Intrusion detection model with twin support vector machines

He, Jin; Zheng, Shihui

doi:10.1007/s12204-014-1524-4

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…K-Map is useful for distinction of the data but it is not supported for large volume of data. Support Vector Machine (SVM) [19][20][21][22][23] [ [25][26][27][28][29][30][31][32]: is beneficial for classification and reformation of data, but at the same time SVM only cannot powerfully in recognizing about new data. Given a set of trained-data-set which require more computational time for big-data [4,12].…”

Section: B Related Workmentioning

confidence: 99%

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Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Kumar*,

Kumar

2019

IJITEE

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 Abstract: Intrusion Detection System (IDS) is the nearly all imperative constituent of computer network security. IDSs are designed to comprehend intrusion attempts in incoming network traffic shrewdly. It deals with big volume of data containing immaterial and outmoded features, which lead to delay in training as well as testing procedures. Therefore, to minimize the false alarm and computation complexity, the features selection technique for intrusion detection has been implemented. In this paper PCA (Principal Component Analysis) and Fuzzy Inference System (FIS) have been used on kdd99 dataset to develop FC-NIDS model. PCA is used to select the attacked features to minimize the computational work, while FIS is used to develop a fuzzy inference system for accuracy in prophecy using MATLAB. The results of the experiment are tested on UCI data sets as a standard bench-mark. It has been found efficient for true prediction of intrusion as well as to reduce the false alarm rate. The proposed fuzzy logic controller IDS (FC-NIDS), is passable to covenant with signature and anomaly based attacks to get enhanced intrusion detection, decreases false alarm and to optimize complexity.

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Section: B Related Workmentioning

confidence: 99%

“…Fuzzy Logic based techniques found best in detection of intrusion for as compared to Data Mining, K-Map [21], MLP [25][26][27][28][29][30], Random Forest [25][26][27][28][29][30], SVM [32,33], Neural Network [31][32][33] and dimensionality reduction [35][36][37].…”

Section: B Related Workmentioning

confidence: 99%

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Kumar*,

Kumar

2019

IJITEE

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“…The effectiveness of TWSVM over other existing classification approaches has been validated on various benchmark datasets. TWSVM has better generalization ability and faster computational speed due to which it has been applied to several real life applications such as intrusion detection [60,61], activity recognition [62], image denoising [63], emotion recognition [64], text classification [65], defect prediction [66,67], disease diagnosis [68,69], and speaker identification [70]. Consider a binary classification problem of " " size.…”

Section: Twin Support Vector Machinementioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

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“…A classifier is constructed by using training data set and its classification ability is checked with the help of testing data set. TWSVM is used for intrusion detection which improves the detection speed and accuracy as well as decrease the time complexity [11,12].…”

Section: In 1990smentioning

confidence: 99%

Intrusion Detection Based on Improved Least Squares Multi-Classification Twin Support Vector Machine

2016

Jour. Math. Sci. Adv. Appl.

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Intrusion detection model with twin support vector machines

Cited by 16 publications

References 14 publications

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Intrusion Detection Based on Improved Least Squares Multi-Classification Twin Support Vector Machine

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