scholarly journals Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection

2017 ◽  
pp. 20-35 ◽  
Author(s):  
Jordan Frery ◽  
Amaury Habrard ◽  
Marc Sebban ◽  
Olivier Caelen ◽  
Liyun He-Guelton
Keyword(s):  
Anomaly Detection ◽  
Gradient Boosting

EMPLOYING GRADIENT BOOSTING AND ANOMALY DETECTION FOR PREDICTION OF FRAUDS IN ENERGY CONSUMPTION

2020 ◽  
pp. 30-41
Author(s):  
Ricardo Nascimento dos Santos ◽  
Sami Yamouni ◽  
Beatriz Albiero ◽  
Estevão Uyrá ◽  
Ramon Vilarino ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Gradient Boosting

scholarly journals A Host-Based Anomaly Detection Framework Using XGBoost and LSTM for IoT Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiali Wang ◽  
Xiang Lu
Keyword(s):  
Anomaly Detection ◽  
Detection System ◽  
Gradient Boosting ◽  
System Call ◽  
Iot Security ◽  
Extreme Gradient Boosting ◽  
Ip Camera ◽  
Iot Devices ◽  
System Call Sequences ◽  
Abnormal Behaviors

The Internet of Things (IoT) is rapidly spreading in various application scenarios through its salient features in ubiquitous device connections, ranging from agriculture and industry to transportation and other fields. As the increasing spread of IoT applications, IoT security is gradually becoming one of the most significant issues to guard IoT devices against various cybersecurity threats. Usually, IoT devices are the main components responsible for sensing, computing, and transmitting; in this case, how to efficiently protect the IoT device itself away from cyber attacks, like malware, virus, and worm, becomes the vital point in IoT security. This paper presents a brand new architecture of intrusion detection system (IDS) for IoT devices, which is designed to identify device- or host-oriented attacks in a lightweight manner in consideration of limited computation resources on IoT devices. To this end, in this paper, we propose a stacking model to couple the Extreme Gradient Boosting (XGBoost) model and the Long Short-Term Memory (LSTM) model together for the abnormal state analysis on the IoT devices. More specifically, we adopt the system call sequence as the indicators of abnormal behaviors. The collected system call sequences are firstly processed by the famous n-gram model, which is a common method used for host-based intrusion detections. Then, the proposed stacking model is used to identify abnormal behaviors hidden in the system call sequences. To evaluate the performance of the proposed model, we establish a real-setting IP camera system and place several typical IoT attacks on the victim IP camera. Extensive experimental evaluations show that the stacking model has outperformed other existing anomaly detection solutions, and we are able to achieve a 0.983 AUC score in real-world data. Numerical testing demonstrates that the XGBoost-LSTM stacking model has excellent performance, stability, and the ability of generalization.

scholarly journals Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1293
Author(s):  
Shamil Islamov ◽  
Alexey Grigoriev ◽  
Ilia Beloglazov ◽  
Sergey Savchenkov ◽  
Ove Tobias Gudmestad
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Oil And Gas ◽  
New Technologies ◽  
Learning Algorithms ◽  
Gas Production ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Drilling Process ◽  
Well Drilling

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.

scholarly journals Risk Factors Evaluation for Monitoring of Well Drilling

2021 ◽  
Author(s):  
Shamil Islamov ◽  
Alexey Grigoriev ◽  
Ilya Beloglazov ◽  
Sergey Savchenkov ◽  
Ove Tobias Gudmestad
Keyword(s):  
Anomaly Detection ◽  
Oil And Gas ◽  
New Technologies ◽  
Warning System ◽  
Time Frame ◽  
Gas Production ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Drilling Process ◽  
Well Drilling

Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work is describing a comparison of machine learning algorithms for anomaly detection during well drilling. Tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we use historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. Experiential results illustrated that a model based on gradient boosting can classify the complications in the drilling process best of all.

scholarly journals Employing Gradient Boosting and Anomaly Detection for Prediction of Frauds in Energy Consumption

2019 ◽  
Author(s):  
Beatriz Albiero ◽  
Ricardo Santos ◽  
Estevo Uyrá ◽  
Ramon Vilarino ◽  
Juliano Silva ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Anomaly Detection ◽  
Electric Power ◽  
Gradient Boosting

Energy fraud is a critical economical burden for electric power orga-nizations in Brazil. In this paper we present the application of novel MachineLearning algorithms to boost efficiency in detection of energy frauds. More-over, we also propose a generalized and unsupervised model for fraud detectionbased on consumption anomalies.

scholarly journals AN INTELLIGENT MODEL BASED ON DEEP TRANSFER LEARNING FOR DETECTING ANOMALIES IN CYBER-PHYSICAL SYSTEMS

2021 ◽  
pp. 124-132
Author(s):  
L. V. Sukhostat
Keyword(s):  
Anomaly Detection ◽  
Transfer Learning ◽  
Neural Model ◽  
Acoustic Signals ◽  
Cyber Physical Systems ◽  
Gradient Boosting ◽  
Industrial Equipment ◽  
Physical Systems ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

Context. The problem of detecting anomalies from signals of cyber-physical systems based on spectrogram and scalogram images is considered. The object of the research is complex industrial equipment with heterogeneous sensory systems of different nature.  Objective. The goal of the work is the development of a method for signal anomalies detection based on transfer learning with the extreme gradient boosting algorithm. Method. An approach based on transfer learning and the extreme gradient boosting algorithm, developed for detecting anomalies in acoustic signals of cyber-physical systems, is proposed. Little research has been done in this area, and therefore various pre-trained deep neural model architectures have been studied to improve anomaly detection. Transfer learning uses weights from a deep neural model, pre-trained on a large dataset, and can be applied to a small dataset to provide convergence without overfitting. The classic approach to this problem usually involves signal processing techniques that extract valuable information from sensor data. This paper performs an anomaly detection task using a deep learning architecture to work with acoustic signals that are preprocessed to produce a spectrogram and scalogram. The SPOCU activation function was considered to improve the accuracy of the proposed approach. The extreme gradient boosting algorithm was used because it has high performance and requires little computational resources during the training phase. This algorithm can significantly improve the detection of anomalies in industrial equipment signals. Results. The developed approach is implemented in software and evaluated for the anomaly detection task in acoustic signals of cyber-physical systems on the MIMII dataset. Conclusions. The conducted experiments have confirmed the efficiency of the proposed approach and allow recommending it for practical use in diagnosing the state of industrial equipment. Prospects for further research may lie in the application of ensemble approaches based on transfer learning to various real datasets to improve the performance and fault-tolerance of cyber-physical systems.

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