democritus university of thraceutopia.duth.gr/~kdemertz/pptx/sicaseg.pdf · 2020. 12. 15. ·...

Konstantinos Demertzis – Lazaros Iliadis

A Computational Intelligence System

for Identification Cyber-Attacks on

Smart Energy Grids

Democritus University of ThraceDep. of Forestry & Management of the

Environment & Natural ResourcesLab of Forest-Environmental Informatics

& Computational Intelligence

3rd CryCybIW26th – 27th May 2016Hellenic Military Academy

Athens, Greece

Dataset

Methodologies

Results

References

5

6

Introduction1

Questions - Discussion5 7

Conclusions - Future Directions

6

5

7

3

2

1

4


Athens, Greece

What Is the Smart Grid?

A smart grid is an energy transmission and distributionnetwork enhanced through digital control, monitoring and

telecommunications capabilities.

It provides a real-time, two-way flow of energy andinformation to all stakeholders in the electricity chain, from

the generation plant to the commercial, industrial and

residential end user.


Athens, Greece

Smart Grid Architecture

Smart grid layers require a system of systems approach withdifferentiated security needs.

The smart grid includes different domains (Smart GridConceptual Model):

1. Bulk generation

2. Transmission

3. Distribution

4. Markets

5. Operations

6. Service Provider

7. Customer - Distributed energy resources - Electric Vehicles


Athens, Greece

Smart Grid Conceptual Model

It relies on a multitude of stakeholders, each with its own specific role and activity within a given domain.


Athens, Greece

Smart Grid Conceptual Model


Athens, Greece

IT infrastructures of Smart Grid

Information and communication infrastructures will play animportant role in connecting and optimizing the available grid

layers.

Grid operation depends on control systems called SupervisoryControl and Data Acquisition (SCADA) that monitor and

control the physical infrastructure.

At the heart of these SCADA systems are specializedcomputers known as Programmable Logic Controllers (PLCs).


Athens, Greece

Smart Grid Cyber Security Specificities

An important aspect of cyber security for critical infrastructureprotection focuses on a basic understanding and awareness of

real-world threats and vulnerabilities that exist within the

industrial automation and control system architectures used in

most process industries and manufacturing facilities.

As a large system of distributed and interconnected systems,the smart grid offers an exceptionally large attack surface.

Every asset of the smart grid is a potential target for a cyberattack.


Athens, Greece


An attack over a critical node may jeopardize the grid securityand lead a cascade effect to a whole system blackout.

The smart grid cyber security challenge is about protecting theever-growing number of smart grid assets and their

communication channels from fast-growing and continuously

evolving cyber threats.


Athens, Greece


These issues face the SCADA Systems and Distribution ControlSystems (DCS) that comprise most industrial environments,

and impact not on the common IT infrastructure like

Windows-based computers and network appliances (switches,

routers and firewalls), but also embedded "proprietary"

equipment such as programmable logic controllers (PLC),

remote terminal units (RTU), intelligent electrical device (IED),

basic process controllers (BPCS), safety instrumented systems

(SIS), operator panels, and ancillary systems that are the basis

of most integrated ICS architectures.


Athens, Greece


There are destructive cyber-attacks against SCADA systems asAdvanced Persistent Threats (APT), were able to take over the

PLCs controlling the centrifuges, reprogramming them in order

to speed up the centrifuges, leading to the destruction of

many and yet displaying a normal operating speed in order to

trick the centrifuge operators and finally can not only shut

things down but can alter their function and permanently

damage industrial equipment.


Athens, Greece

Smart Grid Attack Dataset

The figure shows thepower system framework

configuration used in

generating the attack

scenarios.

Mississippi State University and

Oak Ridge National Laboratory


Athens, Greece


In the network diagramG1 and G2 are power

generators, R1 through

R4 are Intelligent

Electronic Devices (IEDs)

that can switch the

breakers on or off.

These breakers arelabeled BR1 through

BR4.


Athens, Greece


We also have two lines.Line One spans from

breaker one (BR1) to

breaker two (BR2) and

Line Two spans from

breaker three (BR3) to

breaker four (BR4).

Each IED automaticallycontrols one breaker. R1

controls BR1, R2 controls

BR2 and son on.


