Faculty of Science
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To provide the platform of high academic standard in both research and learning in Science. To be the pace-setting Faculty of Science in Nigeria and beyond in producing excellent graduates in research, learning and character.
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Browsing Faculty of Science by Author "Abass, O."
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- ItemOpen AccessApplication of the ARIMA Models for Predicting Students’ Admissions in the University of Lagos(Journal of Scientific Research and Development, 2017) Onyeka-Ubaka, J.N.; Agwuegbo, S.O.N.; Abass, O.The objective was to assess the performance of the AutoRegressive Integrated Moving Average (ARIMA) models when occasional level shifts occur in the time series under study. The secondary data on the University of Lagos’ undergraduates’ admissions (1962–2016) were collected and analysed. It is predicted that universities in Nigeria and elsewhere could forecast their enrolment figures and student population growth rate based on the ARIMA models. The Box–Jenkins (B–J) approach provided the theoretical framework for the statistical analysis. The study used the Kalman Filter (KF) algorithm to develop a method using an ARIMA model to overcome and resolve the three main problems of the B–J methodology. The KF estimated the states for dynamic systems in state-variable formulations. Forecasting university admissions is necessary if student population must match the infrastructural provisions on campuses. The best ARIMA models have been selected by using criteria such as Akaike’s Information Criterion (AIC), Schwarz’s Bayesian Criterion (SBC), Absolute Mean Error (AME), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). To select the best ARIMA model, the data was split into two periods: estimation period and validation period. The results clearly showed a continual increase in the demand for university education in the University of Lagos and, by extension, other universities in Nigeria.
- ItemOpen AccessFORECASTING DISTRIBUTED DENIAL OF SERVICE ATTACK USING HIDDEN MARKOV MODEL(LAUTECH Journal of Engineering and Technology, 2016) Afolorunso, A.A.; Abass, O.; Longe, H. O. D.; Adewole, A.P.consumes the system resources in terms of time, memory, and processors. This paper presents a proposed method for forecasting DDoS in networks. The proposed model employs hidden Markov model (HMM) to forecast DDoS attacks. The method uses the inherent characteristic features of DDoS to determine the observable states of the system.To avoid intractable computations, Kullback-Leibler divergence algorithm was employed to reduce the number of observable states to three. The proposed model is formulated and trained through experiments using DARPA 2000 data set and the preliminary resultsshows that the characteristic features of the DDoS and the entropy concept can be used to formulate an HMM to predict DDoS.
- ItemOpen AccessReducing the Observable States Space of Hidden Markov Model for Distributed Denial of Service Attack Prediction using Kullback-liebler Divergence.(UNILAG Journal of Medicine, Science and Technology, 2017) Afolorunso, A.A.; Adewole, A.P.; Abass, O.; Longe, H. O. D.Distributed Denial of Service (DDoS) attack floods the network with loads of unwanted packets and requests that weigh down the system resources such as memory and processors. Hidden Markov model (HMM) is one of the models that can be used to predict and detect such attacks. A problem to be solved was determining the observable states and subsequently, the model parameters since the performance of the model depends on the accurate selection of these parameters. In this work, the concept of entropy was used to determine the observable states, which characterise the HMM. In order to improve computational efficiency of the algorithm for estimating the parameters of the model, Kullback-Liebler Divergence (KLD) method was employed for reducing and selecting appropriate observable states to achieve a good prediction model. The experimental results justified the suitability of KLD in reducing the entropy-based observable states of HMM for predicting DDoS attack.