Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

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Software defect prediction has been one of the key areas of exploration in the domain of software quality. In this paper, we perform a systematic mapping to analyze all the software defect prediction literature available from 1995 to 2018 using a multi-stage process. A total of 156 studies are selected in the first step, and the final mapping is conducted based on these studies. The ability of a model to learn from data that does not come from the same project or organization will help organizations that do not have sufficient training data or are going to start work on new projects. The findings of this research are useful not only to the software engineering domain, but also to the empirical studies, which mainly focus on symmetry as they provide steps-by-steps solutions for questions raised in the article.

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