Athens, Greece


The IEDs use a distanceprotection scheme which

trips the breaker on

detected faults whether

actually valid or faked

since they have no

internal validation to

detect the difference.


Athens, Greece


Operators can alsomanually issue

commands to the IEDs

R1 through R4 to

manually trip the

breakers BR1 - BR4.

The manual override isused when performing

maintenance on the lines

or other system

components.


Athens, Greece


The dataset comprised of 128 independent variables and 3classes - markers (No Events, Normal Events, Attack).


Athens, Greece


128 features are extracted there are 116 measurements from phasor measurement

units (PMU). A PMU or synchrophasor is a device which

measures the electrical waves on an electricity grid,

using a common time source for synchronization.

4 features for control panel logs 4 features for relay logs 4 features for Snort alerts logs

Marker


Athens, Greece


Types of Scenarios (classes - markers): No Events Short-circuit fault (Normal Events) – this is a short in a power

line and can occur in various locations along the line, the

location is indicated by the percentage range.

Line maintenance (Normal Events) – one or more relays aredisabled on a specific line to do maintenance for that line.


Athens, Greece


Types of Scenarios: Remote tripping command injection (Attack) – this is an

attack that sends a command to a relay which causes a

breaker to open. It can only be done once.

Relay setting change (Attack) – relays are configured with adistance protection scheme and the attacker changes the

setting to disable the relay function such that relay will not

trip for a valid fault or a valid command.

Data Injection (Attack) – here we imitate a valid fault bychanging values to parameters such as current, voltage,

sequence components etc. This attack aims to blind the

operator and causes a black out.


Athens, Greece


Preprocessing Dataset is determined and normalized to the interval

[-1,1] in order to phase the problem of prevalence of

features with wider range over the ones with a

narrower range, without being more important.

The outliers and the extreme values spotted wereremoved based on the Inter Quartile Range technique.


Athens, Greece


Inter Quartile Range technique


Athens, Greece


The final dataset containing 159,045 patterns: 48,455 No Events, 54,927 Natural and 55,663 Attack


Athens, Greece

Methodologies

Extreme Learning Machines (ELM) an emerging learning technique provides efficient unified

solutions to generalized feed-forward networks,

theories show that hidden neurons are important but can berandomly generated, independent from applications and that

ELMs have both universal approximation and classification

capabilities, build a direct link between theories namely:

ridge regression (for non-linear least squares problems), ANN generalization performance, linear system stability and matrix theory,


Athens, Greece

Methodologies

Extreme Learning Machines (ELM) works for the “generalized” Single-hidden Layer feedforward

Networks (SLFNs) but the hidden layer (or called feature

mapping) need not be tuned,

learning theory shows that the hidden nodes/neurons ofgeneralized feedforward networks needn’t be tuned andthese hidden nodes/neurons can be randomly generated,

all the hidden node parameters are independent from thetarget functions or the training datasets,

the hidden node/neuron parameters are not onlyindependent of the training data but also of each other,


Athens, Greece

Methodologies

Extreme Learning Machines (ELM) can handle non-differentiable activation functions, and do

not have issues such as finding a suitable stopping criterion,

learning rate, and learning epochs.

Advantages: ease of use, faster learning speed, higher generalization

performance,

suitable for many nonlinear activation function or kernelfunctions


Athens, Greece

Methodologies

Extreme Learning Machines (ELM)


Athens, Greece

SICASEG

A Computational Intelligence System for Identification Cyber-Attacks on Smart Energy Grids

we propose a novel system that use a Self-adaptiveEvolutionary Extreme Learning Machine (SaE-ELM).

In SaE-ELM, the hidden node learning parameters areoptimized by the Self-adaptive Differential Evolution

Algorithm (SaDEA), whose trial vector generation strategies

and their associated control parameters are self-adapted in a

strategy pool by learning from their previous experiences in

generating promising solutions, and the network output

weights are calculated using the Moore–Penrose (MP)generalized inverse.


Athens, Greece

Results

To identify the integrity of SICASEG we have compared the SaE-ELMwith other neural network methods such as:

Radial Basis Function (RBF) ANN, Group Methods of Data Handling (GMDH) Polynomial ANN (PANN) and Feed Forward Neural Networks (FNN) trained under Genetic

Algorithm (GA)

The results showed that the SaE-ELM has much faster learningspeed (run thousands times faster than conventional methods) and

has much better generalization performance and more accurate and

reliable classification results.


