Intrusion detection model based on ensemble learning for U2R and R2L attacks

Sornsuwit, Ployphan; Jaiyen, Saichon

doi:10.1109/iciteed.2015.7408971

Cited by 30 publications

(9 citation statements)

References 5 publications

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“…Support Vector Machine (SVM) approach was used to solve the classification problems based on an optimal hyper plane in a high-dimensional space. The result of this study show that reducing features contributes to improved efficiency in detecting attacks in works in many weak scales [25]. Gaikwad advanced feature choice.…”

Section: Literature Reviewmentioning

confidence: 82%

An Intelligent Model for Online Recruitment Fraud Detection

Alghamdi¹,

Alharby²

2019

JIS

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This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

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Section: Literature Reviewmentioning

confidence: 82%

An Intelligent Model for Online Recruitment Fraud Detection

Alghamdi¹,

Alharby²

2019

JIS

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“…Sornsuwit and Jaiyen [27] took into account ensemble learning, e.g., Adaboost to improve the detection of U2R and R2L attacks. In addition, the correlation-based technique was used to reduce redundant features.…”

Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

107

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Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs.

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“…As mentioned above in the previous section, it is a derived version from KDD Cup 99 [6], that groups network traffic collected by 1998 DARPA IDS [4]. NSL KDD contains normal records, and records of attacks namely: DoS (Denial-of-Service) which destroy the service availability [13], Probe which extracts detailed information from the servers [14], U2R (User to Root) which try to exploit vulnerabilities in the system in order to obtain super user privileges [15], and R2L (Remote to Local) which send packets to a machine over a network who have no account on in order to lead to vulnerability issues and access secure information [16]. The distribution is illustrated in Table 1 and Table 2, Table 1 shows the distribution in two classes, whereas Table 2 shows the distribution in five classes.…”

Section: Dataset and Pre-treatments 311 Datasetmentioning

confidence: 99%

LSTM deep learning method for network intrusion detection system

Boukhalfa¹,

Abdellaoui²,

Hmina³

et al. 2020

IJECE

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The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services or steal information, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-TermMemory (LSTM) to recognize menaces and to obtain a long-term memory on them, inorder to stop the new attacks that are like the existing ones, and at the sametime, to have a single mean to block intrusions. According to the results of the experiments of detections that we have carried out, the Accuracy reaches upto 99.98 % and 99.93 % for respectively the classification of two classes and several classes, Also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is very effective, it has a great ability to memorize and differentiate between normal traffic and attack traffic and its identification is more accurate than other Machine Learning classifiers.

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Intrusion detection model based on ensemble learning for U2R and R2L attacks

Cited by 30 publications

References 5 publications

An Intelligent Model for Online Recruitment Fraud Detection

An Intelligent Model for Online Recruitment Fraud Detection

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

LSTM deep learning method for network intrusion detection system

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