Vassilis P. Plagianakos

143 papers A 1B 26C 15Misc 9Journal 52Unranked 35
YearRankTypeTitle / Venue / Authors
2026 J jnl
Astron. Comput.
Panagiotis N. Sakellariou, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2026 J jnl
Pattern Recognit. Lett.
Panagiotis Anagnostou, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2025 J jnl
Neural Comput. Appl.
Panagiotis C. Theocharopoulos, Panagiotis Anagnostou, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2025 J jnl
Int. J. Neural Syst.
Petros Barmpas, Panagiotis Anagnostou, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Spiros V. Georgakopoulos
2025 C conf
CIBCB
Aikaterini Bilioni, Panagiotis Anagnostou, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2025 conf
EMNLP (Findings)
Dimitris Roussis, Leon Voukoutis, Georgios Paraskevopoulos, Sokratis Sofianopoulos, Prokopis Prokopidis, Vassilis P. Plagianakos, Athanasios Katsamanis, Stelios Piperidis, Vassilis Katsouros
2025 J jnl
Neural Comput. Appl.
Panagiotis C. Theocharopoulos, Panagiotis Anagnostou, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2025 J jnl
Int. J. Neural Syst.
Paraskevi V. Tsakmaki, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2025 C conf
CIBCB
Katerina Tsiaktani, Panagiotis Anagnostou, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, John A. Gittings, Dionysios E. Raitsos, Athanasia Sergounioti, Vassilis P. Plagianakos
2024 J jnl
Int. J. Emerg. Technol. Learn.
Eleni Tzanaki, Nikos Bessas, Dionisios Vavougios, Vassilis P. Plagianakos
2024 C conf
EANN
Petros Barmpas, Panagiotis Anagnostou, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2024 B conf
CEC
Paraskevi V. Tsakmaki, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2024 B conf
CEC
Petros Barmpas, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2024 J jnl
Int. J. Emerg. Technol. Learn.
Nikos Bessas, Eleni Tzanaki, Dionisios Vavougios, Vassilis P. Plagianakos
2024 J jnl
Int. J. Eng. Pedagog.
Nikos Bessas, Eleni Tzanaki, Denis Vavougios, Vassilis P. Plagianakos
2024 J jnl
Health Inf. Sci. Syst.
Kalliopi-Maria Stathopoulou, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2024 C conf
CIBCB
Steve Stavropoulos, Elissavet Zacharopoulou, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Artemis G. Hatzigeorgiou
2024 C conf
EANN
Paraskevi V. Tsakmaki, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2024 conf
ICC
Petros Spachos, Vassilis P. Plagianakos
2024 J jnl
CoRR
Panagiotis C. Theocharopoulos, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2023 conf
DCOSS-IoT
Angeliki Katsika, Konstantinos Papageorgiou, Alexandros Fakis, Vassilis P. Plagianakos, Georgios P. Spathoulas
2023 J jnl
Neural Comput. Appl.
Panagiotis C. Theocharopoulos, Anastasia Tsoukala, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2023 conf
SEEDA-CECNSM
Angeliki Katsika, Konstantinos Papageorgiou, Alexandros Fakis, Athanasios Kakarountas, Fotis Andritsopoulos, Vassilis P. Plagianakos, Georgios P. Spathoulas
2023 conf
BigDataService
Panagiotis C. Theocharopoulos, Panagiotis Anagnostou, Anastasia Tsoukala, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2023 J jnl
CoRR
Panagiotis C. Theocharopoulos, Panagiotis Anagnostou, Anastasia Tsoukala, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2023 J jnl
J. Open Source Softw.
Panagiotis Anagnostou, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Dimitris K. Tasoulis
2023 B conf
GLOBECOM
Kalliopi Tsiampa, Lili Zhu, Petros Spachos, Vassilis P. Plagianakos
2023 B conf
IEEE Big Data
Panagiotis Anagnostou, Petros Barmpas, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2023 J jnl
CoRR
Lydia Negka, Angeliki Katsika, Georgios P. Spathoulas, Vassilis P. Plagianakos
2023 J jnl
Pattern Recognit.
Ioannis A. Nellas, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2022 J jnl
Health Inf. Sci. Syst.
