scholarly journals Risk Assessment and Automated Anomaly Detection Using a Deep Learning Architecture

2021 ◽  
Author(s):  
Stelios C.A. Thomopoulos
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Deep Learning ◽  
Anomaly Detection ◽  
European Commission ◽  
Waiting Times ◽  
Check Point ◽  
Check Points ◽  
Security Standards ◽  
Security Checks

Risk-based security is a concept introduced in order to provide security checks without inconveniencing travelers that are being checked with unqualified scrutiny checks while maintaining the same level of security with current check point practices without compromising security standards. Furthermore, risk-based security, as a means of improving travelers’ experience at check points is expected to reduce queueing and waiting times while improving at the same travelers’ experience during checks. A number of projects have been funded by the European Commission to investigate the concept of risk-based security and develop the means and technology required to implement it. The author is the Coordinator of two of the flagship projects funded by EC on risk-based security: FLYSEC and TRESSPASS. This chapter discusses and analyses the concept of risk-based security, the inherent competing mechanism between risk assessment, screening time and level of security, and means to implement risk-based security based on anomaly detection using deep learning and artificial intelligence (AI) methods.

MEDSAL Project - Salinization of critical groundwater reserves in coastal Mediterranean areas: Identification, risk assessment and sustainable management with the use of integrated modelling and smart ICT tools

2020 ◽  
Author(s):  
Evangelos Tziritis ◽  
Vassilis Aschonitis ◽  
Gabriella Balacco ◽  
Petros Daras ◽  
Charalampos Doulgeris ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Deep Learning ◽  
Mediterranean Area ◽  
Groundwater Salinization ◽  
Learning Methods ◽  
Modeling Tools ◽  
Isotope Data ◽  
The Mediterranean ◽  
Groundwater Reserves

<p>MEDSAL is a research project (www.medsal.net) focusing on groundwater salinization in the Mediterranean area, funded by the PRIMA Program (Partnership for Research and Innovation in the Mediterranean Area), and running for 36 months starting from September 2019. MEDSAL constitutes a joint Euro-Mediterranean cooperation network of organizations from Mediterranean countries and associated states of the EU contributing national funds. The partnership involves eight academic partners from seven countries (plus an external collaborator – private firm), covering a wide range of academic experts in various scientific fields (e.g. hydrogeology, hydrogeochemistry, environmental isotopes, modeling, hydro-informatics, geostatistics, machine learning).</p><p>MEDSAL aims at developing innovative methods to identify various sources and processes of salinization and at providing an integrated set of modeling tools that capture the dynamics and risks of salinization. Thereby, it aims to secure the availability and quality of groundwater reserves in Mediterranean coastal areas, which are amongst the most vulnerable regions in the world to water scarcity and quality degradation. MEDSAL encompasses six (6) test sites located in five (5) countries: Rhodope, Greece, (ii) Samos Island, Greece, (iii) Salento, Italy, (iv) Tarsus, Turkey, (v) Boufichia, Tunisia, and (vi) Bouteldja, Algeria.</p><p>MEDSAL’s principal objectives are the following: a) Deliver new tools for the identification of complex salinization sources and processes, b) Exploit the potential of Artificial intelligence and Deep Learning methods to improve detection of patterns in multi-dimensional hydrogeochemical and isotope data, c) Elaborate tailor-made risk assessment and development of management plans by coupling salinization forecasts with climate change impacts and future scenarios, and d) Develop a public domain web-GIS Observatory for monitoring, alerting, decision support and management of coastal groundwater reserves around the Mediterranean.</p><p>MEDSAL is expected to have a significant impact on water resources availability and quality by improving the identification and development of adequate strategies and measures for the protection and management of salinization in coastal aquifers. In this context, MEDSAL will provide innovative classification and detection methods of groundwater salinization types for Mediterranean coasts, also in complex karstic and data-scarce environments. These outcomes will be reached by better integration of hydrogeochemical and environmental isotope data with physical-based groundwater flow and transport models and advanced geostatistics. Artificial intelligence and deep learning methods will be also used to improve the detection of patterns in multi-dimensional hydrogeochemical and isotope data.</p>

scholarly journals Detailed Analysis of Top 10 AI- Deep Learning Neural Networks in Intrusion Detection for Internet of Things

2021 ◽  
Author(s):  
Kanimozhi V ◽  
T. Prem Jacob
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Models ◽  
Learning Models ◽  
Neural Network Models

