A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions

Thakkar, Ankit; Lohiya, Ritika

doi:10.1007/s10462-021-10037-9

Cited by 128 publications

(88 citation statements)

References 218 publications

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“…Further, in 2021, Ankit et al surveyed the IDS problem, FS method, ML techniques, and different datasets in the context of IDS and have considered one of the recent datasets i.e. CICIDS2017, and outlined a roadmap for understanding and designing effective IDS models in the future [20].…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Using Classification Algorithms with Feature Selection Mechanism over Real-time Data Traffic

Sah

Singh

Banerjee

2022

Preprint

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The key objective of intrusion detection systems (IDS) is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal. These IDS uses many methods of machine learning (ML) to learn from past-experience attack signatures and identify the new ones. Even though these methods are effective, but they have to suffer from large computational costs due to considering all the traffic features, together. Moreover, emerging technologies like the Internet of Things (IoT), Big data, etc. are getting advanced day by day, as a result, network traffics is also increasing rapidly. Therefore, the issue of computational cost needs to be addressed properly. Thus, in this research, firstly, the ML methods have been used with feature selection technique (FST) to reduce the number of features by picking out only the important ones from NSL-KDD and CICIDS2017 datasets that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network. The experimental result proved that the proposed model i.e. Decision tree (DT) with Recursive feature elimination (RFE) performs better than other classifiers with RFE in terms of accuracy, specificity, precision, sensitivity, F1-score, and G-means on both datasets.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Using Classification Algorithms with Feature Selection Mechanism over Real-time Data Traffic

Sah

Singh

Banerjee

2022

Preprint

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“…Sistem Deteksi instruksi adalah solusi yang umum digunakan untuk menganalisis dan mendeteksi serangan DDoS. Teknik IDS ini memiliki tujuan untuk mendeteksi, mengklasifikasi dan merespon serangan DDoS yang mempengaruhi integritas, kerahasiaan atau ketersediaan sumber daya jaringan (Min et al, 2018) (Thakkar & Lohiya, 2021). Sistem ini didasarkan pada dua metode pendeteksian, yaitu Deteksi Penyalahgunaan dan Deteksi Anomali (Cepheli et al, 2016).…”

Section: Pemantauanunclassified

Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review

Faiz¹,

Somantri²,

Supriyono³

et al. 2022

JITE

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Cybersecurity attacks are becoming increasingly sophisticated and increasing with the development of technology so that they present threats to both the private and public sectors, especially Denial of Service (DoS) attacks and their variants which are often known as Distributed Denial of Service (DDoS). One way to minimize this attack is by using traditional mitigation solutions such as human-assisted network traffic analysis techniques but experiencing some limitations and performance problems. To overcome these limitations, Machine Learning (ML) has become one of the main techniques to enrich, complement and enhance the traditional security experience. The way ML works are based on the process of data collection, training and output. ML is influenced by several factors, one of which is feature engineering. In this study, we focus on the literature review of several recent studies which show that the feature selection process greatly impacts the level of accuracy of this ML. Datasets such as KDD, UNSW-NB15 and others also affect the level of accuracy of ML. Based on this literature review, this study can observe several feature engineering strategies with relevant impacts that can be chosen to improve ML solutions on DDoS attacks.

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“…The labeling process is time-consuming and laborious; thus, continuously maintaining the labeled datasets is impractical.Moreover, high-quality datasets for training the IDSs are hard to obtain, although they play an essential role in enhancing IDS detection performance. The main reason for this shortage is privacy concerns [10][11][12]. Following [13], enriching the network information for IDS datasets requires the neglect of privacy and security issues.…”

Section: Introductionmentioning

confidence: 99%

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

Nguyen

Tran

et al. 2022

Electronics

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The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for us; on the other hand, the devices are susceptible to various cyber-attacks due to the lack of solid security mechanisms and hardware security support. In this paper, we present IMIDS, an intelligent intrusion detection system (IDS) to protect IoT devices. IMIDS’s core is a lightweight convolutional neural network model to classify multiple cyber threats. To mitigate the training data shortage issue, we also propose an attack data generator powered by a conditional generative adversarial network. In the experiment, we demonstrate that IMIDS could detect nine cyber-attack types (e.g., backdoors, shellcode, worms) with an average F-measure of 97.22% and outperforms its competitors. Furthermore, IMIDS’s detection performance is notably improved after being further trained by the data generated by our attack data generator. These results demonstrate that IMIDS can be a practical IDS for the IoT scenario.

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A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions

Cited by 128 publications

References 218 publications

Intrusion Detection System Using Classification Algorithms with Feature Selection Mechanism over Real-time Data Traffic

Intrusion Detection System Using Classification Algorithms with Feature Selection Mechanism over Real-time Data Traffic

Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

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