Application of advanced machine learning techniques to early network traffic classification

The fast-paced evolution of the Internet is drawing a complex context which imposes demanding requirements to assure end-to-end Quality of Service. The development of advanced intelligent approaches in networking is envisioning features that include autonomous resource allocation, fast reaction against unexpected network events and so on. Internet Network Traffic Classification constitutes a crucial source of information for Network Management, being decisive in assisting the emerging network control paradigms. Monitoring traffic flowing through network devices support tasks such as: network orchestration, traffic prioritization, network arbitration and cyberthreats detection, amongst others. The traditional traffic classifiers became obsolete owing to the rapid Internet evolution. Port-based classifiers suffer from significant accuracy losses due to port masking, meanwhile Deep Packet Inspection approaches have severe user-privacy limitations. The advent of Machine Learning has propelled the application of advanced algorithms in diverse research areas, and some learning approaches have proved as an interesting alternative to the classic traffic classification approaches. Addressing Network Traffic Classification from a Machine Learning perspective implies numerous challenges demanding research efforts to achieve feasible classifiers. In this dissertation, we endeavor to formulate and solve important research questions in Machine-Learning-based Network Traffic Classification. As a result of numerous experiments, the knowledge provided in this research constitutes an engaging case of study in which network traffic data from two different environments are successfully collected, processed and modeled. Firstly, we approached the Feature Extraction and Selection processes providing our own contributions. A Feature Extractor was designed to create Machine-Learning ready datasets from real traffic data, and a Feature Selection Filter based on fast correlation is proposed and tested in several classification datasets. Then, the original Network Traffic Classification datasets are reduced using our Selection Filter to provide efficient classification models. Many classification models based on CART Decision Trees were analyzed exhibiting excellent outcomes in identifying various Internet applications. The experiments presented in this research comprise a comparison amongst ensemble learning schemes, an exploratory study on Class Imbalance and solutions; and an analysis of IP-header predictors for early traffic classification. This thesis is presented in the form of compendium of JCR-indexed scientific manuscripts and, furthermore, one conference paper is included. In the present work we study a wide number of learning approaches employing the most advance methodology in Machine Learning. As a result, we identify the strengths and weaknesses of these algorithms, providing our own solutions to overcome the observed limitations. Shortly, this thesis proves that Machine Learning offers interesting advanced techniques that open prominent prospects in Internet Network Traffic Classification.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Automatic Face Mask Identification in Saudi Smart Cities: Using Technology to Prevent the Spread of COVID-19

The novel coronavirus that triggered the COVID-19 outburst is still active around the globe. By now, COVID- 19 has affected practically every facet of progress, most importantly, it has shaken the healthcare system like never before. At its peak, it forced Governments throughout the world into lockdowns to limit the reach of the epidemic. Based on early advisories of the World Health Organization (WHO), the only method of safeguarding oneself from being infected was to wear a face mask. Even today, with fewer cases being reported, masking oneself remains the single most effective and cheap means of prevention. As urban areas continue to grow, effective city management is essential for mitigating the increase of the deadly COVID-19 disease. The success of smart cities depends on significant upgrades to public transportation, highways, companies, homes, and municipal streets. There is room for improvement in the public bus transportation system now in place, and one of those improvements would be to use artificial intelligence. To determine if the person is wearing a face mask, you need an autonomous mask detection and alert system. Therefore, this study introduced a deep learning-based design that combines the attention-based generative adversarial network (ABGAN) with the multi-objective interactive honeybee mating optimization (MOIHBMO) approach to create an automated face mask recognition system. A set of 1386 images has been used to create a real-time dataset. This database contains 690 pictures without face masks and 686 images with them. The suggested algorithm ABGAN-MOIHBMO is compared to other traditional methods for detection of face masks, such as DL, AI, and DNN. The performance indicators used are error rate, inference speed, precision, recall, accuracy, and over fitting assessments. The results demonstrate that the proposed ABGAN-MOIHBMO outperforms the existing methodologies. It provides 96% of precision, 86% of recall, 93% for the f1 score, which are higher/better than the other, traditional methods. The error rate in ABGAN-MOIHBMO is a low 1.1%, which is lower other approaches. To predict and underline the significance of face mask use, the face mask detection technique may be employed in the future at Saudi airports, shopping centers, and other congested locations. On a larger platform, our research will be an effective instrument in helping many nations throughout the globe combat the rapid spread of this contagious illness