Abstract Although there exist various strategies for IoT Intrusion Detection, this research article sheds light on the aspect of how the application of top 10 Artificial Intelligence - Deep Learning Models can be useful for both supervised and unsupervised learning related to the IoT network traffic data. It pictures the detailed comparative analysis for IoT Anomaly Detection on sensible IoT gadgets that are instrumental in detecting IoT anomalies by the usage of the latest dataset IoT-23. Many strategies are being developed for securing the IoT networks, but still, development can be mandated. IoT security can be improved by the usage of various deep learning methods. This exploration has examined the top 10 deep-learning techniques, as the realistic IoT-23 dataset for improving the security execution of IoT network traffic. We built up various neural network models for identifying 5 kinds of IoT attack classes such as Mirai, Denial of Service (DoS), Scan, Man in the Middle attack (MITM-ARP), and Normal records. These attacks can be detected by using a "softmax" function of multiclass classification in deep-learning neural network models. This research was implemented in the Anaconda3 environment with different packages such as Pandas, NumPy, Scipy, Scikit-learn, TensorFlow 2.2, Matplotlib, and Seaborn. The utilization of AI-deep learning models embraced various domains like healthcare, banking and finance, findings and scientific researches, and the business organizations along with the concepts like the Internet of Things. We found that the top 10 deep-learning models are capable of increasing the accuracy; minimize the loss functions and the execution time for building that specific model. It contributes a major significance to IoT anomaly detection by using emerging technologies Artificial Intelligence and Deep Learning Neural Networks. Hence the alleviation of assaults that happen on an IoT organization will be effective. Among the top 10 neural networks, Convolutional neural networks, Multilayer perceptron, and Generative Adversarial Networks (GANs) output the highest accuracy scores of 0.996317, 0.996157, and 0.995829 with minimized loss function and less time pertain to the execution. This article added to completely grasp the quirks of irregularity identification of IoT anomalies. Henceforth, this research analysis depicts the implementations of the Top 10 AI-deep learning models, which come in handy that assist you to perceive different neural network models and IoT anomaly detection better.

scholarly journals Artificial Intelligence for anomaly detection by employing deep learning strategies in IoT networks using the trendy IoT-23 big data from Google's Tensorflow2.2

2021 ◽  
Author(s):  
Kanimozhi V ◽  
T. Prem Jacob
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Multilayer Perceptron ◽  
Network Traffic ◽  
Recurrent Neural Networks ◽  
Learning Models ◽  
Research Article

Abstract Although numerous profound learning models have been proposed, this research article contributed to symbolize the investigation of artificial deep learning models on sensible IoT gadgets to perform online protection in IoT network traffic by using the realistic IoT-23 dataset. This dataset is a recent network traffic dataset generated from the real-time network traffic data of IoT appliances. IoT products are utilized in various program applications such as home, commercial, mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spots of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. Henceforth, IoT anomaly detection systems based on the realistic Iot-23 big data for detecting IoT-based attacks on the artificial neural networks of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Multilayer perceptron (MLP) in IoT- cybersecurity has implemented and executed in this research article. As a result, Convolutional Neural Networks produces an outstanding performance of metric accuracy score is 0.998234, and minimal loss function is 0.008842, compare to Multilayer perceptron and Recurrent Neural Networks in IoT Anomaly Detection. Also generated well-displayed graph plots of Model_Accuracy, Learning curve of artificial Intelligence deep learning algorithms such as MLP, CNN, and RNN.

EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE USING DEEP LEARNING FOR DETECTING GASTRIC CANCER IN ENDOSCOPIC IMAGES

2018 ◽  
Author(s):  
T Hirasawa ◽  
K Aoyama ◽  
J Fujisaki ◽  
T Tada
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Deep Learning ◽  
Endoscopic Images

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 211-214
Author(s):  
Nidhi Thakkar ◽  
Miren Karamta ◽  
Seema Joshi ◽  
M. B. Potdar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Cloud Environment ◽  
Learning Techniques

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals An overview of artificial intelligence/deep learning

Pathology ◽  
2021 ◽  
Vol 53 ◽  
pp. S6
Author(s):  
Jack Garland ◽  
Mindy Hu ◽  
Kilak Kesha ◽  
Charley Glenn ◽  
Michael Duffy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning

Deep Learning and Artificial Intelligence

Data Science ◽  
2020 ◽  
pp. 161-179
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning
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