Athens, Greece

Results

Confusion Matrix:

No Events Natural Attack

48318 98 39 No Events

74 52645 2208 Natural

118 2950 52595 Attack


Athens, Greece

Results

The performance comparisons of algorithms:

ClassifierClassification Accuracy & Performance Metrics

ACC RMSE Precision Recall F-Score ROC Area Validation

SaE-ELM 96.55% 0.1637 0.966% 0.966 0.965% 0.996 10-fcv

RBF ANN 90.60% 0.2463 0,909% 0.907 0.907% 0.905 10-fcv

GMDH 92.66% 0.1828 0.927% 0.927 0.927% 0.980 10-fcv

PANN 91.34% 0.2162 0.914% 0.913 0.914% 0.961 10-fcv

FNN-GA 94.71% 0.2054 0.947% 0.947 0.947% 0.969 10-fcv


Athens, Greece

Future Directions

feature minimization using Principal Component Analysis (PCA) orother existing approaches,

additional computational intelligence methods such as SpikingNeural Networks could be explored and compared on the same

security task and

scalability of ELM with Hadoop Distributed File System (HDFS).


Athens, Greece

Conclusions An innovative Computational Intelligence System for

Identification Cyber-Attacks on SEGs has been introduced.

It performs classification by using a Self-adaptiveEvolutionary ELM, a very fast approach to properly classify

cyber attacks with high accuracy and generalization with

minimum computational power and resources.

This is done to enhance the energetic security and themechanisms of reaction of the general system, without

special requirements.

In this way it adds a higher degree of integrity to the rest ofthe security infrastructure of SEGs.


Athens, Greece

References[1] Chi Cheng, Wee Peng Tay, Guang-Bin Huang, Extreme learning machines for

intrusion detection, Neural Networks (IJCNN), International Joint Conference, DOI:

10.1109/IJCNN.2012.6252449, 2012.

[2] Erik Cambria, Guang-Bin Huang, Extreme Learning Machines, IEEE InTeLLIGenT

SYSTemS, 541-1672/13, 2013.

[3] Jiuwen Cao, Zhiping Lin, Guang-Bin Huang, Self-Adaptive Evolutionary Extreme

Learning Machine, Neural Process Lett (2012) 36:285–305, DOI 10.1007/s11063-012-9236-y.

[4] Yves Aillerie, Said Kayal, Jean-Pierre Mennella, Raj Samani, Sylvain Sauty, Laurent

Schmitt, Smart Grid Deployment Requires a New End-to-End Security Approach, White

Paper - Cyber Security, Intel Corp, 2015.

[5] Mete Ozay, Inaki Esnaola, Fatos T. Yarman Vural, Sanjeev R. Kulkarni, H. Vincent

Poor, Machine Learning Methods for Attack Detection in the Smart Grid, IEEE

Transactions on Neural Networks and Learning Systems, 2015, DOI:

10.1109/TNNLS.2015.2404803.

[email protected]

http://utopia.duth.gr/~kdemertz/


Athens, Greece

mailto:[email protected]

My Publications

Cyber Security informatics

Journals

1. Demertzis, K., and L. Iliadis. Cognitive Web Application Firewall to Critical Infrastructures Protection from Phishing Attacks, Journal of Computations &

Modelling, vol.9, no.2, 2019, 1-26, ISSN: 1792-7625 (print), 1792-8850 (online),

Scienpress Ltd, 2019.

2. Xing, L., K. Demertzis, and J. Yang. Identify Data Streams Anomalies by Evolving

Spiking Restricted Boltzmann Machines. Neural Computing & Application, Springer,

2019. doi:10.1007/s00521-019-04288-5.

3. Demertzis, K., L. Iliadis, and I. Bougoudis. Gryphon: A semi-supervised anomaly

detection system based on one-class evolving spiking neural network. Neural

Computing & Application, Springer, 2019. doi:10.1007/s00521-019-04363-x.

4. Demertzis, K., and L. Iliadis. 2017. Computational intelligence anti-malware

framework for android OS. Vietnam Journal of Computer Science vol. 4, 245–259. https://doi.org/10/gdp86x doi:10.1007/s40595-017-0095-3.

