An Ensemble of Feature Subset Selection With Deep Belief Network Based Secure Intrusion Detection in Big Data Environment

Abstract

In recent times, massive quantity of data and its exponential rise have modified the significance of data security and analysis systems for Big Data. An intrusion detection system (IDS) is a process which helps to monitor and analyze the data for detecting the intrusions in the network. The huge quantity, variation, and high speed of data produced in the system pose a difficulty to the conventional techniques for the detection of attacks. Big Data methods are utilized in IDS for dealing with Big Data for precise and effective data analytic processes. This paper introduces a novel feature subset selection (FSS) with deep belief network (DBN) based intrusion detection in big data environment, called FSS-DBN model. The presented model involves data preprocessing stage to improve the data quality to a certain extent. In addition, the FSS can be considered as an optimization problem and is effectively solved by the use of teaching and learning based optimization (TLBO) algorithm. Moreover, DBN model is applied for identifying the class labels (i.e. intrusions) in the network. For validating the proficient results analysis of the FSS-DBN model, an extensive set of simulations were performed and the superior performance is also highlighted with the maximum sensitivity of 0.9898, specificity of 0.9865, accuracy of 0.9854, F-score of 0.9872, and kappa of 0.9867.

WS-AWRE: Intrusion Detection Using Optimized Whale Sine Feature Selection and Artificial Neural Network (ANN) Weighted Random Forest Classifier

“…To improve the performance further, Ref. [14] employed a hybrid meta-heuristic approach with an OWKELM (Optimal Wavelet Kernel Extreme Learning Machine)-based classifier. The suggested system included a hybrid MFO (Moth Flame Optimization) with HC (Hill Climbing)-based process for feature selection.…”

Artificial Hummingbird Algorithm with Deep Variational Autoencoder Driven Intrusion Detection in Big Data Analytics Environment

“…Finally, BC technology must be implemented to protect inter-cluster data transmission methods. Kumar [15] introduced a Hybrid Meta-heuristic Optimization based Subset of FS (HMOFS) with Optimum Wavelet KELM (OWKELM) based Classi cation method named HMOFS-OWKELM system. The Hadoop Ecosystem was employed for handling BD.…”

“…The loss curves evidently expose the model's position with TR data, underscoring its capability to capture outlines e ciently in both TR and TS data. Notable is the constant re nement of parameters in the AHAAI-IDBDE methodology, aimed at decreasing discrepancies among calculations and actual TR labels.Table 4 demonstrates a brief comparative outcome of the AHAAI-IDBDE technique in terms of distinct metrics[24]. In Fig.8, the AHAAI-IDBDE algorithm is compared with existing systems in terms of and .…”