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In this modern era, due to demand for cloud environments in business, the size, complexity, and chance of attacks to virtual cloud network (VCN) are increased. The protection of VCN is required to maintain the faith of the cloud users. Intrusion detection is essential to secure any network. The existing approaches that use the conventional neural network cannot utilize all information for identifying the intrusions. In this paper, the anomaly-based NIDS for VCN is proposed. For feature selection, grey wolf optimization (GWO) is hybridized with a bald eagle search (BES) algorithm. For classification, a deep learning approach - deep sparse auto-encoder (DSAE) is employed. In this way, this paper proposes a NIDS model for VCN named - GWO-DES-DSAE. The proposed system is simulated in the python programming environment. The proposed NIDS model's performance is compared with other recent approaches for both binary and multi-class classification on the considered datasets - NSL-KDD, UNSW-NB15, and CICIDS 2017 and found better than other methods.

Internet of Things will serve communities across the different domains of life. The resource of embedded devices and objects working under IoT implementation are constrained in wireless networks. Thus, building a scheme to make full use of energy is key issue for such networks. To achieve energy efficiency, an effective Fuzzy-based network data Fusion Light Weight Protocol (FLWP) is proposed in this article. The innovations of FLWP are as follows: 1) the simulated network's data fusion through fuzzy controller and optimize the energy efficiency of smart tech layer of internet of things (Energy IoT); 2) The optimized reactive route is dynamically adjusted based on fuzzy based prediction accurately from the number of routes provided by base protocol. If the selection accuracy is high, the performance enhances the network quality; 3) FLWP takes full advantage of energy to further enhance target tracking performance by properly selecting reactive routes in the network. Authors evaluated the efficiency of FLWP with simulation-based experiments. FLWP scheme improves the energy efficiency.