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Although customization plays a significant role in the provision of software as a service (SaaS), delivering a customizable SaaS application that reflects the tenant's specific requirements with acceptable level of quality is a challenge. Drawing on a pr-developed software customization model for SaaS quality, two fundamental objectives of this study were to determine whether different software customization approaches have direct impacts on SaaS quality, and also to assess the construct reliability and construct validity of the model. A questionnaire-based survey was used to collect data from 244 software professionals with experience in SaaS development. Structural equation modeling was employed to test the construct reliability, construct validity, and research hypotheses. The measurement model assessment suggested that the six-construct model with 39 items exhibited good construct reliability and construct validity. The findings of the structural model assessment show that all customization approaches other than the integration approach significantly influence the quality of SaaS applications. The findings also indicate that both configuration and composition approaches have positive impacts on SaaS quality, while the impacts of the other approaches are negative. The empirical assessment and evaluation of this model, which features a rich set of information, provides considerable benefits to both researchers and practitioners. [ABSTRACT FROM AUTHOR]

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT. [ABSTRACT FROM AUTHOR]

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature. [ABSTRACT FROM AUTHOR]