Anomaly Detection in Cyber-Physical System using Logistic Regression Analysis

The traditional Infrastructure as a Service (IaaS) cloud platform tends to realize high data availability by introducing dedicated storage devices. However, this heterogeneous architecture has high maintenance cost and might reduce the performance of virtual machines. In homogeneous IaaS cloud platform, servers in the platform would uniformly provide computing resources and storage resources, which effectively solve the above problems, although corresponding mechanisms need to be introduced to improve data availability. Efficient storage resource availability management is one of the key methods to improve data availability. As mechanical hard disk is the main way to realize data storage in IaaS cloud platform at present, timely and accurate prediction of mechanical hard disk failure and active data backup and migration before mechanical hard disk failure would effectively improve the data availability of IaaS cloud platform. In this paper, we propose an improved algorithm for early warning of mechanical hard disk failures. We first use the Relief feature selection algorithm to perform parameter selection. Then, we use the zero-sum game idea of Generative Adversarial Networks (GAN) to generate fewer category samples to achieve a balance of sample data. Finally, an improved Long Short-Term Memory (LSTM) model called Convolution-LSTM (C-LSTM) is used to complete accurate detection of hard disk failures and achieve fault warning. We evaluate several models using precision, recall, and Area Under Curve (AUC) value, and extensive experiments show that our proposed algorithm outperforms other algorithms for mechanical hard disk warning.

show abstract

“…(1) Logistic Regression (LR) [43]. LR is a supervised learning method often used in anomaly detection.…”

Section: Baselinementioning

confidence: 99%

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Shi

Ren

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…RF is an ensemble learning method for supervised classification that combines a large number of decision trees and makes prediction on the class with the most votes [37]. LR is a statistical approach that makes estimates by using a logistic function to model a binary dependent variable [38] and it can be used for multi‐class scenarios by applying it repeatedly as one‐against‐rest classification. The results of these standard classifiers are recorded both with and without the use of oversampling techniques.…”

Section: Methodsmentioning

confidence: 99%

A comparative analysis of CGAN‐based oversampling for anomaly detection

Ahsan

Shi

et al. 2021

IET Cyber-Phy Sys Theory & Ap

View full text Add to dashboard Cite

In this work, the problem of anomaly detection in imbalanced datasets, framed in the context of network intrusion detection is studied. A novel anomaly detection solution that takes both data‐level and algorithm‐level approaches into account to cope with the class‐imbalance problem is proposed. This solution integrates the auto‐learning ability of Reinforcement Learning with the oversampling ability of a Conditional Generative Adversarial Network (CGAN). To further investigate the potential of a CGAN, in imbalanced classification tasks, the effect of CGAN‐based oversampling on the following classifiers is examined: Naïve Bayes, Multilayer Perceptron, Random Forest and Logistic Regression. Through the experimental results, the authors demonstrate improved performance from the proposed approach, and from CGAN‐based oversampling in general, over other oversampling techniques such as Synthetic Minority Oversampling Technique and Adaptive Synthetic.

show abstract

“…Marino et.al [36] proposed a cyber-physical sensor (IREST) for detecting anomalies where unsupervised (One Class SVM) and supervised (Decision Trees and Random forests) learning models were used. In [37], Logistic Regression Analysis is implemented data from Synchrophasor systems, like Phasor Measurement Units (PMUs) for anomaly detection.…”

Section: Literature Survey On Machine Learning (Ml) Approaches For Anomaly Detection In Smart Gridmentioning

confidence: 99%

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Preethi¹,

K²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

View full text Add to dashboard Cite

Cyber Physical systems such as smart grids have the potential to address the future energy crisis. Because of the bidirectional flow of information across various domains in a smart grid, anomaly detection is one of the prime security related challenges. Machine learning models have emerged as one of the prospective artificial intelligence technologies to model supervised and unsupervised data for analysis and prediction. This paper reviews the various anomaly detection schemes in a Smart Grid Infrastructure based on machine learning techniques.

show abstract

Anomaly Detection in Cyber-Physical System using Logistic Regression Analysis

Cited by 19 publications

References 14 publications

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

A comparative analysis of CGAN‐based oversampling for anomaly detection

An Introductory Review Of Anomaly Detection Methods In Smart Grids

Contact Info

Product

Resources

About