An Improved Technique for Multi-Dimensional Constrained Gradient Mining

Multi-dimensional Constrained Gradient Mining, which is an aspect of data mining, is based on mining constrained frequent gradient pattern pairs with significant difference in their measures in transactional database. Top-k Fp-growth with Gradient Pruning and Top-k Fp-growth with No Gradient Pruning were the two algorithms used for Multi-dimensional Constrained Gradient Mining in previous studies. However, these algorithms have their shortcomings. The first requires construction of Fp-tree before searching through the database and the second algorithm requires searching of database twice in finding frequent pattern pairs. These cause the problems of using large amount of time and memory space, which retrogressively make mining of database cumbersome.  Based on this anomaly, a new algorithm that combines Top-k Fp-growth with Gradient pruning and Top-k Fp-growth with No Gradient pruning is designed to eliminate these drawbacks. The new algorithm called Top-K Fp-growth with support Gradient pruning (SUPGRAP) employs the method of scanning the database once, by searching for the node and all the descendant of the node of every task at each level. The idea is to form projected Multidimensional Database and then find the Multidimensional patterns within the projected databases. The evaluation of the new algorithm shows significant improvement in terms of time and space required over the existing algorithms.  &nbsp

A SURVIVABLE DISTRIBUTED DATABASE AGAINST BYZANTINE FAILURE

Distributed Database Systems have been very useful technologies in making a wide range of information available to users across the World. However, there are now growing security concerns, arising from the use of distributed systems, particularly the ones attached to critical systems. More than ever before, data in distributed databases are more susceptible to attacks, failures or accidents owing to advanced knowledge explosions in network and database technologies. The imperfection of the existing security mechanisms coupled with the heightened and growing concerns for intrusion, attack, compromise or even failure owing to Byzantine failure are also contributing factors. The importance of  survivable distributed databases in the face of byzantine failure, to other emerging technologies is the motivation for this research. Furthermore, It has been observed that most of the existing works on distributed database only dwelled on maintaining data integrity and availability in the face of attack. There exist few on availability or survibability of distributed databases owing to internal factors such as internal sabotage or storage defects. In this paper, an architecture for entrenching survivability of Distributed Databases occasioned by Byzantine failures is proposed. The proposed architecture concept is based on re-creating data on failing database server based on a set  threshold value.The proposed architecture is tested and found to be capable of improving probability of survivability in distributed database where it is implemented to  99.6%  from 99.2%.

Awareness of Cassava Peel Utilization Forms among Cassava Processors in Rural Communities of Southwest, Nigeria

Significant quantities of generated peels are been thrown on dumping sites in southwest, Nigeria thereby constituting a source of environmental pollution. This study assessed the awareness of cassava peel utilization forms among cassava processors in rural communities of southwest, Nigeria. Interview guide was used to elicit information from 200 cassava processors through a multistage sampling technique. Data were analyzed with both descriptive and inferential statistics. Result revealed that majority of the cassava processors were women (76.5%), married (75.0%), and members of cassava processing associations (89.5%) with 73.0% having at least primary education. The mean age and cassava processing experience were 53.01 and 22.76 years respectively. About 23.0% of the processed cassava tubers constituted peels. The study also revealed that 50% of the cassava processors discarded peels as waste, 26% sold generated peels while 24% fed the peels to their livestock. Also 93.5% of the cassava processors were not aware of any cassava peel utilization technology. Chi-square analysis revealed that significant association existed between existing practice on cassava peel utilization (χ2=17.341, p<0.05) and cassava processors’ awareness of cassava peel utilization forms. The study concluded that substantial quantity of peel been generated is discarded as waste due to lack of awareness of cassava peel utilization technologies in the study areas. The study therefore recommended that improved technologies on the utilization of cassava peel should be popularized through result demonstration among cassava processors in southwest, Nigeria

Food security risk level assessment : a fuzzy logic-based approach

A fuzzy logic (FL)-based food security risk level assessment system is designed and is presented in this article. Three inputs—yield, production, and economic growth—are used to predict the level of risk associated with food supply. A number of previous studies have related food supply with risk assessment for particular types of food, but none of the work was specifically concerned with how the wider food chain might be affected. The system we describe here uses the Mamdani method. The resulting system can assess risk level against three grades: severe, acceptable, and good. The method is tested with UK (United Kingdom) cereal data for the period from 1988 to 2008. The approach is discussed on the basis that it could be used as a starting point in developing tools that may either assess current food security risk or predict periods or regions of impending pressure on food supply

Towards Building Secure Software Systems

Software security breaches are now very extremely common and a larger percentage is caused by software design defects. Since individuals and organizations now completely depend on software systems for their day-to-day operations, it is then important to produce secure software products. This paper discusses the problems of producing secure software products and provides a model for improving software security. The model – Secure Software Development Model (SSDM), is unified model that integrates security engineering with software engineering so as to ensure effective production of secure software products. Supporting structure in form of laws is also presented to guide developers throughout the development process. We then present our experience that validates the model

A hybrid approach to masquerade detection

Masquerade attack, which occurs when an intruder assumes the identity of a legitimate user, has become a serious security challenge to several organizations. Several algorithms have been proposed to tackle this attack and sequence alignment algorithms are the most widely proposed by researchers. The general approach in sequence alignment algorithm is to create user models by analyzing past usage patterns of legitimate users and comparing them with the current session. Many algorithms in use today are still quite not efficient because they have low hit ratio and they do not excellently combine the jobs of both detecting and comparing patterns. In this work, a hybrid approach that combined Naïve Bayes and Semi-Global alignment (Nab-Sem) for efficient masquerade detection is proposed. The purpose of this work is to separate the user modeling and session comparing tasks for better performance. Naïve Bayes was used to recognize patterns in the users’ blocks and Semi-global alignment was used to compare a test block to the user generated pattern. This work was implemented using Microsoft Visual C# and tested using a systematically generated ASCII coded sequence used to represent simulations for standard intrusion and non-intrusion data. The result shows an increase in the detection accuracy from 66.2% using Naïve Bayes, 68.2% using Semiglobal alignment to 93% using Nab-Sem. This reveals an improved approach to masquerade detection.Keywords: Masquerade attack, Sequence alignment, Intrusion, Semi-global alignment, Pattern marching

Packets’ Congestion Management with Fuzzy Admission Control Policy for Differentiated Service Networks

This study presents a priority-based admission control system for ensuring stability in a differentiated service network using a fuzzified admission policy. Packets of varying sizes of data were transmitted from N sources through a traffic conditioner which categorizes the packets into two independent sources, consequently classifying them into “high” and “low” priorities. While class A packets are not denied admission into the buffer, this is not the case with packets of class B. Arrivals from both sources follow a Poisson distribution process of$\alpha$(k) = ($\lambda$ /k!)e -$\lambda$ . It is assumed that r > h / $\mu$   in order to avoid a situation in which a class B arrival is denied admission while the system is empty. The arrival rates  $\lambda$ 1 $\in$ [02$\mu$ ) and $\lambda$2 $\in$ [02 $\alpha$) serve as fuzzy inputs with four linguistic values while the output is a decision, d. Simulation started with an initial state, zero and system performance for the first 300time units was monitored. Results indicate that the admission controller admits arriving class B packets provided that the value of y ≤ 4 while it denied admission to arriving class B packets when y > 4, thus giving a threshold policy of y=4 . Arrivals denied admission are dropped and transmitted to the “tree manager” via the bottleneck router. These packets are arranged as nodes in an AVL tree structure which adopts tree properties to manage and transmit nodes to the buffer based on node rotations