5. Demertzis, Konstantinos. Iliadis, L.S., Anezakis, V.-D., 2018. An innovative soft computing system for smart energy grids cybersecurity. Advances in Building Energy

Research 12, 3–24. https://doi.org/10/gdp862 6. Demertzis, Konstantinos. Kikiras, P., Tziritas, N., Sanchez, S.L., Iliadis, L., 2018. The

Next Generation Cognitive Security Operations Center: Network Flow Forensics

Using Cybersecurity Intelligence. Big Data and Cognitive Computing 2,

35. https://doi.org/10/gfkhpp

7. Demertzis, Konstantinos. Iliadis, L.S., 2016. Ladon: A Cyber Threat Bio-Inspired Intelligence Management System. Journal of Applied Mathematics & Bioinformatics,

vol.6, no.3, 2016, 45-64, ISSN: 1792-6602 (print), 1792-6939 (online), Scienpress Ltd,

2016.

8. Demertzis, K., N. Tziritas, P. Kikiras, S. L. Sanchez, and L. Iliadis. The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for

Efficient Defense against Adversarial Attacks. Big Data Cognitive Computing 3, no. 1

(2019): 6. doi:10.3390/bdcc3010006.

9. Demertzis, K.; Rantos, K.; Drosatos, G. A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem . Big Data Cogn.

Comput. 2020, 4, 9.

Book Chapters

10. Demertzis, K., and L. Iliadis. A Bio-Inspired Hybrid Artificial Intelligence Framework

for Cyber Security. In Computation, Cryptography, and Network Security. Edited by

N. J. Daras and M. T. Rassias, 161–93. Cham: Springer International Publishing, 2015. doi:10.1007/978-3-319-18275-9_7.

11. Demertzis, Konstantinos. Iliadis, L.S., 2018. Real-time Computational Intelligence Protection Framework Against Advanced Persistent Threats.

Book entitled Cyber-Security and Information Warfare, Series: Cybercrime and

Cybersecurity Research, NOVA science publishers, ISBN: 978-1-53614-385-0,

Chapter 5.

https://doi.org/10/gdp86xhttps://doi.org/10.1007/s40595-017-0095-3https://doi.org/10/gdp862https://doi.org/10/gfkhpphttps://doi.org/10.3390/bdcc3010006https://doi.org/10.1007/978-3-319-18275-9_7

12. Demertzis, K., and L. Iliadis. A Computational Intelligence System Identifying Cyber-

Attacks on Smart Energy Grids. In Modern Discrete Mathematics and Analysis: With

Applications in Cryptography, Information Systems and Modeling, Springer

Optimization and Its Applications. Edited by N. J. Daras and T. M. Rassias, 97–116. Cham: Springer International Publishing, 2018. doi:10.1007/978-3-319-74325-7_5.

Conferences

13. Demertzis, K., L. Iliadis, P. Kikiras, and N. Tziritas. Cyber-Typhon: An Online Multi-task Anomaly Detection Framework. In Artificial Intelligence Applications and

Innovations. AIAI 2019. IFIP Advances in Information and Communication Technology,

Vol. 559. Edited by J. MacIntyre, I. Maglogiannis, L. Iliadis, and E. Pimenidis. Cham:

Springer, 2019. doi:10.1007/978-3-030-19823-7_2.

14. Demertzis, K., and L. Iliadis. Bio-inspired Hybrid Intelligent Method for Detecting

Android Malware. In Knowledge, Information and Creativity Support Systems,

Advances in Intelligent Systems and Computing. Edited by S. Kunifuji, G. A.

Papadopoulos, A. M. J. Skulimowski, and J. Kacprzyk, 289–304. Springer International Publishing, 2016. doi:10.1007/978-3-319-27478-2_20.

15. Demertzis, K., and L. Iliadis. Evolving Smart URL Filter in a Zone-Based Policy Firewall

for Detecting Algorithmically Generated Malicious Domains . In Statistical Learning

and Data Sciences. Edited by A. Gammerman, V. Vovk, and H. Papadopoulos, 223–33. Lecture Notes in Computer Science. Springer International Publishing,

2015. doi:10.1007/978-3-319-17091-6_17.