Petros Barmpas, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Panagiotis Anagnostou, Matthew Prina, José Luis Ayuso-Mateos, Jerome Bickenbach, Ivet Bayes, Martin Bobak, Francisco Félix Caballero, Somnath Chatterji, Laia Egea-Cortés, Esther García-Esquinas, Matilde Leonardi, Seppo Koskinen, Ilona Koupil, Andrzej Pajak, Martin Prince, Warren Sanderson, Sergei Scherbov, Abdonas Tamosiunas, Aleksander Galas, Josep Maria Haro, Albert Sanchez-Niubo, Vassilis P. Plagianakos, Demosthenes Panagiotakos
2022 B conf
IJCNN
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Serafeim P. Moustakidis, Dimitrios Tsaopoulos, Vassilis P. Plagianakos
2022 B conf
IEEE Big Data
Konstantinos Lazaros, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos
2022 J jnl
CoRR
Panagiotis Anagnostou, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Dimitris K. Tasoulis
2022 J jnl
CoRR
Ioannis A. Nellas, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Spiros V. Georgakopoulos
2022 C conf
EANN
Panagiotis C. Theocharopoulos, Anastasia Tsoukala, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2021 conf
EFMI-STC
Konstantinos Karitis, Parisis Gallos, Ioannis S. Triantafyllou, Vassilis P. Plagianakos
2021 conf
ICDM (Workshops)
Ioannis A. Nellas, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2021 J jnl
Appl. Artif. Intell.
Panagiotis Anagnostou, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Matthew Prina, José Luis Ayuso-Mateos, Joachim Bickenbach, I. Bayes-Marin, Francisco Félix Caballero, Laia Egea-Cortés, Esther García-Esquinas, Matilde Leonardi, Sergei Scherbov, Abdonas Tamosiunas, Aleksander Galas, Josep Maria Haro, A. Sánchez-Martínez, Vassilis P. Plagianakos, Demosthenes Panagiotakos
2021 conf
CIBB
Ioannis L. Dallas, Aristidis G. Vrahatis, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2020 J jnl
Neural Comput. Appl.
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Georgios I. Mallis, Aristidis G. Vrahatis, Vassilis P. Plagianakos, Ilias G. Maglogiannis
2020 conf
EFMI-STC
George Kostikidis, Parisis Gallos, Ioannis S. Triantafyllou, Vassilis P. Plagianakos
2020 J jnl
Inf.
Aristidis G. Vrahatis, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2020 conf
MIE
Spiros V. Georgakopoulos, Parisis Gallos, Vassilis P. Plagianakos
2020 C conf
EANN
Kostas Delibasis, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Ilias Maglogiannis, Vassilis P. Plagianakos
2020 ch.
Advanced Computational Intelligence in Healthcare (7)
Aristidis G. Vrahatis, Sotiris K. Tasoulis, Ilias Maglogiannis, Vassilis P. Plagianakos
2019 C conf
CIBCB
Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2019 conf
INNSBDDL
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos
2019 C conf
EANN
Sotiris K. Tasoulis, Georgios I. Mallis, Spiros V. Georgakopoulos, Aristidis G. Vrahatis, Vassilis P. Plagianakos, Ilias G. Maglogiannis
2019 J jnl
Int. J. Artif. Intell. Tools
Yannis Manolopoulos, Vassilis P. Plagianakos
2019 B conf
IJCNN
Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2019 C conf
BIBE
Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2019 J jnl
Neural Comput. Appl.
Spiros V. Georgakopoulos, Konstantina Kottari, Kostas Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2019 conf
IEEE BigData
Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2019 C conf
CIBCB
Aristidis G. Vrahatis, Sotiris K. Tasoulis, Georgios N. Dimitrakopoulos, Vassilis P. Plagianakos
2018 J jnl
AI Mag.
Manolis Koubarakis, George A. Vouros, Georgios Chalkiadakis, Vassilis P. Plagianakos, Christos Tjortjis, Ergina Kavallieratou, Dimitris Vrakas, Nikolaos Mavridis, Georgios Petasis, Konstantinos Blekas, Anastasia Krithara
2018 Misc ed.
AIAI
Lazaros S. Iliadis, Ilias Maglogiannis, Vassilis P. Plagianakos
2018 ed.
