A fuzzy semantic information retrieval system for transactional applications

In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users

An architectural-based approach to detecting spim in electronic means of communication

Spams are what users and developers should be aware of in all Internet-based communication tools (such as e-mail, websites, Social Networking Sites (SNS), instant messengers and so on). This is because spammers have not ceased from using these platforms to deceive and lure users into releasing vibrant and sensitive information (especially, financial details). This paper developed an architectural based technique for SPIM (Instant Message Spam or IM SPAM) detection using the classification method. The classification was done using the C4.5 classifier with a dataset of messages gotten from an instant messaging environment. The dataset served as the input to the classification algorithm method which was able to distinguish spam from non-spam messages. This classification method was depicted in a tree form to show its usefulness. The results show that its precision, recall and accuracy rate satisfied standard recommendation with a commendable error rate. The proposed technique will find implication in the reduction of the number of Internet users.Keywords: Social Networking sites, spammers, Instant message spam, C4.5 Classifiers, e-mails

Congestion Control in Packet Switched Wide Area Networks using a Feedback Model

ABSTRAC

A MODEL FOR PREVENTIVE CONGESTION CONTROL MECHANISM IN ATM NETWORKS

Maximizing bandwidth utilization and providing performance guarantees, in the context of multimedia networking, are two incompatible goals. Heterogeneity of the multimedia sources calls for effective traffic control schemes to satisfy their diverse Quality of Service (Qos) requirement. These include admission control at connection set up, traffic control at the source end and efficient scheduling schemes at the switches. The emphasis in this paper is on traffic control at both connection set up and source end. A model for the Connection Admission Control (CAC) is proposed using probabilistic technique. Mathematical formulas are derived Cell Loss Probability (CLP), violation probability (PV) and cell throughput (TC). The performances at two UPC models (fluid flow and approximation) are investigated using the leaky bucket (LB) algorithm. The CLP, PV, and TC performed for different traffic sources which are characterized by their mean bit rate, peak bit rate and average number of bits generated during the burst. The results of the simulation show that the model for the Connection Admission Control (CAC) performs satisfactorily well for different traffic sources. Also, both models for the leaky bucket are almost coincident in policing the peak rate and mean rate of the source. Hence, policing effect is improved considerably using the proposed model

System Simulation of a Bayesian Network-Based Performance Prediction Model for Data Communication Networks

In this paper, a paradigm of a Bayesian Network–based performance prediction model for computer networks security risk management was emulated. Model simulation was carried out for the prediction model formulated. Java programming language tools were used to simulate, validate and verify the model. The core of simulation program was written in Java programming language. Some jar files were created in the code logic for all the modules in the prediction model. MS-DOS or command prompt was used to compile and run java and jar files. Batch scripts i.e. .bat files were written to compile the jar files. The output of the execution is shown using Java API files. Simulation technology was used in this study to evaluate network performance since it is very costly to deploy a complete test bed containing multiple networked computers, routers and data links to validate and verify the prediction model. The resulting risk impact on network confidentiality, Integrity and availability determine the criticality of the overall network performance which will aid in the effective application of countermeasures to mitigate the effect of network security risks

An Improved Anomalous Intrusion Detection Model

The volume of cyber-attack targeting network resources within the cyberspace is steadily increasing and evolving. Network intrusions compromise the confidentiality, integrity or availability of network resources causing reputational damage and the consequential financial loss. One of the key cyber-defense tools against these attacks is the Intrusion Detection System. Existing anomalous intrusion detection models often misclassified normal network traffics as attacks while minority attacks go undetected due to an extreme imbalance in network traffic data. This leads to a high false positive and low detection rate. This study focused on improving the detection accuracy by addressing the class imbalanced problem which is often associated with network traffic dataset. Live network traffic packets were collected within the test case environment with Wireshark during normal network activities, Syncflood attack, slowhttppost attack and exploitation of known vulnerabilities on a targeted machine. Fifty-two features including forty-two features similar to Knowledge Discovery in Database (KDD ’99) intrusion detection dataset were extracted from the packet meta-data using Spleen tool. The features were normalized with min-max normalization algorithm and Information Gain algorithm was used to select the best discriminatory features from the feature space. An anomalous intrusion detection model was formulated by a cascade of k-means clustering algorithm and random-forest classifier. The proposed model was simulated and its performance was evaluated using detection accuracy, sensitivity, and specificity as metrics. The result of the evaluation showed 10% higher detection accuracy, 29% sensitivity, and 0.2% specificity than the existing model. Keywords— anomalous, cyber-attack, Detection, Intrusio

Yield gains in extra-early maize cultivars of three breeding eras under multiple environments

Open Access JournalAvailability of extra-early maize cultivars has facilitated the expansion of maize production into savannas of West and Central Africa (WCA). Fifty-six extra-early maize cultivars of three breeding eras;1995 to 2000, 2001 to 2006, and 2007 to 2012 were evaluated for 2 yr under 24 multiple-stress and 28 non-stress environments in WCA. Objectives of the study were to determine genetic improvement in grain yield of cultivars developed during the breeding eras, and identify high-yielding and s multiple-stress and non-stress environments. Yield gains from era 1 to era 3 under multiple stresses was associated with increased days to anthesis, reduced stalk lodging, and improved husk cover. Cultivars 2004 TZEE-Y Pop STR C4, TZEE-W Pop STR QPM C0, and TZEE-W Pop STR BC2 C0 of era 2; and TZEE-W STR 107 BC1, TZEE-W Pop STR C5, and 2012 TZEE-Y DT STR C5 of era 3 were high-yielding and stable across multiple-stress environments while 98 Syn EE-W from era 1, FERKE TZEE-W STR, TZEE-W Pop STR C3, and TZEE-Y Pop STR QPM C0 from era 2, and TZEE-W Pop STR C5, 2009 TZEE-OR2 STR QPM, 2009 TZEE-W STR, TZEE-Y STR 106, and TZEE-W DT C0 STR C5 from era 3 were outstanding across non-stress environments and should be tested extensively and commercialized. Considerable improvement has been made in breeding for multiple-stress tolerant extra-early maize cultivars