Intrusion detection method based on nonlinear correlation measure

Ambusaidi, Mohammed A.; Tan, Zhiyuan; He, Xiangjian; Nanda, Priyadarsi; Lu, Liang; Jamdagni, Aruna

doi:10.1504/ijipt.2014.066377

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…There are also approaches to anomaly detection outside of classification, such as clustering [ 11 ] and correlation analysis [ 12 ]. Clustering uses distance metrics to determine if a new point’s distance from a cluster centroid exceeds an acceptable value: if this is the case, it is considered anomalous.…”

Section: Introductionmentioning

confidence: 99%

A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

Cowton

Kyriazakis

Plötz

et al. 2018

Sensors

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We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of “healthy environmental data” had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1–7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.

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Section: Introductionmentioning

confidence: 99%

A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

Cowton

Kyriazakis

Plötz

et al. 2018

Sensors

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“…where u q is the feasible point of problem ( 6) obtained in q th inner iteration. e projected Barzilai-Borwein (PBB) method [21] is applied to solve problem (6), which is introduced in Section 4.3.3. In practice, problem ( 6) is an ill-conditioned problem.…”

Section: E Subproblem Of (W B)mentioning

confidence: 99%

“…It aims to distinguish attack data from normal behaviors. Besides the traditional rule-based detection techniques [1,2], considerable methods based on statistical theory [3,4], information theory [5,6], and machine learning [7,8] are widely used in abnormal traffic detection problem.…”

Section: Introductionmentioning

confidence: 99%

A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering

Cui

2021

Security and Communication Networks

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New vulnerabilities and ever-evolving network attacks pose great threats to today’s cyberspace security. Anomaly detection in network traffic is a promising and effective technique to enhance network security. In addition to traditional statistical analysis and rule-based detection techniques, machine learning models are introduced for intelligent detection of abnormal traffic data. In this paper, a novel model named SVM-C is proposed for the anomaly detection in network traffic. The URLs in the network traffic log are transformed into feature vectors via statistical laws and linear projection. The obtained feature vectors are fed into a support vector machine (SVM) classifier and classified as normal or abnormal. Based on the idea of SVM and clustering, we construct an optimization model to train the parameters of the feature extraction method and traffic classifier. Numerical tests indicate that the proposed model outperforms the state of the arts on all the tested datasets.

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“…To compute such correlation, Pearson's correlation coefficient is commonly used, although this coefficient is only able to express linear relations. Other correlation-based filter methods use Mutual Information (MI) (based on entropy), which is able to measure complex relations [128], but can be applied to discrete features only. CFS methods based on Mutual Information have been used for NIDS in [85,86,102].…”

Section: Feature Subset Selectionmentioning

confidence: 99%

Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process

Molina-Coronado

Mori

Mendiburu

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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The identification of cyberattacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. In addition, we also present the characteristics and motivations behind the use of each of these techniques and propose more adequate

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Intrusion detection method based on nonlinear correlation measure

Cited by 11 publications

References 26 publications

A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering

Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process

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