An Intrusion Detection System: Using a Grasshopper Algorithm

Abstract

With the advent of technology and the Internet, security concerns seem to increase every day. and as the technology has become increasingly sophisticated, concerted and cooperative attacks have risen too. In such circumstances, there is a pressing need for software tools that can detect a wide range of infiltrations. So far, suggestions have been made by researchers for the intrusion detection system. However, more research is still needed to improve its accuracy. Therefore, the proposed method detects and classifies a series of distributed denial of service attacks using the grasshopper optimization algorithm (GOA). In this way, after reducing the feature by the grasshopper optimization algorithm (GOA) and finding the optimal features, we classify the attacks as normal and abnormal by using the Nearest Neighbor Classifier (KNN). Experiments have been performed on the NSL-KDD datasetThe results show that the proposed method has a significant advantage over PSO, GA, and ACO in accuracy and speed of identifying attacks.

Differential Evolution Model for Identification of Most Influenced Gene in Brest Cancer Data

“…The identification of regulatory motifs unique to each gene cluster and the proposing of cis-regulatory elements [10] that are particular to each gene cluster may be achieved by searching for comparable DNA sequences at the promoter regions of genes within the same cluster. Gene expression data may be used to make inferences about the mechanism of the transcriptional regulatory network, based on clustering [17,18], which are discussed further below. In the end, grouping distinct samples based on their matching expression patterns may uncover subcell types that are difficult to detect using classic morphology-based techniques.…”