A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer. Moreover, this network is threatened by attacks like vampire attack where the network is loaded by fake traffic. Here, Dual Encoding Recurrent Neural network (DERNNet) is proposed for classifying the vampire nodes s node in the network. Moreover, the Grey Wolf Optimization (GWO) algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points; thereby maximizing battery/ lifetime of the network nodes. The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing (KIDSASR), Risk-aware Reputation-based Trust (RaRTrust) model and Activation Function-based Trusted Neighbor Selection (AF-TNS) in terms of various parameters. These existing methods may lead to wastage of energy due to vampire attack, which further reduce the lifetime and increase average energy consumed in the network. Hence, the proposed DERNNet method achieves 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 92.4% of packet delivery ratio, 85.2% of network lifetime, and 94.3% of classification accuracy.

One of the effective communication technology is wireless sensor network technology which helps to monitor the surrounding information by sensed nodes. The effective utilization of sensed nodes is utilized in different applications such as military, health information, environmental monitoring, disaster relief and target analyze. The application requires the collection of information which may be collected from one location and transferred to the other location for making their process so easier. During the information transformation process, the network may affect by several intermediate attack, in which denial of service is one of the serious attack because it affects the entire network resources such as network energy, power, bandwidth. The unavailability of the resources reduces the entire sensor network performance. For managing the attack related issues, in this paper introduces the Energy Efficient Extreme Learning Neural Network (EEELNN) approach for overcoming the attack related issues. Initially the network transmitted zone is computed along with energy, power, bandwidth, neighboring node information and lifetime for eliminating the attack in sensor network. The computed information is processed and trained by extreme learning neural network that successfully predict the attack related data, node and network zone with effective manner that leads to improve the overall network performance. At last system efficiency is evaluated using simulation results such as detection rate, classification accuracy, false alarm rate and detection time.