HANDOVER MANAGEABILITY AND PERFORMANCE MODELING IN MOBILE COMMUNICATION NETWORKS

In cellular Networks, a mobile station (MS’s) move from one cell region to another on seamless Communicationscheduling.. Handoff or Handover is an essential issue for the seamless communication. Several approaches havebeen proposed for handoff performance analysis in mobile communication systems. In Code-Division Multiple-Access (CDMA) systems with soft handoff, mobile stations (MS’s) within a soft-handoff region (SR) use multipleradio channels and receive their signals from multiple base stations (BS’s) simultaneously. Consequently, SR’sshould be investigated for handoff analysis in CDMA systems. In this paper, a model for soft handoff in CDMAnetworks is developed by initiating an overlap region between adjacent cells facilitating the derivation of handoffmanageability performance model. We employed an empirical modelling approach to support our analyticalfindings, measure and investigated the performance characteristics of typical communication network over a specificperiod from March to June, 2013 in an established cellular communication network operator in Nigeria. Theobserved data parameters were used as model predictors during the simulation phase. Simulation results revealedthat increased system capacity degrades the performance of the network due to congestion, dropping and callblocking, which the system is most likely to experience, but the rate of those factors could be minimized by properlyconsidering the handoff probabilities level. Comparing our results, we determined the effective and efficientperformance model and recommend it to network operators for an enhanced Quality of Service (QoS), which willpotentially improve the cost-value ratio for mobile users and thus confirmed that Soft Handoff (SH) performancemodel should be carefully implemented to minimize cellular communication system defects.Keywords: CDMA, QoS, optimization, Handoff Manageability, Congestion, Call Blocking and Call Dropping,

CLASSIFICATION OF CYBERSECURITY INCIDENTS IN NIGERIA USING MACHINE LEARNING METHODS

Cybercrime has become more likely as a result of technological advancements and increased use of the internet and computer systems. As a result, there is an urgent need to develop effective methods of dealing with these cyber threats or incidents to identify and combat the associated cybercrimes in Nigerian cyberspace adequately. It is therefore desirable to build models that will enable the Nigeria Computer Emergency Response Team (ngCERT) and law enforcement agencies to gain valuable knowledge of insights from the available data to detect, identify and efficiently classify the most prevalent cyber incidents within Nigeria cyberspace, and predict future threats. This study applied machine learning methods to study and understand cybercrime incidents or threats recorded by ngCERT to build models that will characterize cybercrime incidents in Nigeria and classify cybersecurity incidents by mode of attacks and identify the most prevalent incidents within Nigerian cyberspace. Seven different machine learning methods were used to build the classification and prediction models. The Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (CART) and Random Forest (RF) Algorithms were used to discover the relationship between the relevant attributes of the datasets then classify the threats into several categories. The RF, CART, and KNN models were shown to be the most effective in classifying our data with accuracy score of 99%  each while others has accuracy scores of 98% for SVM, 89% for NB, 88% for LR, and 88% for LDA. Therefore, the result of our classification will help organizations in Nigeria to be able to understand the threats that could affect their assets

CLASSIFICATION OF CYBERSECURITY INCIDENTS IN NIGERIA USING MACHINE LEARNING METHODS

Cybercrime has become more likely as a result of technological advancements and increased use of the internet and computer systems. As a result, there is an urgent need to develop effective methods of dealing with these cyber threats or incidents to identify and combat the associated cybercrimes in Nigerian cyberspace adequately. It is therefore desirable to build models that will enable the Nigeria Computer Emergency Response Team (ngCERT) and law enforcement agencies to gain valuable knowledge of insights from the available data to detect, identify and efficiently classify the most prevalent cyber incidents within Nigeria cyberspace, and predict future threats. This study applied machine learning methods to study and understand cybercrime incidents or threats recorded by ngCERT to build models that will characterize cybercrime incidents in Nigeria and classify cybersecurity incidents by mode of attacks and identify the most prevalent incidents within Nigerian cyberspace. Seven different machine learning methods were used to build the classification and prediction models. The Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (CART) and Random Forest (RF) Algorithms were used to discover the relationship between the relevant attributes of the datasets then classify the threats into several categories. The RF, CART, and KNN models were shown to be the most effective in classifying our data with accuracy score of 99%  each while others has accuracy scores of 98% for SVM, 89% for NB, 88% for LR, and 88% for LDA. Therefore, the result of our classification will help organizations in Nigeria to be able to understand the threats that could affect their assets

