RETRACTED: Novel heuristic dual-ant clustering algorithm for network intrusion outliers detection

Li, Tao

doi:10.1016/j.ijleo.2014.08.036

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Currently available methods can be grouped into three categories: distance-oriented, statistical distribution-oriented, and clustering-oriented approaches. Statistical distribution-oriented methods require the data points under investigation to follow a specific distribution [7], which can then be used to identify outliers. Distance-oriented approaches detect outliers by measuring the number of neighbors a data point has -an outlier is a data point with few neighbors.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method

Govindaraj¹,

Kaliappan²,

Swaminathan³

2023

ISI

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Outlier identification and elimination are essential preprocessing steps for data analysis tasks such as clustering, classification, and regression. The accuracy of data analysis outcomes may be compromised if outliers are not adequately addressed. Detecting outliers is particularly challenging when they are characterized by unusual combinations of multiple attributes. Furthermore, the presence of outliers can impact various data processing activities, necessitating either the reduction of outlier influence or their complete removal. Outlier detection in multivariate data presents a complex process that becomes increasingly difficult when dealing with high-dimensional datasets. Consequently, this study focuses on the identification of such outliers in multivariate datasets using intelligent techniques. In the proposed approach, outliers are detected using an Improved Neural Network (INN), where the hidden neurons are tuned by a novel Synergistic Firefly-Grey Wolf Optimization (SF-GWO) algorithm. This algorithm combines the strengths of the Firefly Optimization (SFO) and Grey Wolf Optimization (GWO) techniques to maximize accuracy. The unique method results in enhanced classification model performance, reduced computation time, and increased classification accuracy. The proposed model has been evaluated and compared with well-established traditional techniques, demonstrating its effectiveness in addressing the challenges of outlier detection in multidimensional datasets.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method

Govindaraj¹,

Kaliappan²,

Swaminathan³

2023

ISI

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“…Increasingly more consideration from specialists and researchers as of late. Portable distributed computing exploits the incredible registering and capacity abilities of distributed computing, just as the portability and adaptability of versatile terminals, carrying extraordinary comfort to individuals' lives [2]. On the portable cloud stage, the versatile terminal will present the calculation assignment of use programs with a lot of computation and information handling to the remote cloud server farm.…”

Section: Introductionmentioning

confidence: 99%

Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

Sudhakaran

Swaminathan

Yuvaraj

et al. 2020

Int. J. Interact. Mob. Technol.

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Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.

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“…Moreover, almost all outlier detection approaches (ODAs) such as firefly algorithm (FA), genetic algorithm and ant algorithm are not valid for ST TD analysis. For instance, Karthikeyan et al [10] developed a hybrid discrete FA for solving multi-objective flexible job shop scheduling problems; Cucina et al [11] discussed a genetic algorithm for the detection of multiple additive outliers in multivariate time series; and Li and Xiao [12] gave a novel heuristic dual-ant clustering algorithm for the detection network intrusion outliers.…”

Section: Introductionmentioning

confidence: 99%

ST TD outlier detection

Sun

Zhao

Yue³

et al. 2017

IET Intelligent Transport Systems

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RETRACTED: Novel heuristic dual-ant clustering algorithm for network intrusion outliers detection

Cited by 8 publications

References 9 publications

An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method

An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method

Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

ST TD outlier detection

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