Faculty of Computing

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    PublicationOpen Access
    A comparative study of data mining algorithms in the prediction of auto insurance claims
    (European International Journal of Science and Technology, 2016) Weerasinghe, K. P. M. L. P; Wijegunasekara, M. C
    Insurance claims are a significant and costly problem for insurance companies. The prediction of auto insurance claims has been a challenging research problem for many auto insurance companies. Identifying the risk factors which are affected for the high number of claims and denying them may lead to increased corporate profitability and keep insurance premiums at a below rate. The key objective of conducting this study is to examine the data mining techniques in developing a predictive model in support of auto insurance claim prediction and a comparative study of them. The research was carried out by using Artificial Neural Network (ANN), Decision Tree (DT) and Multinomial Logistic Regression (MLR) to develop the prediction model. The results indicated that the ANN is the best predictor with 61.71% overall classifier accuracy. Decision tree came out to be the second with 57.05% accuracy and the logistic regression model indicated 52.39% accuracy. Parameters of optimal NN model gives 6 input neurons and 7 minimum hidden neurons with 0.15 learning rate. The comparative study of multiple prediction models provided us with an insight into the relative prediction ability of different data mining methods. The comparison of the results of the decision tree and neural network models showed an interesting pattern. Policies that are misclassified by one model are correctly classified by the other. This might be an indication that the combination of the models could result in a better classification performance.
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    PublicationEmbargo
    Autonomous Cyber AI for Anomaly Detection
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Madhuvantha, K.A.N.; Hussain, M.H.; De Silva, H.W.D.T.; Liyanage, U.I.D.; Rupasinghe, L.; Liyanapathirana, C.
    Since available signature-based Intrusion Detection systems (IDS) are lacking in performance to identify such cyber threats and defend against novel attacks. It does not have the ability to detect zero-day or advanced malicious activities. To address the issue with signature-based IDS, a possible solution is to adopt anomaly-based detections to identify the latest cyber threats including zero days. We initially focused on network intrusions. This research paper discusses detecting network anomalies using AIbased technologies such as machine learning (ML) and natural language processing (NLP). In the proposed solution, network traffic logs and HTTP traffic data are taken as inputs using a mechanism called beats. Once relevant data has been extracted from the captured traffic, it will be passed to the AI engine to conduct further analysis. Algorithms such as Word2vec, Convolution Neural Network (CNN), Artificial Neural networks (ANN), and autoencoders are used in order to conduct the threat analysis. HTTP DATASET CSIC 2010, that NSL-KDD, CICIDS are the benchmarking datasets used in parallel with the above algorithms in order to receive high accuracy in detection. The outputted data is integrated and visualized using the Kibana dashboard and blockchain model is implemented to maintain and handle all the data.