Due to increasing incidents of cyber-attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. However, most of the conducted studies rely on static and one-time dataset where all the changes monitored are based on the dataset used. As network behaviors and patterns change and intrusions evolve, thus it has very much become necessary to move away from static and one-time dataset toward more dynamically configurable classifiers. The Current researches show that different classifiers provide different results about the patterns to be classified. These different results combined together (aka ensemble) yields better performance than individual classifiers. In this paper we have used a hybrid ensemble intrusion detection system consisting of a Misuse Binary Tree of Classifiers as the first stage and an anomaly detection model based upon SVM Classifier as the second stage. The Binary Tree consists of several best known classifiers specialized in detecting specific attacks at a high level of accuracy. Combination of a Binary Tree and specialized classifiers will increase accuracy of the misuse detection model. The misuse detection model will detect only known attacks. In-order to detect unknown attacks, we have an anomaly detection model as the second stage. SVM has been used, since it's the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior. The proposed hybrid intrusion detection has been tested and evaluated using KDD Cup '99, NSL-KDD and UNSW-NB15 datasets.

Thick data analytics are being pursued to break the barriers of using the big data predictive analytics for small datasets. The main objective of this paper is to improve the performance of the EEG for biometric authentication using eye blinking brain signals through the use of ensembles techniques. Biometric identification differs largly from the other EEG eye movement analytics applications such as detecting epileptic seizure, identification of stress feature or detecting driving drowsiness as it requires high model rubstness and accuracy. A perfect biometric should be unique, universal and permanent over time. Previous analytical approaches on eye movement failed to show the reliability of the the brain signals to distinguish individuals based on the properties of eye-movements seen as time-signals and for this reason the eye movement have not been considered as a possible solution for a biometric system. This paper's primary focus is on the use of ensemble methods to secure the robustness of the person identification from the EEG eye movement waves. Our approach is a multitier one and it start with training notable binary classification models for biometic identification using eye movement. The training tier is followed by ensemble learning (boosting, bagging, and stacking algorithms) to narrow the differences of accuracy gap among classifiers. The classifier's robustness has been measured with the help of variety of accuracy measures including the Matthews correlation coefficient (MCC). The third tier is guage the person prediction model stability using the AUROC (Area Under the Receiver Operating Characteristics) metric. The results obtained in this study proves that it is possible to use an eye tracking based biometric for detection of person identity with reasonably high sensitivity and specificity.