An application of automatic event detection based on neural network at St Gallen (Switzerland) deep geothermal field

2021 ◽  
Author(s):  
Paola Forlenza ◽  
Silvia Scarpetta ◽  
Ortensia Amoroso ◽  
Paolo Capuano ◽  
Roberto Scarpa
Keyword(s):  
Machine Learning ◽  
Event Detection ◽  
Seismic Noise ◽  
Spectral Characteristics ◽  
Control Measures ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Anthropogenic Activity ◽  
Automatic Event Detection ◽  
Three Components

<p>In seismology, when dealing with low signal-to-noise recordings, traditional event detection methods are often unable to recognise all the weak events hidden within the seismic noise. We are interested in investigating how machine learning techniques can be a useful tool to improve automatic event detection by recognising the similarity between events. We are  interested in studying areas where anthropogenic activity, related to the exploitation of subsoil resources, can generate induced seismicity. Therefore, it is essential to increase the detection of weak events to improve knowledge about the seismicity of the area and its related consequences.<br>The SOM (Self-Organizing Map) is an unsupervised machine learning approach that is widely used for clustering, visualization and data-exploration tasks in various applications. The SOM carries out a nonlinear mapping of data onto a two-dimensional map, preserving the most important topological and metric relationships of the data. One of the reasons for using SOM for clustering indeed is to benefit from its topological structure when interpreting the data clusters. <br>In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components.<br>The SOM was trained on dataset, recorded at the St Gallen geothermal site, composed of 388 records of seismic noise and 347 earthquakes with magnitude (ML<sup>corr</sup>) between -1.2 and 3.5 collected by the Swiss Seismological Service in 2013 while realizing well control measures after drilling and acidizing the GT-1 well.<br>We obtained promising first results as SOM strategy correctly discriminates all known earthquakes events, clustering them into different nodes, distant from the group of nodes where noise falls. We also jointly tested synthetic traces in which we have hidden events traces within seismic noise or noise artificially generated. We studied the signals of each cluster individually, assessing the similarities of the waveform and spectral characteristics for the three components. In addition, the results are also evaluated in terms of events location, hypocentral distance, magnitude, and origin time.<br>This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by the Italian Ministry of Education and Research.</p>

scholarly journals Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

2019 ◽  
pp. 1-12
Author(s):  
Mokhtar Al-Suhaiqi ◽  
Muneer A. S. Hazaa ◽  
Mohammed Albared
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approach ◽  
Plagiarism Detection ◽  
Machine Learning Approach ◽  
Cross Lingual ◽  
Cross Language

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity  and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM   classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used. 

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

2021 ◽  
pp. 1085-1099
Author(s):  
S. Abijah Roseline ◽  
S. Geetha
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Security Threat ◽  
Signature Detection ◽  
Learning Techniques ◽  
Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

scholarly journals Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2509 ◽  
Author(s):  
Kamran Shaukat ◽  
Suhuai Luo ◽  
Vijay Varadharajan ◽  
Ibrahim A. Hameed ◽  
Shan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Modern World ◽  
The Internet ◽  
Cyber Threats ◽  
Risk Detection ◽  
Learning Techniques ◽  
Made In

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

scholarly journals Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Biosensors ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 193
Author(s):  
Alanna V. Zubler ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Near Infrared ◽  
Plant Stress ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Stress Detection ◽  
Recent Advances ◽  
Wide Range

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

scholarly journals Proposal for a System Model for Offline Seismic Event Detection in Colombia

2020 ◽  
Vol 12 (12) ◽  
pp. 231
Author(s):  
Julián Miranda ◽  
Angélica Flórez ◽  
Gustavo Ospina ◽  
Ciro Gamboa ◽  
Carlos Flórez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Event Detection ◽  
Seismic Event ◽  
Detection System ◽  
Sample Selection ◽  
P Wave ◽  
Machine Learning Techniques ◽  
Seismic Events ◽  
Seismic Event Detection ◽  
Events Detection

This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system.

scholarly journals Mitigating Webshell Attacks through Machine Learning Techniques

2020 ◽  
Vol 12 (1) ◽  
pp. 12 ◽  
Author(s):  
You Guo ◽  
Hector Marco-Gisbert ◽  
Paul Keir
Keyword(s):  
Machine Learning ◽  
Feature Matching ◽  
Naive Bayes ◽  
Web Server ◽  
Naïve Bayes ◽  
Malicious Code ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model

A webshell is a command execution environment in the form of web pages. It is often used by attackers as a backdoor tool for web server operations. Accurately detecting webshells is of great significance to web server protection. Most security products detect webshells based on feature-matching methods—matching input scripts against pre-built malicious code collections. The feature-matching method has a low detection rate for obfuscated webshells. However, with the help of machine learning algorithms, webshells can be detected more efficiently and accurately. In this paper, we propose a new PHP webshell detection model, the NB-Opcode (naïve Bayes and opcode sequence) model, which is a combination of naïve Bayes classifiers and opcode sequences. Through experiments and analysis on a large number of samples, the experimental results show that the proposed method could effectively detect a range of webshells. Compared with the traditional webshell detection methods, this method improves the efficiency and accuracy of webshell detection.

scholarly journals Automatic event detection in microblogs using incremental machine learning

2017 ◽  
Vol 68 (10) ◽  
pp. 2394-2411 ◽  
Author(s):  
Tharindu Rukshan Bandaragoda ◽  
Daswin De Silva ◽  
Damminda Alahakoon
Keyword(s):  
Machine Learning ◽  
Event Detection ◽  
Automatic Event Detection

scholarly journals An Ensemble-Based Malware Detection Model Using Minimum Feature Set

MENDEL ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Zelinka ◽  
Eslam Amer
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model ◽  
Learning Techniques ◽  
Proposed Model ◽  
Positive Rate ◽  
Minimum Number

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

scholarly journals An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 53-57
Author(s):  
Yazeed Abdulmalik
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Semantic Features ◽  
Sql Injection ◽  
Detection Model ◽  
Learning Techniques ◽  
Sql Injection Attack

SQL Injection Attack (SQLIA) is a common cyberattack that target web application database. With the ever increasing and varying techniques to exploit web application SQLIA vulnerabilities, there is no a comprehensive method that can solve this kind of attacks. Therefore, these various of attack techniques required to establish many methods against in order to mitigate its threats. However, most of these methods have not yet been evaluated, where it is still just theories and require to implement and measure its performance and set its limitation. Moreover, most of the existing SQL injection countermeasures either used syntax-based detection methods or a list of predefined rules to detect the SQL injection, which is vulnerable in advance and sophisticated type of attacks because attackers create new ways to evade the detection utilizing their pre-knowledge. Although semantic-based features can improve the detection, up to our knowledge, no studies focused on extracting the semantic features from SQL stamens. This paper, investigates a designed model that can improve the efficacy of the SQL injection attack detection using machine learning techniques by extracting the semantic features that can effectively indicate the SQL injection attack. Also, a tenfold approach will be used to evaluate and validate the proposed detection model.

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