Department Of Computer Science

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https://ir.unilag.edu.ng/handle/123456789/3381
The overall philosophy of Computer Sciences degree programmes in the Faculty of Science is to produce graduates with fully integrated science knowledge so that they can have a sound background required to fully understand the theoretical base of Computer Science.

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Browsing Department Of Computer Science by Author "Afolorunso, A.A."

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  • Item
    Open Access
    FORECASTING 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.
  • Item
    Open Access
    Reducing 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.