16. Demertzis, K., and L. Iliadis. SAME: An Intelligent Anti-malware Extension for Android

ART Virtual Machine. In Computational Collective Intelligence. Edited by M. Núñez, N. T. Nguyen, D. Camacho, and B. Trawiński, 235–45. Lecture Notes in Computer Science. Springer International Publishing, 2015. doi:10.1007/978-3-319-24306-1_23.

17. Demertzis, K., and L. Iliadis. A Hybrid Network Anomaly and Intrusion Detection

Approach Based on Evolving Spiking Neural Network Classification . In E-Democracy,

Security, Privacy and Trust in a Digital World, Communications in Computer and

Information Science. Edited by A. B. Sideridis, Z. Kardasiadou, C. P. Yialouris, and V.

Zorkadis, 11–23. Springer International Publishing, 2014. doi:10.1007/978-3-319-11710-2_2.

18. Demertzis, K., and L. Iliadis. Evolving Computational Intelligence System for Malware

Detection. In Advanced Information Systems Engineering Workshops, Lecture Notes

in Business Information Processing. Edited by L. Iliadis, M. Papazoglou, and K. Pohl,

322–34. Springer International Publishing, 2014. doi:10.1007/978-3-319-07869-4_30. 19. Demertzis, K., L. Iliadis, and V. Anezakis. 2018. MOLESTRA: A Multi-Task Learning

Approach for Real-Time Big Data Analytics, in: 2018 Innovations in Intelligent

Systems and Applications (INISTA). Presented at the 2018 Innovations in Intelligent

Systems and Applications (INISTA), pp. 1–8. doi:10.1109/INISTA.2018.8466306. 20. Demertzis, Konstantinos. Iliadis, L., Anezakis, V.-D., 2018. A Dynamic Ensemble

Learning Framework for Data Stream Analysis and Real-Time Threat Detection, in:

Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018, Lecture Notes in Computer Science. Springer International Publishing, pp. 669–681.

21. Demertzis, Konstantinos. Iliadis, L., Spartalis, S., 2017. A Spiking One-Class Anomaly Detection Framework for Cyber-Security on Industrial Control Systems, in: Boracchi,

https://doi.org/10.1007/978-3-030-19823-7_2https://doi.org/10.1007/978-3-319-27478-2_20https://doi.org/10.1007/978-3-319-17091-6_17https://doi.org/10.1007/978-3-319-24306-1_23https://doi.org/10.1007/978-3-319-11710-2_2https://doi.org/10.1007/978-3-319-11710-2_2https://doi.org/10.1007/978-3-319-07869-4_30https://doi.org/10.1109/INISTA.2018.8466306

G., Iliadis, L., Jayne, C., Likas, A. (Eds.), Engineering Applications of Neural Networks,

Communications in Computer and Information Science. Springer International

Publishing, pp. 122–134. 22. Rantos, K., G. Drosatos, K. Demertzis, C. Ilioudis, and A. Papanikolaou. 2018.

Blockchain-based Consents Management for Personal Data Processing in the IoT

Ecosystem. Presented at the International Conference on Security and Cryptography,

pp. 572–577. doi:10.5220/0006911005720577. 23. Rantos, K., G. Drosatos, K. Demertzis, C. Ilioudis, A. Papanikolaou, and A. Kritsas.

ADvoCATE: A Consent Management Platform for Personal Data Processing in the IoT

Using Blockchain Technology. In Innovative Security Solutions for Information

Technology and Communications. SECITC 2018, Vol. 11359. Edited by J. L. Lanet and

C. Toma. Lecture Notes in Computer Science. Cham: Springer, 2019. doi:10.1007/978-

3-030-12942-2_23.

24. Demertzis, K.; Rantos, K.; Drosatos, G. A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem . Big Data Cogn.

Comput. 2020, 4, 9.

Environmental informatics

Journals

25. Demertzis, K.; Iliadis, L. GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning

Method for Hyperspectral Image Analysis and Classification . Algorithms 2020, 13, 61

26. Anezakis, V.-D., K. Demertzis, L. Iliadis, and S. Spartalis. Hybrid intelligent modeling

of wild fires risk. Evolving Systems, no. 4 (2018): 267–83. https://doi.org/10/gdp863 doi:10.1007/s12530-017-9196-6.

