Software reliability growth models (SRGMs) are very important for software reliability estimation and prediction and have been successfully applied in the critical airborne software. However, there is no general model...
详细信息
Software reliability growth models (SRGMs) are very important for software reliability estimation and prediction and have been successfully applied in the critical airborne software. However, there is no general model which can perform well for different cases. Thus, some researchers proposed to obtain more accurate estimation and prediction than one single model by combining various individual SRGMs together. adaboosting is a commonly used machine learning algorithm for combining several weak predictors into a single strong predictor to significantly improve the estimating and forecasting accuracy, which may be very suitable for the combination of SRGMs. Hence, two novel adaboosting-based combination approaches for improving the parametric SRGMs are presented in this paper. The first one selects several variations of one original SRGM for obtaining the self-combination model (ASCM). The second selects several various candidate SRGMs for obtaining the multi-combinational model (AMCM). Finally, two case studies are presented and the results show that: (1) the ASCM is fairly effective and applicable for improving the estimation and prediction performance of its corresponding original SRGM without adding any other factors and assumptions;(2) the AMCM is notably effective and applicable for combining SRGMs because it has well applicability and provides a significantly better reliability estimation and prediction power than the traditional SRGMs and also yields a better estimation and prediction power than the neural-network-based combinational model. Copyright (C) 2011 John Wiley & Sons, Ltd.
From a security perspective, the research of the jeopardized 6G wireless communications and its expected ultra-densified ubiquitous wireless networks urge the development of a robust intrusion detection system (IDS) w...
详细信息
From a security perspective, the research of the jeopardized 6G wireless communications and its expected ultra-densified ubiquitous wireless networks urge the development of a robust intrusion detection system (IDS) with powerful capabilities which could not be sufficiently provided by the existing conventional systems. IDSs are still insufficient against continuous renewable unknown attacks on the wireless communication networks, especially with the new highly vulnerable networks, leading to low accuracy and detection rate with high (false-negative and false-positive) rates. To this end, this paper proposed a novel anomaly detection in communication networks by using an ensemble learning (EL) algorithm-based anomaly detection in communication networks (ADCNs). EL-ADCNs consists of four main stages;the first stage is the preprocessing steps. The feature selection method is the second stage. It adopts the proposed hybrid method using correlation with the random forest algorithm of ensemble learning (CFS-RF). It reduces dimensionality and retrieves the best subset feature of all the three datasets (NSL_KDD, UNSW_NB2015, and CIC_IDS2017) separately. The third stage is using hybrid EL algorithms to detect intrusions. It involves modifying two classifiers (i.e., random forest (RF), and support vector machine (SVM)) to apply them as adaboosting and bagging EL algorithms;using the voting average technique as an aggregation process. The final stage is testing the proposal using binary and multi-class classification forms. The experimental results of applying 30, 35, and 40 features of the proposed system to the three datasets achieved the best results of a 99.6% accuracy with a 0.004 false-alarm rate for NSL_KDD, a 99.1% accuracy with a 0.008 false-alarm rate for UNSW_NB2015, and a 99.4% accuracy with a 0.0012 false-alarm rate for CIC_IDS2017.
暂无评论