An Efficient Clustering System for the Measure of Page (Document) Authoritativeness

A collection of documents D1 of a search result R1 is a cluster if all the documents in D1 are similar in a way and dissimilar to another collection say D2 for a given query Q1. Implying that, given a new query Q2, the search result R2 may pose an intersection or a union of documents from D1 and D2 or more to form D3. However within these collections say D1, D2, D3 etc, one or two pages certainly would be better in relevance to the query that invokes them. Such a page is regarded being ‘authoritative’ than others. Therefore in a query context, a given search result has pages of authority. The most important measure of a search engine’s efficiency is the quality of its search results. This work seeks to cluster search results to ease the matching of searched documents with user’s need by attaching a page authority value (pav). We developed a classifier that falls in the margin of supervised and unsupervised learning which would be computationally feasible and producing most authoritative pages. A novel searching and clustering engine was developed using several measure-factors such as anchor text, proximity, page rank, and features of neighbors to rate the pages so searched. Documents or corpora of known measures from the Text Retrieval Conference (TREC), the Initiative for the Evaluation of XML Retrieval (INEX) and Reuter’s Collection, were fed into our work and evaluated comparatively with existing search engines (Google, VIVISIMO and Wikipedia). We got very impressive results based on our evaluation. Additionally, our system could add a value – pav to every searched and classified page to indicate a page’s relevance over the other. A document is a good match to a query if the document model is likely to generate the query, which will in turn happen if the document contains the query words often. This approach thus provides a different realization of some of the basic ideas for document ranking which could be applied through some acceptable rules: number of occurrence, document zone and relevance measures. The biggest problem facing users of web search engines today is the quality of the results they get back. While the results are often amusing and expand users' horizons, they are often frustrating and consume precious time. We have made available a better page ranker that do not depend heavily on the page developer’s inflicted weights but considers the actual factors within and without the target page. Though very experimental on research collections, the user can within the collection of the first ten search results listing, extract his or her relevant pages with ease. Keywords: page Authoritativeness, page Rank, search results, clustering algorithm, web crawling

A Hierarchical Clustering Approach for the Creation of a Simple Semantic Web Application

The goal of the Semantic Web is to develop enabling standards and technologies designed to receive more exact results when searching for information, and to help machines understand more information on the Web so that they can support richer discovery, data integration and navigation. This can be achieved if there is a common vocabulary for a set of domains. Information is published using standard vocabulary. This study explores the processes of creating a taxonomy for a set of journal articles using hierarchical clustering algorithm. 100 journal articles that cut across different fields were downloaded from the internet. These served as sample data. These journal articles were serialized, stemmed and tokenized. Term frequency was calculated for each journal article.  Some representative terms were selected from each journal article and similarity matrix was generated for the entire journal articles. Complete hierarchical clustering was used to create a cluster of the articles. JavaTree view program was used to view the dendrogram of the cluster. It was observed that the articles cluster around their subject, subject area, field of study, area of application, journal type, author, place of case study. This demonstrated that journal articles have properties on a taxonomy, could be created as a basis for a semantic web. Keywords: Semantic web, clustering, taxonomy, similarity, document collection

