In Data mining, Classification and prediction are the two very essential forms of data analysis. They are widely used for extracting models for describing important data classes. This paper aims in designing classifier models based on five different classification algorithms namely, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest and Support Vector Machines (SVM), to classify and predict patients with diabetes. These classifiers are experimented with 10 fold Cross Validation and their performances are evaluated by computing Accuracy, Precision, F-Score, Recall and ROC measure. The test experiment shows that the accuracy given by classifier models developed by using Decision Tree, KNN, Naïve Bayes, SVM and Random Forest are 73.82%, 71.65%, 76.30%, 65.10% and 68.74 % respectively. Similarly, their precisions and recall are 0.705, 0.552, 0.759, 0.424, 0.538 and 0.738, 0.763, 0.82, 0.651, 0.804 respectively. Thus, this study shows that the Naïve Bayes algorithm provides the better accuracy in predicting diabetes as compared to other techniques. And, the data set chosen for this study is “Pima Indian Diabetic Dataset” taken from University of California, Irvine (UCI) Repository of Machine Learning databases.

The maintenance of web server security is a daunting task today. Threats arise from hardware failures, software flaws, tentative probing and worst of all malicious attacks. Analysing server logs to detect suspicious activities is regarded as a key form of defence, however, their sheer size makes human log analysis challenging. Additionally, traditional intrusion detection systems rely on methods based on pattern-matching techniques which are not sustainable given the high rates at which new attack techniques are launched every day. The aim of this paper is to develop a proto-type intelligent log based intrusion detection system that can detect known and unknown intrusions automatically. Under a data mining framework, the intrusion detection system is trained with unsupervised learning algorithms specifically the k-means algorithm and the One Class SVM (Support Vector Machine) algorithm. The development of the prototype system is limited to machine generated logs due to lack of real access log files. However, the system’s development and implementation proved to be up to 85% accurate in detecting anomalous log patterns within the test logs.