27. Bougoudis, I., K. Demertzis, L. Iliadis, V.-D. Anezakis, and A. Papaleonidas. 2018.

FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air

pollution in Athens. Neural Computing & Application, Springer, vol. 29, 375–388. https://doi.org/10/gc9bbf doi:10.1007/s00521-017-3125-2.

28. Bougoudis, I., K. Demertzis, and L. Iliadis. 2016. HISYCOL a hybrid computational

intelligence system for combined machine learning: the case of air pollution

modeling in Athens. Neural Computing & Application, Springer, vol. 27, 1191–1206. https://doi.org/10/f8r7vf doi:10.1007/s00521-015-1927-7.

29. Anezakis, V.-D., L. Iliadis, K. Demertzis, and G. Mallinis. Hybrid Soft Computing

Analytics of Cardiorespiratory Morbidity and Mortality Risk Due to Air Pollution .

In Information Systems for Crisis Response and Management in Mediterranean

Countries, Lecture Notes in Business Information Processing. Edited by I. M. Dokas, N.

Bellamine-Ben Saoud, J. Dugdale, and P. Díaz, 87–105. Springer International Publishing, 2017. doi:10.1007/978-3-319-67633-3_8.

30. Bougoudis, I., K. Demertzis, and L. Iliadis. Fast and low cost prediction of extreme air

pollution values with hybrid unsupervised learning. Integrated Computer-Aided

Engineering 23, no. 2 (2016): 115–27. https://doi.org/10/f8dt4t doi:10.3233/ICA-150505.

31. Demertzis, K., and L. Iliadis. 2017. Detecting invasive species with a bio-inspired

semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus .

Neural Computing & Application, Springer, vol. 28 pp. 1225–1234. https://doi.org/10/gbkgb7 doi:10.1007/s00521-016-2591-2.

32. Demertzis, Konstantinos. Iliadis, L., Anezakis, V.-D., 2017. Commentary: Aedes

albopictus and Aedes japonicus—two invasive mosquito species with different

https://doi.org/10.5220/0006911005720577https://doi.org/10.1007/978-3-030-12942-2_23https://doi.org/10.1007/978-3-030-12942-2_23

temperature niches in Europe. Frontiers Environmental Science

5. https://doi.org/10/gdp865

33. Demertzis, K., L. Iliadis, S. Avramidis, and Y. A. El-Kassaby. 2017. Machine learning use

in predicting interior spruce wood density utilizing progeny test information. Neural

Computing & Application, Springer, vol. 28, 505–519. https://doi.org/10/gdp86z doi:10.1007/s00521-015-2075-9.

34. Demertzis, Konstantinos. Iliadis, L.S., Anezakis, V.-D., 2018. Extreme deep learning in

biosecurity: the case of machine hearing for marine species identification . Journal

of Information and Telecommunication 2, 492–510. https://doi.org/10/gdwszn 35. Dimou, V., V.-D. Anezakis, K. Demertzis, and L. Iliadis. Comparative analysis of

exhaust emissions caused by chainsaws with soft computing and statistical

approaches. International Journal of Environmental Science and Technology 15, no. 7

(2018): 1597–608. https://doi.org/10/gdp864 doi:10.1007/s13762-017-1555-0. 36. Iliadis, L., V.-D. Anezakis, K. Demertzis, and G. Mallinis. 2017. Hybrid Unsupervised

Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases .

IJISCRAM 9, 13–35. https://doi.org/10/gfkhpm doi:10.4018/IJISCRAM.2017070102.

Conferences

37. Demertzis, K., L. Iliadis, and V. D. Anezakis. A Machine Hearing Framework for Real-

Time Streaming Analytics Using Lambda Architecture. In Engineering Applications of

Neural Networks. EANN 2019. Communications in Computer and Information Science,

Vol. 1000. Edited by J. Macintyre, L. Iliadis, I. Maglogiannis, and C. Jayne. Cham:

Springer, 2019. doi:10.1007/978-3-030-20257-6_21.

38. Anezakis, V., G. Mallinis, L. Iliadis, and K. Demertzis. 2018. Soft computing forecasting

of cardiovascular and respiratory incidents based on climate change scenarios , in:

2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). Presented

at the 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp.

