Unsupervised log message anomaly detection

Farzad, Amir; Gulliver, T. Aaron

doi:10.1016/j.icte.2020.06.003

Cited by 94 publications

(43 citation statements)

References 16 publications

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“…Unlike other isolation schemes, their technique provided a greater number of decision trees arbitrations that helped to identify many sparse anomalies. Similarly, Farzad et al [27] suggested a hybrid approach for log message detection where isolation forest algorithm predicted the positive samples from the dataset. In addition, autoencoder networks were implemented for feature extraction, model training, and anomaly detection.…”

Section: Related Workmentioning

confidence: 99%

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

Ali¹,

Ishak²,

Bhatti³

2021

Computers, Materials &Amp; Continua

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The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which are worth investigating. Current supervised Machine Learning (ML) based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography (CT) for disease detection. This strategy relies on a larger clinical dataset and does not focus on early symptom identification. Towards this end, an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms, fever, and cough. Specifically, various metrics, including body temperature, breathing and cough patterns, and physical activity, were considered in this study. Finally, a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals. The proposed technique shows an outlier detection with an accuracy of 89%, on average.

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Section: Related Workmentioning

confidence: 99%

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

Ali¹,

Ishak²,

Bhatti³

2021

Computers, Materials &Amp; Continua

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“…However, it depends on manual inspection heavily by developers, which is inefficient and high error rate. Therefore, many machine learning methods are applied into log anomaly detection [4,5], such as Logistic Regression (LR), Support Vector Machine (SVM), Principal Component Analysis (PCA), etc. Machine learning methods can save time, reduce the possibility of error, and further improve detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec

Wang¹,

Zhao²,

He³

et al. 2022

Computer Systems Science and Engineering

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“…Over the years, several algorithms were applied to unsupervised AAD problems, including Isolation Forest (IF) [9,10] and One-Class Support Vector Machines (OCSVM) [4,29]. Following the success of Deep Learning, there has been a growing usage of neural network architectures for AAD.…”

Section: Introductionmentioning

confidence: 99%

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Coelho

Pereira

Matos

et al. 2021

IFIP Advances in Information and Communication Technology

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Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.

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Unsupervised log message anomaly detection

Cited by 94 publications

References 16 publications

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

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