Creation of Central National Database in Nigeria: Challenges and Prospects

This paper focuses on the creation of central national database based on Entity Relationship (ER) data model in order to describe each person individually and uniquely.  Also, the model establishes the use of the unique code (National Security code) as the primary key to identify each entity, entity relationship within and among public services entities.  Presently, Nigeria is critically facing security challenges because people are not uniquely identified and tagged.  The situation has led to insecurity in Nigeria with different trends and dimensions from one geopolitical zone to the other.  For instance, this has led to various faceless activities such as Islamic sect called Boko Haram in the North, rampant armed robbery in the South-West, notorious robbery and kidnapping in the South-East and South-South.  Corruption and unemployment are all over.  To curb the menace, a central national residency database must be created to solving identity challenge, loose neighboring borders, sectional terrorist, corruption, Crime and criminalities, unemployment, poverty and leadership distrust.  The paper recommends how the government can achieve central coordination of national security and attitudinal change of the country men despite the prevailing security challenge. Key-words: Central National Database, Data, National Security code, Entity Relationship (ER), Utilities, Residency code, Enforcement, National security, Attitudinal change JEL Classification: C82, M15, O3

Reign Mobile Application for Hotspot Detection

Reign mobile hotspot detection system is a cross platform mobile application developed to help warn its users of hostile areas (i.e., areas prone to accident, flooding, kidnapping, civil unrest, etc.). It also has functionalities that allow users to report hazardous areas through a preconfigured e-mail, which includes the users current location and a description of the hazard being reported. The goal of this project is to explore the use of mobile computing, by means of mobile apps, to address some of the social and developmental challenges being experienced in Nigeria. Thus, we could adapt technology to improve social conditions as well as, possibly, save lives. The motivation for this project is the ubiquity of mobile computing, particularly when we consider that Nigeria with a population of over 140 million people is currently estimated to have a mobile broadband Internet penetration equivalent of about 30%. These users mostly connect through mobile devices with at least 100million unique mobile communication lines registered. The app was developed with HTML5 and JAVA programming languages, uses GPS coordinates to map locations and a push server to send alerts to registered users. Currently, the Android version of the app has been developed and is being tested. During the development and testing, we interacted with security and paramilitary institutions  like the Police, Federal Road Safety Service (FRSC) and the Nigerian Metrological Agency (NIMET) in order to ascertain areas that are prone to hazards. Preliminary tests in Lagos and Abuja confirm the functionality and usefulness of the app. Keywords: Mobile computing, hotspot detection, security hazards, crime detection and prevention, alerts

Pseudomonas cyclic lipopeptides suppress the rice blast fungus Magnaporthe oryzae by induced resistance and direct antagonism

Beneficial Pseudomonas spp. produce an array of antimicrobial secondary metabolites such as cyclic lipopeptides (CLPs). We investigated the capacity of CLP-producing Pseudomonas strains and their crude CLP extracts to control rice blast caused by Magnaporthe oryzae, both in a direct manner and via induced systemic resistance (ISR). In planta biocontrol assays showed that lokisin-, white line inducing principle (WLIP)-, entolysin- and N3-producing strains successfully induced resistance to M. oryzae VT5M1. Furthermore, crude extracts of lokisin, WLIP and entolysin gave similar ISR results when tested in planta. In contrast, a xantholysin-producing strain and crude extracts of N3, xantholysin and orfamide did not induce resistance against the rice blast disease. The role of WLIP in triggering ISR was further confirmed by using WLIP-deficient mutants. The severity of rice blast disease was significantly reduced when M. oryzae spores were pre-treated with crude extracts of N3, lokisin, WLIP, entolysin or orfamide prior to inoculation. In vitro microscopic assays further revealed the capacity of crude N3, lokisin, WLIP, entolysin, xantholysin and orfamide to significantly inhibit appressoria formation by M. oryzae. In addition, the lokisin and WLIP biosynthetic gene clusters in the producing strains are described. In short, our study demonstrates the biological activity of structurally diverse CLPs in the control of the rice blast disease caused by M. oryzae. Furthermore, we provide insight into the non-ribosomal peptide synthetase genes encoding the WLIP and lokisin biosynthetic machineries