A Deep One-class Model for Network Anomaly Detection

Dai, Songlin; Yan, Jian; Wang, Xiaoming; Zhang, Lin

doi:10.1088/1757-899x/563/4/042007

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…The use of convolution has also reduced the training time. In [ 73 ], stacked autoencoders are used with a one-class classification model. The use of autoencoders allows the selection of the most relevant features and the reduction of data dimensionality.…”

Section: Analysis and Algorithms For Streaming Datamentioning

confidence: 99%

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Time series big data: a survey on data stream frameworks, analysis and algorithms

et al. 2023

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Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.

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Section: Analysis and Algorithms For Streaming Datamentioning

confidence: 99%

“…Several works are based on ANNs, such as [37,[64][65][66][67][68][69][70][71][72][73][74]. In [64], motivated by the presence of a high rate of false alarms and improving accuracy, Hussain et al proposed a FeedForward Neural Network (FNN) to detect anomalies in cellular networks.…”

Section: Existing Solutionsmentioning

confidence: 99%

Time series big data: a survey on data stream frameworks, analysis and algorithms

et al. 2023

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“…A major goal of network intrusion detection system (NIDS) is to distinguish the abnormal activities from the normal behaviour of the network. It is more flexible to detect a possible outcome of new emerging threads and also highly sensitive to fall in false alarm rate [5]. Although, the other complication arises in the anomaly detection technique is that they exhibit failure in single point, speed scalability, detection rate etc.. To overcome these issues, network intrusion detection system using machine learning methodologies are introduced to enhance the system performance.…”

Section: Imentioning

confidence: 99%

A New Design of Custom Optimized Cnn-Lstm Assists to Detect Network Anomaly Using Categorical Data

Kanmani¹,

Malar²,

Roopa³

et al. 2021

Preprint

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For traditional intrusion detection model, the system effectiveness is fully based on training dataset and feature selection. During feature selection, it needs more labour charge and trusted mainly on expert’s knowledge. Moreover, the training dataset contains more imbalanced data which in terms model tends to be biased. Here, an automatic approach is introduced to correct deficiency in the system. In this paper, the author proposes novel network anomaly detection (NID) build using categorical data. A model has to be designed with modified form of deep neural network primarily utilized for detecting anomaly within the network. Custom CNN-LSTM with Harris Hawks Optimization (named as custom optimized CNN-LSTM) is designed as a new classifier majorly used to detect the anomaly from word cloud to distinguish the data with effective performance. The experimental result shows that the proposed method achieves a promising output for network anomaly detection.

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“…They described the advantages and disadvantages of deep learning based anomaly detection techniques and also explained the application areas of various methods. Dai, Yan, Wang and Zhang [7] proposed a one class model for anomaly detection using autoencoders and support vector machine model. Thomas and Judith [8] proposed voting based ensemble of outlier detectors to improve the performance of individual outlier detection algorithms.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Dimensionality Reduction for Outlier Detection in High Dimensional Data

2020

IJETER

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The infrequent data objects in a dataset containing important characteristics are called outliers. Dimensionality reduction techniques are used for reducing the memory requirement and processing time while handling high dimensional datasets. Principal Component Analysis (PCA) is one of the most commonly used techniques for reducing the dimensions in high dimensional datasets. Autoencoders (AE) are used in deep learning for dimensionality reduction. In this paper, we evaluate the performance of these dimensionality reduction techniques for outlier detection task and also propose a hybrid dimensionality reduction technique that can be used in the preprocessing step of many machine learning models. Experimental results using publicly available datasets show that the proposed method outperforms the PCA method and autoencoder based method in outlier detection tasks using isolation forest.

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A Deep One-class Model for Network Anomaly Detection

Cited by 3 publications

References 17 publications

Time series big data: a survey on data stream frameworks, analysis and algorithms

Time series big data: a survey on data stream frameworks, analysis and algorithms

A New Design of Custom Optimized Cnn-Lstm Assists to Detect Network Anomaly Using Categorical Data

Hybrid Dimensionality Reduction for Outlier Detection in High Dimensional Data

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