Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear Support Vector Machine (l-SVM). It constructs an auto-encoder method, an efficient learning approach for feature construction unsupervised manner. Here, the inclusive certified signature (ICS) is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers. By training the samples in the preliminary stage, the selected features are provided into the classifier (lSVM) to enhance the prediction ability for intrusion and classification accuracy. Thus, the model efficiency is learned linearly. The multi-classification is examined and compared with various classifier approaches like conventional SVM, Random Forest (RF), Recurrent Neural Network (RNN), STL-IDS and game theory. The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy, precision, recall, F-measure, pvalue, MCC and so on. The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection. Here, the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

The salient features of WSN like use of wireless radio communication, collaborative nature and deployment in the open environment exposes it to many security threats. Since WSN has tight limitations on the power consumption, transmission and computation the complex cryptographic algorithms can't be used to provide the security. Key management in WSN is the fundamental line of defense for a secure communication and thus it is very important. In this paper we propose a new framework for enhanced key management for hierarchical WSN which enhances the security of the network. In the proposed framework the base station computes all the keys required for both inter and intra cluster communications. Cluster is further isolated into small geographical areas on the basis of hop count from the cluster head. The sensor nodes in the network join the cluster on the basis of the distance (hop counts) from the cluster head which localizes the path key things and reduces the overhead. The proposed framework is divided into four stages pre key distribution, pair wise key establishment, computing the path key and re keying all the keys. General TermsWireless sensor network, security, key management, hop count, and cluster.