Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

Abstract

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate.

Enhanced Arachnid Swarm-Tuned Convolutional Neural Network Model for Efficient Intrusion Detection

“…The ASO opt CNN model undergoes an evaluation where it is compared to various existing models. These existing include KNN [34], SVM [35], BiLSTM [36], deep CNN [37], PSObased deep CNN [38], and SSO-based deep CNN [39] to gauge its performance.…”

An improved deep bagging convolutional neural network classifier for efficient intrusion detection system

“…This section discusses the different literature and recent studies on IDS. The NSL-KDD data set is used to test several classification algorithms in paper [3]. Using the WEKA tool, this can investigate protocols with intruder attacks.…”

“…Reduce the rate of error and the time it takes to identify it, as well as the computing complexity. The performance of the proposed work's feature selection is compared to those of existing FS on IDS, such as standard DFO [21], FMIFS [22], FLCFS [23], and IGDFOPSOCCNN [3]. Table 2 illustrates that, when compared to other existing contemporary techniques, the proposed IIW-DFO FS achieves high accuracy with low complexity analysis, meaning that these selected features would improve classification accuracy and protect the computer network from intruders.…”

“…We compare our proposed convolutional deep neural network based IDS to current IDS systems such as DMNB [24], DBN-SVM [25], PSOM [1], and IGDFOPSOCCNN [3] to prove the deep neural networkbased IDS systems. The evaluation's results are shown in Table 3.…”

Distributed denial of service attacks detection for software defined networks based on evolutionary decision tree model

“…Ensemble technique was employed in the paper [20] to enhance IDS efficiency. The traditional NSL-KDD dataset is used to test multiple classification algorithms [21]. Karan et al [22] presented a system for a DDoS attacks detection in SDN.…”