Combination of R1-PCA and median LDA for anomaly network detection

Elkhadir, Zyad; Chougdali, Khalid; Benattou, Mohammed

doi:10.1109/isacv.2017.8054985

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The number of DDoS attacks are increasing due to the growing number of IoT devices with low security mechanisms and the fact that nowadays it is fairly easy to acquire attack tools. This has created a situation where large number of these devices can be used to perform distributed attacks, that is, to carry out more powerful attacks [1,2,5,7,12]. So far 24 different DDoS attack vectors have been found globally [12].…”

Section: Network Anomaly Detectionmentioning

confidence: 99%

“…Selected algorithms were Rotational Invariant L1-norm Principal Component Analysis (R1-PCA) and median Linear Discriminant Analysis (median LDA), and the focus was on detecting anomalies of Denial-Of-Service and Network Probe attacks. [7] The authors stated that the origin of PCA comes from minimizing the sum of squared errors and it is very sensitive for outliers. In their proposed method rotational invariance was used instead, which searches for the principal eigenvectors of a covariance matrix.…”

Section: Network Anomaly Detectionmentioning

confidence: 99%

“…LDA has a known small sample size problem, that is, when the actual data has more dimensions than the training data, computing becomes impossible. [7] The authors used KDDcup99 dataset for testing the proposed method and they converted all discrete values of the dataset to continuous values. For accuracy testing two factors were used: i) detection rate and ii) false positive rate.…”

Section: Network Anomaly Detectionmentioning

confidence: 99%

“…Test result table shows that there was no linear dependency compared with training data and detection rate. [7] As future work Z. Elkhadir et al stated that they would like to test the proposed dimension reduction algorithms with multidimensional network with real multidimensional network data including images and text. [7] The proposed method is well suited for detecting DoS attacks, even with low number of training data.…”

Section: Network Anomaly Detectionmentioning

confidence: 99%

“…[7] As future work Z. Elkhadir et al stated that they would like to test the proposed dimension reduction algorithms with multidimensional network with real multidimensional network data including images and text. [7] The proposed method is well suited for detecting DoS attacks, even with low number of training data. For detecting Probe attacks, the amount of training data has to be higher, as tests showed that lowest detection rate was 71,6% and that probably is not enough for good detection rate.…”

Section: Network Anomaly Detectionmentioning

confidence: 99%

See 4 more Smart Citations

State of the Art Literature Review on Network Anomaly Detection

Bodström

Hämäläinen

2018

Lecture Notes in Computer Science

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As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additionally, the expected number of connected devices as well as the fast-paced development caused by the Internet of Things, raises huge risks in cyber security that must be dealt with accordingly. When considering all above-mentioned reasons, there is no doubt that there is plenty of room for more advanced methods in network anomaly detection hence more advanced statistical methods and machine learning based techniques have been proposed recently in detecting anomalies.

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Section: Network Anomaly Detectionmentioning

confidence: 99%

Section: Network Anomaly Detectionmentioning

confidence: 99%

Section: Network Anomaly Detectionmentioning

confidence: 99%

Section: Network Anomaly Detectionmentioning

confidence: 99%

Section: Network Anomaly Detectionmentioning

confidence: 99%

See 3 more Smart Citations

State of the Art Literature Review on Network Anomaly Detection

Bodström

Hämäläinen

2018

Lecture Notes in Computer Science

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Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks

Chen

Wei³

et al. 2019

IEEE Access

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Network function virtualization (NFV) is a promising network paradigm that enables the design and implementation of novel network services with lower cost, increased agility, and faster time-to-value. However, network anomalies caused by software malfunction, hardware failure, mis-configuration, or cyber attacks can greatly degrade the performance of NFV networks. A few matrix decomposition-based methods have shown their effectiveness in finding the existence of network-wide anomalies. However, a little attention has been paid to multiple anomalies detection and anomaly devices localization. To bridge this gap, in this paper, we propose a matrix differential decomposition (MDD)-based anomaly detection and localization algorithm for NFV networks. First, an NFV network prototype is built to investigate the property of NFV networks, and the effectiveness of traditional anomaly detection methods is evaluated. Second, we detail the MDD-based Anomaly DEtection and Localization (MADEL) algorithm. Finally, a series of experiments are conducted on three different NFV networks to evaluate the performance of the proposed algorithm. Experimental results show that the MADEL algorithm could effectively detect and localize different types of network anomalies.

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A Hybrid Dimension Reduction Based Linear Discriminant Analysis for Classification of High-Dimensional Data

Zorarpacı

2021

2021 IEEE Congress on Evolutionary Computation (CEC)

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Combination of R1-PCA and median LDA for anomaly network detection

Cited by 8 publications

References 14 publications

State of the Art Literature Review on Network Anomaly Detection

State of the Art Literature Review on Network Anomaly Detection

Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks

A Hybrid Dimension Reduction Based Linear Discriminant Analysis for Classification of High-Dimensional Data

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