Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flexibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with substantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on organizations, enterprises, and even economies. Accuracy, high performance, and real-time systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System (IDS) has stimulated the interest of numerous research investigators over the last decade. In this paper, a novel HFS-LGBM IDS is proposed for SDN. First, the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset. In the first phase, the Correlation based Feature Selection (CFS) algorithm is used to obtain the feature subset. The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination (RF-RFE) in the second phase. A LightGBM algorithm is then used to detect and classify different types of attacks. The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy, precision, recall and f-measure.

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This paper considers distance adaptive dynamic routing and spectrum assignment (RSA) for elastic optical networks with shared backup path protection (SBPP). Efficient heuristic algorithms based on spectrum window planes are proposed for implementing distance and modulation format adaptive RSA so as to maximize spare capacity sharing among multiple protection lightpaths. A differentiated sharable frequency slot cost was also defined for the first time to more efficiently share protection resource. Network performance is evaluated in terms of bandwidth blocking probability (BBP) through simulations. The proposed SBPP technique is effective in reducing BBP and improving spectral efficiency compared to conventional SBPP schemes and 1+1 path protection. The impact of transponder tunability on bandwidth blocking performance is also evaluated to show that a limited tuning range is sufficient to achieve a BBP performance close to that with full tunability.

To cope with the explosive growth of the Internet, data centers, which primarily serve as its cloud computing infrastructures, are also growing at a fast rate. These data centers typically serve both real-time delay-sensitive jobs and deferrable but deadline-oriented jobs. They exhibit two fundamental properties, which make them particularly suitable for implementing a Demand Response (DR) strategy. First, they are big consumers of electricity, and second, they deal with time-flexible loads. We consider a data center where the various diverse services are deployed in different tiers to capture some of the intricacies of real operations focusing on deferrable jobs. To tackle resource provisioning for deferrable jobs, we leverage Receding Horizon Control (RHC) based scheduling. We formulate and solve the optimal server provisioning problem in two different ways, namely, minimization of operational cost and joint minimization of capital and operational cost, for server provisioning at different tiers, based on a discrete-time model. We then propose and evaluate a simple and effective mechanism to facilitate more renewable integration into the grid through providing monetary incentives on deadline deferral. We show that our proposed method achieves significantly good performance with respect to various important performance parameters of the system.