A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Rajagopal, Smitha; Kundapur, Poornima Panduranga; Hareesha, K S

doi:10.1155/2020/4586875

Cited by 153 publications

(70 citation statements)

References 37 publications

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“…The varying gene expression can be efficiently analyzed using microarray where all the genes of a particular organism are placed in different grooves on a slide. Gene expression data could be effectively maintained and processed using statistical methods to analyze diseases much easier [14]. The state of a cell communicated by the layout of RNA will thusly serve to be of great help to check whether a cell might be a normal or a variation one [15].…”

Section: Related Workmentioning

confidence: 99%

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Chandrakar¹,

Shrivas²,

Sahu³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Gene expression levels are important for identifying and diagnosing diseases like cancer. Gene expression microarray information contains a high extent feature set, which minimizes the performance and the accuracy of classifiers. OBJECTIVES: This paper proposes a Modified Genetic Algorithm (MGA) that is based on Classifier Subset Evaluators -Genetic Search (Eval-CSE_GS) for selecting the relevant feature subsets. The MGA feature selection procedure is applied to microarray information for cancer patients that minimize a high dimension feature subset into low dimension feature subsets. METHODS: The various data mining methods for classifying the various kinds of cancer disease patients are presented. The proposed model refers to an ensemble model (PEM) for the organization of cancer disease by reducing the feature subsets, which results show improvements in the success rate. RESULTS: The proposed ensemble model obtains the accuracy of 94.58%, 96.56% and 97.04% for PEM-1 to PEM-3, respectively. CONCLUSION: Our proposed MGA-PEM model gives satisfactory results for cancer identification and classification.

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Section: Related Workmentioning

confidence: 99%

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Chandrakar¹,

Shrivas²,

Sahu³

2021

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…The experiments revealed that fuzzy SVM is more robust than SVM. A predictive model using stacking for intrusion detection was developed 24 . The system was tested on UNSW‐NB15 and UGR'16 datasets and results show that misclassification rate is minimal.…”

Section: Related Workmentioning

confidence: 99%

An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network

Thaseen

Banu

Lavanya

et al. 2020

Trans Emerging Tel Tech

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Serious concerns regarding vulnerability and security have been raised as a result of the constant growth of computer networks. Intrusion detection systems (IDS) have been adopted by network administrators to provide essential network security. Commercial IDS in the market do not have the capability to identify novel attacks but generate false alarms for legitimate user activities. Neural networks can be applied for the solution of these issues and for providing improved accuracy. Correlation‐based attribute selection ranks the features according to the highest correlation between the attributes and class label. In this article, the authors propose a correlation‐based feature selection integrated with neural network for identifying anomalies. Experimental analysis performed on NSL‐KDD and UNSW‐NB datasets, which are benchmark datasets of intrusion detection with current attacks. The results show that the proposed model is superior in terms of accuracy, sensitivity, and specificity in comparison with some of the state‐of‐the‐art techniques. With the emergence of the Internet of Things Technology, such IDS can be deployed for securing the IoT servers in future. Wireless payment systems can be secured by building and deploying IDS. A secure integrated network management can be achieved which is error‐free and thereby improving performance.

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“…To strengthen proposed scheme, we have also analyzed our framework with publically available TUIDS [19], UNSW-NB15 [20,22] and UNIBS datasets [21]. Figure 7 illustrates the impact of processing delay with respect to node density in presence of insider malicious adversaries.…”

Section: Figure6 Guiding Principle Implementation Framework Assessmentmentioning

confidence: 99%

Context-Aware Autonomous Security Assertion for Industrial IoT

et al. 2020

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The Industrial Internet of Things (IIoT) platform consists of purpose-driven communication controllers, enterprise-grade modems (routers and gateways), and edge computing systems that require integrated software and sensing capability in mission-critical environments. Extensible purpose-built industrial supervisory control and data acquisition networks are prone to numerous cybersecurity threats. In this paper, the historical databased qualitative threat assessment was part of the comprehensive risk breakdown (i.e., to quantify assessment and remediation) of the practicing industry (i.e., systems that rely on robotics, big data & analytics). Furthermore, a risk and operability (HAZOP & convolution neuralnetwork) evaluation was proved to be the paramount study for autonomous vulnerability assessment. Through autonomous network management, continuous software monitoring, data-driven device insights, and integrated content filtering, the proposed endpoint protection scheme shows significant improvement in preventing data breaches, denial of service (DoS), and malware detection. A distinctive computational methodology to determine the cyber risk for industrial structures with IoT-explicit control factors has been programmed and elucidated in the perspective of IIoT systems. Firmware driven emulation (integrated and optimized) outcome aided to reduce breach ratio, better incident detection, and enhanced protection of confidential data.

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A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Cited by 153 publications

References 37 publications

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network

Context-Aware Autonomous Security Assertion for Industrial IoT

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