Intrusion Detection System Using Random Forest on the NSL-KDD Dataset

Negandhi, Prashil; Trivedi, Yash; Mangrulkar, Ramchandra

doi:10.1007/978-981-13-6001-5_43

Cited by 46 publications

(18 citation statements)

References 5 publications

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“…Negandhi et al employed the supervised learning algorithm to train a network attacks detection model based on random forests. e model reduces the number of input features under an effective feature selection mechanism, not only improving the running speed but also realizing a high accuracy [15]. Sarker et al proposed an ML-based multilayered framework for the purpose of promoting the security of network system, which aims at the applicability towards data-driven intelligent decisions and protects network systems and devices from network attacks [16].…”

Section: Related Workmentioning

confidence: 99%

Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection

Wang

Zhou³

et al. 2021

Security and Communication Networks

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With the increase of Internet visits and connections, it is becoming essential and arduous to protect the networks and different devices of the Internet of Things (IoT) from malicious attacks. The intrusion detection systems (IDSs) based on supervised machine learning (ML) methods require a large number of labeled samples. However, the number of abnormal behaviors is far less than that of normal behaviors, let alone that the shots of malicious behavior samples which can be intercepted as training dataset are actually limited. Consequently, it is a key research topic to conduct the anomaly detection for the small number of abnormal behavior samples. This paper proposes an anomaly detection model with a few abnormal samples to solve the problem in few-shot detection based on convolutional neural networks (CNN) and autoencoder (AE). This model mainly consists of the CNN-based supervised pretraining module and the AE-based data reconstruction module. Only a few abnormal samples are utilized to the pretrain module to build the structure of extracting deep features. The data reconstruction module simply chooses the deep features of normal samples as training data. There also exist some effective attention mechanisms in the pretraining module. Through the pretraining of small samples, the accuracy of abnormal detection is improved compared with merely training normal samples with AE. The simulation results prove that this solution can solve the above problems occurring in network behavior anomaly detection. In comparison to the original AE model and other clustering methods, the proposed model advances the detection results in a visible way.

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Section: Related Workmentioning

confidence: 99%

Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection

Wang

Zhou³

et al. 2021

Security and Communication Networks

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“…Random forest is an ensemble technique. It is called as bootstrapping and aggregation is the result based on majority vote of base models on the test data [23]. Random forest is a bagging technique which feed the data to the base models by row sampling with replacement and predicting the classes.…”

Section: Random Forest Classifiermentioning

confidence: 99%

A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Sonker¹,

Gupta²

2021

IJECE

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Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.

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“…The internet plays an essential role in our daily part of life, a resource used for learning information in different fields such as education, business, and others. Specifically, most organisations could use the Internet as the technology for accomplishing their management activities [1,2]. They can utilise the Internet application for profit growth and keep confidential/private information secret.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

et al. 2022

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Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples.Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. Theresults show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes. INDEX TERMSNetwork IntrusionDetection System (NIDS), Density-based spatial clustering of applications with noise (DBSCAN), Anticipated Distance-based Clustering (ADC), Data Preprocessing, Likelihood Naïve Bayes (LNB), and IDS Datasets.

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Intrusion Detection System Using Random Forest on the NSL-KDD Dataset

Cited by 46 publications

References 5 publications

Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection

Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection

A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

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