1–8doi:10.1109/EAIS.2018.8397174. 39. Anezakis, V.-D., K. Demertzis, and L. Iliadis. 2018. Classifying with fuzzy chi-square

test: The case of invasive species. AIP Conference Proceedings 1978,

290003. https://doi.org/10/gdtm5q

40. Anezakis, V.-D., K. Demertzis, L. Iliadis, and S. Spartalis. A Hybrid Soft Computing

Approach Producing Robust Forest Fire Risk Indices. In Artificial Intelligence

Applications and Innovations, IFIP Advances in Information and Communication

Technology. Edited by L. Iliadis and I. Maglogiannis, 191–203. Springer International Publishing, 2016. doi:10.1007/978-3-319-44944-9_17.

41. Anezakis, V.-D., K. Dermetzis, L. Iliadis, and S. Spartalis. Fuzzy Cognitive Maps for

Long-Term Prognosis of the Evolution of Atmospheric Pollution, Based on Climate

Change Scenarios: The Case of Athens. In Computational Collective Intelligence.

Edited by N.-T. Nguyen, L. Iliadis, Y. Manolopoulos, and B. Trawiński, 175–86. Lecture Notes in Computer Science. Springer International Publishing, 2016. doi:10.1007/978-

3-319-45243-2_16.

42. Bougoudis, I., K. Demertzis, L. Iliadis, V.-D. Anezakis, and A. Papaleonidas. Semi-

supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers .

In Engineering Applications of Neural Networks, Communications in Computer and

Information Science. Edited by C. Jayne and L. Iliadis, 51–63. Springer International Publishing, 2016. doi:10.1007/978-3-319-44188-7_4.

43. Demertzis, Konstantinos. Anezakis, V.-D., Iliadis, L., Spartalis, S., 2018. Temporal

Modeling of Invasive Species’ Migration in Greece from Neighboring Countries Using Fuzzy Cognitive Maps, in: Iliadis, L., Maglogiannis, I., Plagianakos, V. (Eds.), Artificial

Intelligence Applications and Innovations, IFIP Advances in Information and

Communication Technology. Springer International Publishing, pp. 592–605. 44. Demertzis, K., and L. Iliadis. Adaptive Elitist Differential Evolution Extreme Learning

Machines on Big Data: Intelligent Recognition of Invasive Species . In Advances in Big

Data, Advances in Intelligent Systems and Computing. Edited by P. Angelov, Y.

Manolopoulos, L. Iliadis, A. Roy, and M. Vellasco, 333–45. Springer International Publishing, 2017. doi:10.1007/978-3-319-47898-2_34.

45. Demertzis, K., and L. Iliadis. Intelligent Bio-Inspired Detection of Food Borne

Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus

Sceleratus. In Engineering Applications of Neural Networks, Communications in

Computer and Information Science. Edited by L. Iliadis and C. Jayne, 89–99. Springer International Publishing, 2015. doi:10.1007/978-3-319-23983-5_9.

46. Demertzis, K., L. Iliadis, and V. Anezakis. 2017. A deep spiking machine-hearing

system for the case of invasive fish species, in: 2017 IEEE International Conference

on INnovations in Intelligent SysTems and Applications (INISTA). Presented at the

2017 IEEE International Conference on INnovations in Intelligent SysTems and

Applications (INISTA), pp. 23–28doi:10.1109/INISTA.2017.8001126. 47. Iliadis, L., V.-D. Anezakis, K. Demertzis, and S. Spartalis. Hybrid Soft Computing for

Atmospheric Pollution-Climate Change Data Mining. In Transactions on

Computational Collective Intelligence XXX. Edited by N. Thanh Nguyen and R.

Kowalczyk, 152–77. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018. doi:10.1007/978-3-319-99810-7_8.

Book Chapters

48. K. Demertzis, Bougoudis, I., L. Iliadis, Geospatial Big Data Analytics: Air Quality and

Pollution Forecasting Using AI and GIS, MACHINE LEARNING PARADIGMS – Advances in Data Analytics, Springer, INTELLIGENT SYSTEMS REFERENCE LIBRARY bookseries (in

press)

49. Demertzis, K., and L. Iliadis. 2018. The Impact of Climate Change on Biodiversity: The

Ecological Consequences of Invasive Species in Greece , in: Leal Filho, W., Manolas,

E., Azul, A.M., Azeiteiro, U.M., McGhie, H. (Eds.), Handbook of Climate Change

Communication: Vol. 1: Theory of Climate Change Communication, Climate Change

Management. Springer International Publishing, Cham, pp. 15–38. https://doi.org/doi:10.1007/978-3-319-69838-0_2.