Effect of traffic dataset on various machine-learning algorithms when forecasting air quality

© Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://10.1108/JEDT-10-2021-0554Purpose (limit 100 words) Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic datasets on air quality predictions has not been clearly investigated. This research investigates the effects traffic dataset have on the performance of Machine Learning (ML) predictive models in air quality prediction. Design/methodology/approach (limit 100 words) To achieve this, we have set up an experiment with the control dataset having only the Air Quality (AQ) dataset and Meteorological (Met) dataset. While the experimental dataset is made up of the AQ dataset, Met dataset and Traffic dataset. Several ML models (such as Extra Trees Regressor, eXtreme Gradient Boosting Regressor, Random Forest Regressor, K-Neighbors Regressor, and five others) were trained, tested, and compared on these individual combinations of datasets to predict the volume of PM2.5, PM10, NO2, and O3 in the atmosphere at various time of the day. Findings (limit 100 words) The result obtained showed that various ML algorithms react differently to the traffic dataset despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%. Research limitations/implications (limit 100 words) This research is limited in terms of the study area and the result cannot be generalized outside of the UK as many conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research. Therefore, leaving out a few other ML algorithms. Practical implications (limit 100 words) This study reinforces the belief that the traffic dataset has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form traffic dataset in the development of an air quality prediction model. This implies that developers and researchers in air quality prediction need to identify the ML algorithms that behave in their best interest before implementation. Originality/value (limit 100 words) This will enable researchers to focus more on algorithms of benefit when using traffic datasets in air quality prediction.Peer reviewe

Satellite Communications

This study is motivated by the need to give the reader a broad view of the developments, key concepts, and technologies related to information society evolution, with a focus on the wireless communications and geoinformation technologies and their role in the environment. Giving perspective, it aims at assisting people active in the industry, the public sector, and Earth science fields as well, by providing a base for their continued work and thinking

Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

Forecasting dispersal of nonindigenous species

A leading conceptual model of the invasion process suggests that nonindigenous species (NIS) must pass through a series of ‘filters’ when dispersing from colonized to non-colonized regions. These steps include the initial dispersal of propagules, survival of these propagules upon encountering the new physicochemical environment, and biological integration into the new community. Here, I forecast invasions for two aquatic NIS, the spiny waterflea Bythotrephes longimanus and zebra mussel Dreissena polymorpha based on the entire invasion sequence using gravity models to assess movement of propagules, data on lake morphometry and physicochemistry, and data on fish community composition. The gravity models included information on movement patterns of recreationalists and life-history characteristics of the NIS that may facilitate invasions. I also contrast the abilities of a hierarchical approach to a single ‘all-in-one’ model that considered all variables simultaneously in detecting actual invasions versus false alarms. Here, the ‘all-in-one’ model was better at predicting invasions if they had, in fact, occurred. Next, I compare predictions of Bythotrephes invasions for three types of gravity models: total-flow-, production- and doubly-constrained. These models differ in the type of information required to parameterize the model. The Production-constrained model was most likely to detect actual invasions relative to false alarms, and the total-flow-constrained model was least likely to predict false positives. I also compare backcast patterns of propagule pressure for two groups of related species: one group comprising the spiny waterflea and the fishhook waterflea Cercopagis pengoi; and the other, the zebra mussel and quagga mussel Dreissena rostriformis bugensis. Differences in species\u27 life-histories may interact with various transport mechanisms to produce highly dissimilar levels of propagule pressure to inland lakes. Species with the broadest distribution had the highest propagule pressure scores. Finally, I examine the attributes of an invasion network formed by lakes invaded by spiny waterfleas connected by recreational traffic. I was interested in whether specific lakes served as ‘hubs’, and whether the network of lakes exhibited a scale-free topology. Management implications for a scale-free invasion network include a potential decrease in the overall rate of NIS spread if propagule flow from ‘hubs’ is reduced

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications