An analysis of random forest algorithm based network intrusion detection system

Aung, Yi Yi; Min, Myat Myat

doi:10.1109/snpd.2017.8022711

Cited by 36 publications

(10 citation statements)

References 4 publications

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“…These are findings that were replicated by our own initial tests. Additionally, there are many examples of current intrusion detection research also utilising RF such as [31] and [32].…”

Section: Stacked Non-symmetric Deep Auto-encodersmentioning

confidence: 99%

A Deep Learning Approach to Network Intrusion Detection

Shone

Ngoc

Phai

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

1,201

481

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Abstract-Network Intrusion Detection Systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed non-symmetric deep auto-encoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in GPU-enabled TensorFlow and evaluated using the benchmark KDD Cup '99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

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“…These are findings that were replicated by our own initial tests. Additionally, there are many examples of current intrusion detection research also utilising RF such as [31] and [32].…”

Section: Stacked Non-symmetric Deep Auto-encodersmentioning

confidence: 99%

A Deep Learning Approach to Network Intrusion Detection

Shone

Ngoc

Phai

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

1,201

481

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show abstract

“…Aung et al. [ 25 ] proposed a hybrid IDS method based on k-means and RF algorithms on the KDD’99 dataset, with the results indicating a high-performance accuracy and low model training time. Another study [ 26 ] used the RF model and adapted it to the Apache Spark distributed processing system to realize real-time detection with satisfactory efficiency and accuracy compared to existing systems.…”

Section: Related Workmentioning

confidence: 99%

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method

Kim

Kang

et al. 2022

Sensors

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In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.

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“…Yi Yi Aung et al [14] states the security of the computer system is of great importance, And in the last few years, there have seen an affected growth in the amount of intrusions that intrusion detection has become the dominant of current information security. Firewalls cannot provide complete protection.…”

Section: Literature Surveymentioning

confidence: 99%

A Review on Various Methods of Intrusion Detection System

Agrawal¹,

Agrawal²

2020

CEIS

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Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. Data mining is used to clean, classify and examine large amount of network data. Since a large volume of network traffic that requires processing, we use data mining techniques. Different Data Mining techniques such as clustering, classification and association rules are proving to be useful for analyzing network traffic. This paper presents the survey on data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, thereby helping the users to develop secure information systems.

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An analysis of random forest algorithm based network intrusion detection system

Cited by 36 publications

References 4 publications

A Deep Learning Approach to Network Intrusion Detection

A Deep Learning Approach to Network Intrusion Detection

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method

A Review on Various Methods of Intrusion Detection System

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