Other

50. Demertzis K, Tsiotas D, Magafas L. Modeling and forecasting the COVID-19 temporal

spread in Greece: an exploratory approach based on complex network defined

splines. arXiv:200501163 [physics] [Internet]. 2020 May 3 [cited 2020 May 5];

Available from: http://arxiv.org/abs/2005.01163.

Panhellenic Conferences

51. Aνεζάκης, Bαρδής-Δημήτριος, Kωνσταντίνος Δεμερτζής, Λάζαρος Hλιάδης, (2017) Πρόβλεψη Xαλαζοπτώσεων Mέσω Mηχανικής Mάθησης. 3o Πανελλήνιο Συνέδριο Πολιτικής Προστασίας SafeEvros 2016: Oι νέες τεχνολογίες στην υπηρεσία της Πολιτικής Προστασίας, Proceedings, ISBN : 978-960-89345-7-3, Iούνιος 2017, Eκδοτικός Oίκος: ∆ημοκρίτειο Πανεπιστήμιο Θράκης.

52. Kωνσταντίνος Δεμερτζής, Λάζαρος Hλιάδης, 2019, H Αξιοποίηση των Συγχρόνων Τεχνολογιών Πληροφορικής και Επικοινωνιών στην Μουσειοπαιδαγωγική: Η Περίπτωση του Λαογραφικού Μουσείου Νέας Ορεστιάδας και Περιφέρειας , Πρακτικά 2ου Πανελλήνιου Συνεδρίου Ιστορίας και Πολιτισμού της Ορεστιάδας Πολιτιστική Κληρονομιά και Τοπική Ανάπτυξη, Έκδοση Σχολής Επιστημών Γεωπονίας και Δασολογίας Δημοκρίτειου Πανεπιστημίου Θράκης και Δήμου Ορεστιάδας, ISBN: 978-618-00-1152-4, Απρίλιος 2019, Επιμέλεια: Ευάγγελος Μανωλάς.

Greek Books Chapters

53. Κωνσταντίνος Δεμερτζής, 2018, Ενίσχυση της Διοικητικής Ικανότητας των Δήμων Μέσω της Ηλεκτρονικής Διακυβέρνησης: Η Στρατηγική των Έξυπνων Πόλεων με Σκοπό την Αειφόρο Ανάπτυξη, Θέματα Δασολογίας και Διαχείρισης Περιβάλλοντος και Φυσικών Πόρων, 10ος Τόμος: Περιβαλλοντική Πολιτική: Καλές Πρακτικές, Προβλήματα και Προοπτικές, σελ. 84 - 100, ISSN: 1791-7824, ISBN: 978-960-9698-14-6, Νοέμβριος 2018, Eκδοτικός Oίκος: Δημοκρίτειο Πανεπιστήμιο Θράκης.

54. Kωνσταντίνος Δεμερτζής, Λάζαρος Hλιάδης, 2015, Γενετική Tαυτοποίηση Xωροκατακτητικών Eιδών με Eξελιγμένες Mεθόδους Tεχνητής Nοημοσύνης: H Περίπτωση του Aσιατικού Kουνουπιού Tίγρης (Aedes Albopictus) . Θέματα Δασολογίας & Διαχείρισης Περιβάλλοντος & Φυσικών Πόρων, 7ος τόμος, Kλιματική Aλλαγή: Διεπιστημονικές Προσεγγίσεις, ISSN: 1791-7824, ISBN: 978-960-9698-11-5, Eκδοτικός Oίκος: Δημοκρίτειο Πανεπιστήμιο Θράκης.

http://utopia.duth.gr/~kdemertz/papers/HESADM.pdfhttp://utopia.duth.gr/~kdemertz/papers/HESADM.pdf

C:\Users\user\Desktop\SICASEG.compress.pdfC:\Users\user\Desktop\SICASEG.pdfC:\Users\user\Desktop\My Publications_2.pdf

C:\Users\user\Desktop\My Publications_2.pdf

democritus university of thraceutopia.duth.gr/~kdemertz/pptx/sicaseg.pdf · 2020. 12. 15. ·...

Documents