Intrusion detection system using PCA and Fuzzy PCA techniques

Hadri, Amal; Chougdali, Khalid; Touahni, Raja

doi:10.1109/acosis.2016.7843930

Cited by 41 publications

(20 citation statements)

References 5 publications

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“…For feature extraction they have used PCA and LDA algorithms. Fuzzy based PCA is used to make IDS [9]. To classify K nearest neighbor is used.…”

Section: A Cascading Layered Approach For Intrusion Detection To Improve Detection Accuracy Of Major and Minor Attacksmentioning

confidence: 99%

A cascading layered approach for Intrusion Detection to improve detection accuracy of Major and Minor Attacks

2020

IJATCSE

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The growth of computer network for inter communication and data sharing has also raised the challenge of security of data over network. There are many threat or attacks in network that tries to privacy theft of data over network. Intrusion Detection System (IDS) is helpful to protect network form attacks. The attacks over network is divided into four categories i.e. User-to-Root (U2R), Root-to-User (R2L), Prob and Denial-of-Service (DoS). In this paper, a cascading layered based approach is follow to make an IDS. The approach is followed to improve the detection rate of all categories of attacks. Feature selection for Prob & DoS attacks and U2R & R2L attacks is done. An Artificial Neural Network (ANN) is used to improve the detection accuracy of U2R and R2L attacks. The training data for minor attacks is resampled to detect any novel attacks. The proposed approach gives a very good results.

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“…For feature extraction they have used PCA and LDA algorithms. Fuzzy based PCA is used to make IDS [9]. To classify K nearest neighbor is used.…”

Section: A Cascading Layered Approach For Intrusion Detection To Improve Detection Accuracy Of Major and Minor Attacksmentioning

confidence: 99%

A cascading layered approach for Intrusion Detection to improve detection accuracy of Major and Minor Attacks

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

“…Now our ultimate aim is to have a projection matrix W that can minimize the value of the objective function (13). 2 unknown variables W and d i existing in the objective function (13).…”

Section: Nonlinear 2p -Norm Pca Algorithmmentioning

confidence: 99%

“…Now our ultimate aim is to have a projection matrix W that can minimize the value of the objective function (13). 2 unknown variables W and d i existing in the objective function (13). Hence, it will not accept a closed-form solution & it is hard to straightforwardly solve the solution of the objective function (13).…”

Section: Nonlinear 2p -Norm Pca Algorithmmentioning

confidence: 99%

“…2 unknown variables W and d i existing in the objective function (13). Hence, it will not accept a closed-form solution & it is hard to straightforwardly solve the solution of the objective function (13). Therefore, an algorithm could be elaborated for alternately updating W (while keeping d i fixed) and d i (while keeping W fixed).…”

Section: Nonlinear 2p -Norm Pca Algorithmmentioning

confidence: 99%

“…Other techniques exist which are extended variants of PCA like for instance: (i) Kernel PCA [8] that maps nonlinearly the original data into a higher-dimensional space & after that it applies the PCA algorithm to extract the features (ii) the weighted PCA (WPCA) [9] it employs a weighted distance to address the impact of outliers onto the directions, (iii) the popular Fuzzy PCA [10][11][12][13] that fuzzify the original data to get the fuzzy membership for every data & transform PCA into Fuzzy PCA, (iv) Sparse PCA [14] that extends the classical PCA by introducing sparsity structures to the input variables.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear l2,p-norm based PCA for Anomaly Network Detection

Hadri¹,

Chougdali²,

Touahni³

2020

Adv. sci. technol. eng. syst. j.

Self Cite

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Intrusion detection systems are well known for their ability to detect internal and external intrusions, it usually recognizes intrusions through learning the normal behaviour of users or the normal traffic of activities in the network. So, if any suspicious activity or behaviour is detected, it informs the users of the network. Nonetheless, intrusion detection system is usually prone to a high false positive rate & a low detection rate as a consequence of the tremendous amount of meaningless information used in the network traffic utilized to create the intrusion detection system. To overcome that, many techniques like Principal Component Analysis (PCA), L 1 -PCA and 2,p -norm based PCA were suggested. However, these methods are linear and not robust to outliers. This paper introduces the nonlinear variant of the 2,p -norm principal component analysis. Namely, the nonlinear 2,p -norm principal component analysis intends to project the data sets into a more feasible form so that the meaning of the data is damaged as less as possible. The proposed technique is not uniquely robust to outliers but keeps PCA's positive properties as well. Experimental results on the datasets KDDCup99 and NSL-KDD show that the proposed technique is extra effective, robust and outperform PCA, L 1 -PCA and 2,p -norm based PCA algorithms.

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An intrusion detection system using optimized deep neural network architecture

Ramaiah

Vanmathi

Ravi

et al. 2021

Trans Emerging Tel Tech

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Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber‐attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural‐based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber‐attacks.

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Intrusion detection system using PCA and Fuzzy PCA techniques

Cited by 41 publications

References 5 publications

A cascading layered approach for Intrusion Detection to improve detection accuracy of Major and Minor Attacks

A cascading layered approach for Intrusion Detection to improve detection accuracy of Major and Minor Attacks

Nonlinear l2,p-norm based PCA for Anomaly Network Detection

An intrusion detection system using optimized deep neural network architecture

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