An Ensemble Classifier Approach on Different Feature Selection Methods for Intrusion Detection

2018 ◽

Vol 7 (1) ◽

pp. 57-72

Author(s):

H.P. Vinutha ◽

Poornima Basavaraju

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Detection Rate ◽

Information Gain ◽

False Positive Rate ◽

Ensemble Classifier ◽

Ensemble Classification ◽

Chi Square ◽

Traffic Pattern ◽

Data Mining Algorithms

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.

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Feature Extraction Methods for Intrusion Detection Systems

Image Processing ◽

10.4018/978-1-4666-3994-2.ch054 ◽

2013 ◽

pp. 1064-1092

Author(s):

Hai Thanh Nguyen ◽

Katrin Franke ◽

Slobodan Petrovic

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Intrusion Detection ◽

Domain Knowledge ◽

Intrusion Detection Systems ◽

Feature Construction ◽

Selection Methods ◽

Detection Systems ◽

Negative Effect ◽

Managing Risk

Intrusion Detection Systems (IDSs) have become an important security tool for managing risk and an indispensable part of overall security architecture. An IDS is considered as a pattern recognition system, in which feature extraction is an important pre-processing step. The feature extraction process consists of feature construction and feature selection . The quality of the feature construction and feature selection algorithms is one of the most important factors that affects the effectiveness of an IDS. Achieving reduction of the number of relevant traffic features without negative effect on classification accuracy is a goal that largely improves the overall effectiveness of the IDS. Most of the feature construction as well as feature selection works in intrusion detection practice is still carried through manually by utilizing domain knowledge. For automatic feature construction and feature selection, the filter, wrapper, and embedded methods from machine learning are frequently applied. This chapter provides an overview of various existing feature construction and feature selection methods for intrusion detection systems. A comparison between those feature selection methods is performed in the experimental part.

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Building an efficient intrusion detection system based on feature selection and ensemble classifier

Computer Networks ◽

10.1016/j.comnet.2020.107247 ◽

2020 ◽

Vol 174 ◽

pp. 107247 ◽

Cited By ~ 14

Author(s):

Yuyang Zhou ◽

Guang Cheng ◽

Shanqing Jiang ◽

Mian Dai

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Ensemble Classifier

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Optimizing Filter-Based Feature Selection Method Flow for Intrusion Detection System

Electronics ◽

10.3390/electronics9122114 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2114

Author(s):

Murtaza Ahmed Siddiqi ◽

Wooguil Pak

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

High Efficiency ◽

Detection System ◽

Feature Selection Method ◽

Extraction Methods ◽

Security Requirements ◽

Digital Information ◽

Selection Methods ◽

Process Flow

In recent times, with the advancement in technology and revolution in digital information, networks generate massive amounts of data. Due to the massive and rapid transmission of data, keeping up with security requirements is becoming more challenging. Machine learning (ML)-based intrusion detection systems (IDSs) are considered as one of the most suitable solutions for big data security. Despite the progress in ML, unrelated features can drastically influence the performance of an IDS. Feature selection plays a significant role in improving ML-based IDSs. However, the recent growth of dimensionality in data poses quite a challenge for current feature selection and extraction methods. Due to high data dimensionality, feature selection methods suffer in terms of efficiency and effectiveness. In this paper, we are introducing a new process flow for filter-based feature selection with the help of a transformation technique. Generally, normalization or transformation is implemented before classification. In our proposed model, we implemented and evaluated the effects of normalization before feature selection. To present a clear analysis on the effects of power transformation, five different transformations were implemented and evaluated. Furthermore, we implemented and compared different feature selection methods with the proposed process flow. Results show that compared with existing process flow and feature selection methods, our proposed process flow for feature selection can locate a more relevant set of features with high efficiency and accuracy.

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A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

10.21203/rs.3.rs-442429/v1 ◽

2021 ◽

Author(s):

Jayaprakash Pokala ◽

B. Lalitha

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Ensemble Classifier ◽

Support Vector ◽

Routing Attacks ◽

Salp Swarm Algorithm ◽

Network Intrusion ◽

Swarm Algorithm

Abstract Internet of Things (IoT) is the powerful latest trend that allows communications and networking of many sources over the internet. Routing protocol for low power and lossy networks (RPL) based IoT networks may be exposed to many routing attacks due to resource-constrained and open nature of the IoT nodes. Hence, there is a need for network intrusion detection system (NIDS) to protect RPL based IoT networks from routing attacks. The existing techniques for anomaly-based NIDS (ANIDS) subjects to high false alarm rate (FAR). Therefore, a novel bio-inspired voting ensemble classifier with feature selection technique is proposed in this paper to improve the performance of ANIDS for RPL based IoT networks. The proposed voting ensemble classifier combines the results of various base classifiers such as logistic Regression, support vector machine, decision tree, bidirectional long short-term memory and K-nearest neighbor to detect the attacks accurately based on majority voting rule. The optimized weights of base classifiers are obtained by using the feature selection method called simulated annealing based improved salp swarm algorithm (SA-ISSA), which is the hybridization of particle swarm optimization, opposition based learning and salp swarm algorithm. The experiments are performed with RPL-NIDDS17 dataset that contains seven types of attack instances. The performance of the proposed model is evaluated and compared with existing feature selection and classification techniques in terms of accuracy, attack detection rate (ADR), FAR and so on. The proposed ensemble classifier shows better performance with higher accuracy (96.4%), ADR (97.7%) and reduced FAR (3.6%).

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