Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset

Deshmukh, Datta H.; Ghorpade, Tushar; Padiya, Puja

doi:10.1109/iccict.2015.7045674

Cited by 47 publications

(13 citation statements)

References 4 publications

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“…In this section, we first determine the parameters of BAT-MC to obtain the optimal model through experiments which carry out on a public dataset: the NSL-KDD dataset [46], [47]. Then, we analyze the performance of the BAT-MC model.…”

Section: Discussionmentioning

confidence: 99%

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

et al. 2020

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Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.

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Section: Discussionmentioning

confidence: 99%

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

et al. 2020

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“…Even though the data is structured, it is not advisable to feed them directly to algorithm. [4] suggests the use of data pre-processing to improve machine learning.Classification and clustering accuracy is predominantly dependent on the proper representation of data. Data preprocessing involves data cleaning, data transformation, data reduction, oversampling data anddata selection.…”

Section: IIImentioning

confidence: 99%

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2018

IJET

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“…When used in classification or prediction it is necessary to identify the features that would enable higher accuracy in classification. [7] suggests the use of data preprocessing to improve machine learning. Classification and clustering accuracy is predominantly dependent on the proper representation of data.…”

Section: IVmentioning

confidence: 99%

“…Machine learning literally means, make the machine learn, machine learns by processing the data with various machine learning algorithm [7]. There is no fixed algorithm to provide high accuracy this is called No Free lunch theorem [8], however deep learning provides a better accuracy in most cases.…”

Section: Machine Learningmentioning

confidence: 99%

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2018

IJET

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Many customers visit a restaurant based on food critics and reviews on websites such as Zomato.com. Restaurant strive at the initial stages of opening but their demand deteriorates after the initial hype. Further business for these restaurants are largely based on their reviews. What can the restaurant do to make their ratings better? Food taste is an obvious trigger to improve the ratings of a restaurant, but there are other factors that improve the ratings of a restaurant. Such as inclusion of cuisine, option for home delivery, presence of table etc. This paper aims a creating a prediction model for the reviews and analyze the trigger event that would improve the ratings.

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Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset

Cited by 47 publications

References 4 publications

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

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