scholarly journals Using Autoencoders for Anomaly Detection and Transfer Learning in IoT

Computers ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 88
Author(s):  
Chin-Wei Tien ◽  
Tse-Yung Huang ◽  
Ping-Chun Chen ◽  
Jenq-Haur Wang
Keyword(s):  
Anomaly Detection ◽  
Transfer Learning ◽  
Real Data ◽  
Smart Devices ◽  
Learning Methods ◽  
Network Traffic Analysis ◽  
Tcp Protocol ◽  
Machine Learning Approach ◽  
Device Type ◽  
Comparable Performance

With the development of Internet of Things (IoT) technologies, more and more smart devices are connected to the Internet. Since these devices were designed for better connections with each other, very limited security mechanisms have been considered. It would be costly to develop separate security mechanisms for the diverse behaviors in different devices. Given new and changing devices and attacks, it would be helpful if the characteristics of diverse device types could be dynamically learned for better protection. In this paper, we propose a machine learning approach to device type identification through network traffic analysis for anomaly detection in IoT. Firstly, the characteristics of different device types are learned from their generated network packets using supervised learning methods. Secondly, by learning important features from selected device types, we further compare the effects of unsupervised learning methods including One-class SVM, Isolation forest, and autoencoders for dimensionality reduction. Finally, we evaluate the performance of anomaly detection by transfer learning with autoencoders. In our experiments on real data in the target factory, the best performance of device type identification can be achieved by XGBoost with an accuracy of 97.6%. When adopting autoencoders for learning features from the network packets in Modbus TCP protocol, the best F1 score of 98.36% can be achieved. Comparable performance of anomaly detection can be achieved when using autoencoders for transfer learning from the reference dataset in the literature to our target site. This shows the potential of the proposed approach for automatic anomaly detection in smart factories. Further investigation is needed to verify the proposed approach using different types of devices in different IoT environments.

scholarly journals Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact

2020 ◽  
Author(s):  
Adam Goodge ◽  
Bryan Hooi ◽  
See Kiong Ng ◽  
Wee Siong Ng
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Defense Mechanisms ◽  
Model Performance ◽  
Real Data ◽  
Learning Methods ◽  
Small Perturbations ◽  
Unsupervised Anomaly Detection ◽  
Latent Representations ◽  
Supervised Image Classification

Detecting anomalies is an important task in a wide variety of applications and domains. Deep learning methods have achieved state-of-the-art performance in anomaly detection in recent years; unsupervised methods being particularly popular. However, deep learning methods can be fragile to small perturbations in the input data. This can be exploited by an adversary to deliberately hinder model performance; an adversarial attack. This phenomena has been widely studied in the context of supervised image classification since its discovery, however such studies for an anomaly detection setting are sorely lacking. Moreover, the plethora of defense mechanisms that have been proposed are often not applicable to unsupervised anomaly detection models. In this work, we study the effect of adversarial attacks on the performance of anomaly-detecting autoencoders using real data from a Cyber physical system (CPS) testbed with intervals of controlled, physical attacks as anomalies. An adversary would attempt to disguise these points as normal through adversarial perturbations. To combat this, we propose the Approximate Projection Autoencoder (APAE), which incorporates two defenses against such attacks into a general autoencoder. One of these involves a novel technique to improve robustness under adversarial impact by optimising latent representations for better reconstruction outputs.

One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

2020 ◽  
Author(s):  
Alberto Leira ◽  
Esteban Jove ◽  
Jose M Gonzalez-Cava ◽  
José-Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Side Effects ◽  
Anomaly Detection ◽  
General Anesthesia ◽  
Closed Loop ◽  
Real Data ◽  
Successful Performance ◽  
Final Model ◽  
Detection Techniques ◽  
Adverse Side Effects ◽  
Operative Recovery

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1590
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Performance Monitoring ◽  
Nonlinear Problems ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

scholarly journals How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach

2016 ◽  
Vol 64 ◽  
pp. 20-24 ◽  
Author(s):  
Daisuke Ichikawa ◽  
Toki Saito ◽  
Waka Ujita ◽  
Hiroshi Oyama
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Learning Approach ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Approach

Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

2021 ◽  
Author(s):  
Süleyman UZUN ◽  
Sezgin KAÇAR ◽  
Burak ARICIOĞLU
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Step Size ◽  
Learning Methods ◽  
Data Set ◽  
Graphic Images ◽  
Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

Spacecraft Time-Series Anomaly Detection Using Transfer Learning

2021 ◽  
Author(s):  
Sriram Baireddy ◽  
Sundip R. Desai ◽  
James L. Mathieson ◽  
Richard H. Foster ◽  
Moses W. Chan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning
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