A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer

Alazzam, Hadeel; Sharieh, Ahmad; Sabri, Khair Eddin

doi:10.1016/j.eswa.2020.113249

Cited by 257 publications

(108 citation statements)

References 21 publications

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“…In [21], the researchers implemented a feature reduction method for intrusion detection system using the Pigeon Inspired Optimizer (PIO). The PIO is a bio-inspired algorithm influenced from the flight of white pigeons.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset

2020

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Computer networks intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) are critical aspects that contribute to the success of an organization. Over the past years, IDSs and IPSs using different approaches have been developed and implemented to ensure that computer networks within enterprises are secure, reliable and available. In this paper, we focus on IDSs that are built using machine learning (ML) techniques. IDSs based on ML methods are effective and accurate in detecting networks attacks. However, the performance of these systems decreases for high dimensional data spaces. Therefore, it is crucial to implement an appropriate feature extraction method that can prune some of the features that do not possess a great impact in the classification process. Moreover, many of the ML based IDSs suffer from an increase in false positive rate and a low detection accuracy when the models are trained on highly imbalanced datasets. In this paper, we present an analysis the UNSW-NB15 intrusion detection dataset that will be used for training and testing our models. Moreover, we apply a filter-based feature reduction technique using the XGBoost algorithm. We then implement the following ML approaches using the reduced feature space: Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Logistic Regression (LR), Artificial Neural Network (ANN) and Decision Tree (DT). In our experiments, we considered both the binary and multiclass classification configurations. The results demonstrated that the XGBoost-based feature selection method allows for methods such as the DT to increase its test accuracy from 88.13 to 90.85% for the binary classification scheme.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset

2020

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“…On the other hand, our proposed IDS utilizes a DNN-based classifier that is more suitable for massive data. Another recent paper [28] used a pigeon inspired optimizer (PIO)-based feature selection approach. The PIO method is among the newly developed bio-inspired swarm algorithms for optimizing problems.…”

Section: Related Workmentioning

confidence: 99%

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

Siddiqi

Pak

2020

Electronics

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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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“…An approach based on the mutual information used to perform the feature selection in the intrusion detection context is defined in [38], whereas an interesting approach that combines several feature selection algorithms is presented in [39]. Another interesting approaches of feature selection is presented in [40], where the authors formalize such a technique in terms of a pigeon-inspired optimizer algorithm, applying it in an intrusion detection scenario. In [41], the feature selection technique is considered in the context of the big data scenario, since the authors proposed a penalty-based wrapper objective function to evaluate the feature selection process, whereas, in [42], the same authors faced the feature selection problem in a high-dimensional data scenario.…”

Section: Related Workmentioning

confidence: 99%

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

et al. 2020

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The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions.

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A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer

Cited by 257 publications

References 21 publications

Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset

Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset

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

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

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