Human error tolerant anomaly detection based on time-periodic packet sampling

Uchida, Masato

doi:10.1016/j.knosys.2016.05.050

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Few-shot learning is an emerging type of transfer learning technique. By reusing the transferrable knowledge of existing models, a classifier can be built to identify the novel category using only a few labeled training samples [13]. Along with the popularity of deep learning, few-shot learning model is increasingly drawn attention in modern industrial applications.…”

Section: B Few-shot Learning In Industrial Applicationsmentioning

confidence: 99%

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Zhou

Liang

Shimizu

et al. 2021

IEEE Trans. Ind. Inf.

205

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With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial Cyber-Physical Systems (CPS). Intelligent anomaly detection for identifying cyber/physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this study, we propose a Few-Shot Learning model with Siamese Convolution Neural Network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve False Alarm Rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

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Section: B Few-shot Learning In Industrial Applicationsmentioning

confidence: 99%

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Zhou

Liang

Shimizu

et al. 2021

IEEE Trans. Ind. Inf.

205

View full text Add to dashboard Cite

show abstract

“…In bigdata, the dataset is classified into structured and unstructured data. In this dataset feature extraction, and feature reduction are the two important attributes [11].Sampling, normalization, denoising and transformation are done by this data set to obtain noise removal, feature extraction and single input [12]. For large datasets, the sampling process is done by stratified sampling and random sampling [13].…”

Section: Introductionmentioning

confidence: 99%

“…This optimization is done rapidly and effectively by identifying the gradients from a few examples rather than the whole training set. The visible neurons are first provided to produce i v with training data and then the hidden layers j h are sampled based on the probabilities shown in(12).This process is repeated one more time so the visible neurons get updated and buried neurons provide another one-step "rebuilt" states i v and j h .Next the rise of joint likelihood task of data, the describe rule for the visible to hidden weights ij w…”

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confidence: 99%

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

Dahiya,

Srivastva

2019

IJEAT

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Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

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A Piecewise Aggregate pattern representation approach for anomaly detection in time series

Ren

Liu

et al. 2017

Knowledge-Based Systems

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Human error tolerant anomaly detection based on time-periodic packet sampling

Cited by 4 publications

References 26 publications

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

A Piecewise Aggregate pattern representation approach for anomaly detection in time series

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