Cybercrime Pervasiveness, Consequences, and Sustainable Counter Strategies

As our connectivity and dependency on technology increases, so does our vulnerability. Technology has provided not only new tools, but also new opportunities for criminals in the digital world. The abuse of new technologies has been threatening economic and Jinancial security and actually devastating the lives of affected indivicluals. In Nigeria, cybercrime has recorded mostly foregin-based individuals and organizations as victims thereby getting Nigeria ranked among the nations with notorious pemasiveness of high-tech crimes. Indeed, adequately formulating a strategy to contain the menace of cybercrime presents aformidable challenge to law enforcement. This paper x-rays noted instances of cybercrime pervasiveness, its devastating consequences, and up-to-date countermeasures in Nigeria It develops an enforceable/sustainable framework to determine how critical infrastructures are put at risk snd how law enforcement should react in responding to the threats

Data Mining Approach for Target Marketing SMEs in Nigeria

Presently, in addition to the lack of funds experienced by SMEs in Nigeria, the main challenges experienced by SMEs (Small and Medium Enterprises) in Nigeria has to do with them not being able to understand and the apply marketing concept. Also there are a lot of resources being wasted inmarketing campaign which does not target anybody in particular. This study is therefore focused on finding the target market of SMEs using the K-means clustering technique in combination with sentiment analysis, also known as opinion mining. The study also aims at recommending the best medium to market these SMEs as discovered from the output of the analysis. The result of this research will give positive direction to improving the profit of small and medium business through target marketing. Also, this study is not trivial because it will help to reduce marketing cost through target marketing. The study also helps to discover the opinion of the populace on the small andmedium business and the medium through which they can be targeted. This discovery will definitely form solid foundation for further marketing action and improve their profit.Keywords: Data Mining, K-means Clustering, Target Marketing, Sentiment Analysis, SME, Opinion Mining

Oral health knowledge and practice among traders in Ibadan

Background: Adopting good oral health practices such as regular tooth brushing and flossing; healthy nutritional habits and regular visits to the dentist, play a vital role in the wellbeing of an individual by preventing oral diseases such as dental caries and periodontal disease. Adequate knowledge of factors related to oral health may influence oral health practices leading to improved oral health.Objective: The objective of this study was to assess the oral health knowledge and practice of a group of adults in Ibadan, Nigeria to enable planning of oral health services.Method: A cross sectional survey involving 400 randomly selected traders at the Bola Ige Modern Market, Gbagi, Ibadan. A semi-structured intervieweradministered questionnaire was used to obtain information on sociodemographics, oral health knowledge and practice. Data was analyzed using descriptive statistics at p < 0.05.Results: The mean age of participants was 36.9 ±1.2, 37.0% had adequate knowledge that tooth decay was related to sweet food substances, but the majority (61.9%) consumed carbonated drinks at least once a week. Only 22.8% had ever visited a dentist in their life time and 5.8% in the last six months. Knowledge of who a dentist was associated with their level of education, with more participants who had tertiary education (83%) having greater knowledge (X2= 38.3, p < 0.001). About 70.3% of the study participants used a toothbrush and toothpaste to clean their teeth.Conclusion: There is poor oral health knowledge amongst adult Nigerians and they utilize oral health practices that do not engender good oral health.Keywords: Dental caries, Periodontal disease, Oral health awarenes

Inter-Decadal Nature of Rainfall Character Over Sudano-Sahel, North-West Nigeria

The study investigated the spatial pattern of decadal variations in annual rainfall amounts in the Sokoto-Rima River Basin, Northwestern Nigeria. Rainfall dataset which is available on high-resolution (0.5 x 0.5 degree) grids resolution from the Climatic Research Unit CRU TS 3.21 of the University of East Anglia, Norwich, United Kingdom was used for the period 1943-2012 for Bunza, Dakindari southwest of the study area, Gulma, Augi, northwest of the Sokoto-Rima basin, Goronyo, Galadi, northeast Maje and Dan-Dume stations southeast of the basin. Correlation of CRU TS dataset was performed with measured rainfall data (Yelwa climatic station) from Nigerian Meteorological Agency, using the Pearson Product Moment Correlation statistic at 0.05 significant levels. In general, the study found that periods of downward fluctuations in annual rainfall below mean values corresponded with period of rise in global temperature occasioned by anthropogenic greenhouse gases emission. The study revealed a significant decrease in the annual rainfall from late 1980 onwards.Key Word: Rainfall, decadal departure, River Basin, Rainfall variation. Climate Research Uni

Deep learning model for preicting multistage cyber attacks

The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel – with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.Keywords: Alert correlation, Cyberattack prediction, Cybersecurity, Deep learning, Cyberattacks, Supervised and Unsupervised LearningVol. 26 No 1, June 201