A new approach for data stream classification: unsupervised feature representational online sequential extreme learning machine

Aydoğdu, Özge; Ekinci, Murat

doi:10.1007/s11042-020-09300-y

Cited by 5 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, researchers in [12] presented feature selection techniques based on unsupervised learning schemes to enhance data analytics tasks and computational intelligence performance on large datasets. Researchers in [13,14,15] reported enhancing the performance of real-time analytics operations on big data by using appropriate feature representation techniques. Furthermore, the adoption of autoencoders for feature representation is seen in many applications, such as the classification of anti-drug response [16] and diagnosis of autism spectrum disorder [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Network Intrusion Detection System Based on Semi-Supervised Approach for IoT

A¹,

Mangla²

2023

IJACSA

View full text Add to dashboard Cite

An intrusion detection system (IDS) is one of the most effective ways to secure a network and prevent unauthorized access and security attacks. But due to the lack of adequately labeled network traffic data, researchers have proposed several feature representations models over the past three years. However, these models do not account for feature generalization errors when learning semantic similarity from the data distribution and may degrade the performance of the predictive IDS model. In order to improve the capabilities of IDS in the era of Big Data, there is a constant need to extract the most important features from large-scale and balanced network traffic data. This paper proposes a semi-supervised IDS model that leverages the power of untrained autoencoders to learn latent feature representations from a distribution of input data samples. Further, distance function-based clustering is used to find more compact code vectors to capture the semantic similarity between learned feature sets to minimize reconstruction loss. The proposed scheme provides an optimal feature vector and reduces the dimensionality of features, reducing memory requirements significantly. Multiple test cases on the IoT dataset MQTTIOT2020 are conducted to demonstrate the potential of the proposed model. Supervised machine learning classifiers are implemented using a proposed feature representation mechanism and are compared with shallow classifiers. Finally, the comparative evaluation confirms the efficacy of the proposed model with low false positive rates, indicating that the proposed feature representation scheme positively impacts IDS performance.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Network Intrusion Detection System Based on Semi-Supervised Approach for IoT

A¹,

Mangla²

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Apart from the above applications, several researchers have reported the prime role of FRL in handling the large volume of biological data to identify therapeutic peptides and serve as future benchmark in designing promising tools for disease screening [8,9]. More potentially in recent studies, many authors have claimed that the application of FRL can boost the performance of real-time analytic tasks on massive IoT data [10][11][12][13]. Taking inspiration from these literatures, the impetus of this work is to apply FRL and develop an efficient IDS which can be in pace with current trends to handle the large volume of network traffic in a big data environment and display higher detection accuracy for intrusion.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

Vaiyapuri¹,

Binbusayyis²

2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-theart performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

show abstract

Parallelized extreme learning machine for online data classification

Vidhya

Aji

2022

Appl Intell

View full text Add to dashboard Cite

A new approach for data stream classification: unsupervised feature representational online sequential extreme learning machine

Cited by 5 publications

References 45 publications

A Novel Network Intrusion Detection System Based on Semi-Supervised Approach for IoT

A Novel Network Intrusion Detection System Based on Semi-Supervised Approach for IoT

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

Parallelized extreme learning machine for online data classification

Contact Info

Product

Resources

About