Commentary: Aedes albopictus and Aedes japonicus—two invasive mosquito species with different temperature niches in Europe

Demertzis, Konstantinos; Iliadis, Lazaros; Anezakis, Vardis-Dimitris

doi:10.3389/fenvs.2017.00085

Cited by 11 publications

(14 citation statements)

References 22 publications

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“…An ensemble forecasting method may not necessarily yield the maximum performance, but it definitely decreases the overall risk of a particularly poorer choice. On the other hand, it should be related by a detailed examination of the elements or structure and it should explain in-depth some critical decision points correlated to the procedure and use of the approach [9,47,48]. Some of these points are presented below.…”

Section: Ensemble Learningmentioning

confidence: 99%

“…The number of predictors comprised in the conception of an ensemble model has a big influence on the performance of the entire model. The detailed exploration of the optimal number of classifiers is a constant research issue as well as a constant consultation among researchers, and it should be noted that there are very limited studies that address this matter [9,47,48].…”

Section: Ensemble Sizementioning

confidence: 99%

“…In this study, comprehensive statistical research was used to determine the appropriate number of classifiers [49]. More recently, after the publishing of the "law of diminishing returns in ensemble construction" [9,[47][48][49][50], it is suggested that the ideal number of classifiers that can yield the highest precision in an ensemble model for a dataset is equivalent to the obtained number of classes. However, it is generally accepted that a priori determination of the number of classifiers without scientific evidence is a precarious decision that does not guarantee the quality of the model.…”

Section: Ensemble Sizementioning

confidence: 99%

“…The selection processes of the appropriate predictors to be comprised in an ensemble method [9,[47][48][49][50] should be based on the restrictions' settings and configurations that can take into consideration different decision boundaries. For example, the obvious choice of classifiers with the smallest error in training data is considered as not proper for generating ensembles, as performance in a training dataset, even when cross validation is used, may be misleading in terms of classification performance in unknown data [51].…”

Section: Model Selectionmentioning

confidence: 99%

“…In addition, an ensemble model is appropriate to effectively express the mathematical modeling of data vectors that are used to describe complicated relationships, such as the one between normal and malicious network traffic. The combination of four different algorithms facilitates the sorting process, making each classifier more robust, and it accelerates the convergence of the generic multiple model, which is less noisy than any single one [9]. Thus, this approach offers generalization and avoids overfitting which is one of the basic targets in machine learning.…”

Section: Introductionmentioning

confidence: 99%

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The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence

Demertzis

Kikiras

Tziritas

et al. 2018

BDCC

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A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network's services, but also for attacks identification and for the consequent forensics' investigations. A serious potential disadvantage of the traditional software solutions used today for computer network monitoring, and specifically for the instances of effective categorization of the encrypted or obfuscated network flow, which enforces the rebuilding of messages packets in sophisticated underlying protocols, is the requirements of computational resources. In addition, an additional significant inability of these software packages is they create high false positive rates because they are deprived of accurate predicting mechanisms. For all the reasons above, in most cases, the traditional software fails completely to recognize unidentified vulnerabilities and zero-day exploitations. This paper proposes a novel intelligence driven Network Flow Forensics Framework (NF3) which uses low utilization of computing power and resources, for the Next Generation Cognitive Computing SOC (NGC2SOC) that rely solely on advanced fully automated intelligence methods. It is an effective and accurate Ensemble Machine Learning forensics tool to Network Traffic Analysis, Demystification of Malware Traffic and Encrypted Traffic Identification.

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Section: Ensemble Learningmentioning

confidence: 99%

Section: Ensemble Sizementioning

confidence: 99%

Section: Ensemble Sizementioning

confidence: 99%

Section: Model Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence

Demertzis

Kikiras

Tziritas

et al. 2018

BDCC

Self Cite

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A Dynamic Ensemble Learning Framework for Data Stream Analysis and Real-Time Threat Detection

Demertzis

Iliadis

Anezakis

2018

Artificial Neural Networks and Machine Learning – ICANN 2018

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Can data from native mosquitoes support determining invasive species habitats? Modelling the climatic niche of Aedes japonicus japonicus (Diptera, Culicidae) in Germany

et al. 2019

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Invasive mosquito species and the pathogens they transmit represent a serious health risk to both humans and animals. Thus, predictions on their potential geographic distribution are urgently needed. In the case of a recently invaded region, only a small number of occurrence data is typically available for analysis, and absence data are not reliable. To overcome this problem, we have tested whether it is possible to determine the climatic ecological niche of an invasive mosquito species by using both the occurrence data of other, native species and machine learning. The approach is based on a support vector machine and in this scenario applied to the Asian bush mosquito (Aedes japonicus japonicus) in Germany. Presence data for this species (recorded in the Germany since 2008) as well as for three native mosquito species were used to model the potential distribution of the invasive species. We trained the model with data collected from 2011 to 2014 and compared our predicted occurrence probabilities for 2015 with observations found in the field throughout 2015 to evaluate our approach. The prediction map showed a high degree of concordance with the field data. We applied the model to medium climate conditions at an early stage of the invasion (2011-2015), and developed an explanation for declining population densities in an area in northern Germany. In addition to the already known distribution areas, our model also indicates a possible spread to Saarland, southwestern Rhineland-Palatinate and in 2015 to southern Bavaria, where the species is now being increasingly detected. However, there is also evidence that the possible distribution area under the mean climate conditions was underestimated.

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Commentary: Aedes albopictus and Aedes japonicus—two invasive mosquito species with different temperature niches in Europe

Abstract: Citation:Demertzis K, Iliadis L and Anezakis V-D (2017)

Cited by 11 publications

References 22 publications

The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence

The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence

A Dynamic Ensemble Learning Framework for Data Stream Analysis and Real-Time Threat Detection

Can data from native mosquitoes support determining invasive species habitats? Modelling the climatic niche of Aedes japonicus japonicus (Diptera, Culicidae) in Germany

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