savings in terms of the entire network’s energy consumption.