AIAI (Workshops)
Lazaros S. Iliadis, Ilias Maglogiannis, Vassilis P. Plagianakos
2018 conf
ICANN (1)
Kostas Delibasis, Ilias Maglogiannis, Spiros V. Georgakopoulos, Konstantina Kottari, Vassilis P. Plagianakos
2018 conf
IEEE BigData
Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2018 conf
SETN
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos
2018 J jnl
CoRR
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos
2018 J jnl
IEEE Trans. Medical Imaging
Dimitrios K. Iakovidis, Spiros V. Georgakopoulos, Michael Vasilakakis, Anastasios Koulaouzidis, Vassilis P. Plagianakos
2018 conf
SETN
Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, Vassilis P. Plagianakos, Kyriakos N. Sgarbas
2018 J jnl
Neurocomputing
Spiros V. Georgakopoulos, Konstantina Kottari, Kostas Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2018 C conf
INISTA
Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2018 J jnl
CoRR
Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2018 J jnl
Multim. Tools Appl.
Konstantina Kottari, Kostas Delibasis, Vassilis P. Plagianakos
2018 conf
IEEE BigData
Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2017 C conf
EANN
Spiros V. Georgakopoulos, Vassilis P. Plagianakos
2017 conf
ICIMTH
Dimosthenis Kyriazis, Serge Autexier, Ivan Brondino, Michael J. Boniface, Lucas Donat, Vegard Engen, Rafael Fernandez, Ricardo Jiménez-Peris, Blanca Jordan, Gregor Jurak, Athanasios Kiourtis, Thanos Kosmidis, Mitja Lustrek, Ilias Maglogiannis, John Mantas, Antonio Martínez, Argyro Mavrogiorgou, Andreas Menychtas, Lydia Montandon, Cosmin-Septimiu Nechifor, Sokratis Nifakos, Alexandra Papageorgiou, Marta Patiño-Martínez, Manuel Perez, Vassilis P. Plagianakos, Dalibor Stanimirovic, Gregor Starc, Tanja Tomson, Francesco Torelli, Vicente Traver Salcedo, George Vassilacopoulos, Usman Wajid
2017 C conf
EANN
Spiros V. Georgakopoulos, Konstantina Kottari, Kostas Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2017 ed.
PCI
Vasileios Vlachos, Ilias K. Savvas, Cleo Sgouropoulou, Vassilis P. Plagianakos, Christos Douligeris, Ioannis Voyiatzis, Vassilis Tampakas, George Soultis
2016 Misc conf
AIAI
Spiros V. Georgakopoulos, Konstantina Kottari, Kostas Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2016 J jnl
Integr. Comput. Aided Eng.
Konstantinos K. Delibasis, Spiros V. Georgakopoulos, Konstantina Kottari, Vassilis P. Plagianakos, Ilias Maglogiannis
2016 conf
PETRA
Konstantinos K. Delibasis, Ilias Maglogiannis, Vassilis P. Plagianakos
2015 J jnl
Inf. Sci.
Ilias Maglogiannis, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2015 conf
IEEE BigData
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos
2015 J jnl
Int. J. Artif. Intell. Tools
Konstantinos K. Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2015 Misc conf
AIAI
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Ilias Maglogiannis, Vassilis P. Plagianakos
2014 Misc conf
AIAI
Konstantinos K. Delibasis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos, Ilias Maglogiannis
2014 Misc conf
AIAI
Konstantina Kottari, Kostas Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2014 J jnl
Artif. Intell. Rev.
Sotiris K. Tasoulis, Ilias Maglogiannis, Vassilis P. Plagianakos
2014 conf
VISAPP (2)
Konstantinos K. Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2014 J jnl
Comput. Vis. Image Underst.
Konstantinos K. Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2014 B conf
IEEE Congress on Evolutionary Computation
Vassilis P. Plagianakos
2013 conf
EANN (1)
Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Ilias Maglogiannis
2013 conf
PETRA
Konstantinos K. Delibasis, Theodosios Goudas, Vassilis P. Plagianakos, Ilias Maglogiannis
2013 C conf
BIBE
Konstantinos K. Delibasis, Vassilis P. Plagianakos, Theodosios Goudas, Ilias Maglogiannis
2013 B conf
IEEE Congress on Evolutionary Computation
Vassilis P. Plagianakos
2013 J jnl
Pattern Recognit. Lett.
Sotiris K. Tasoulis, Dimitris K. Tasoulis, Vassilis P. Plagianakos
2013 Misc conf
AIAI
Konstantinos K. Delibasis, Vassilis P. Plagianakos, Ilias Maglogiannis
2013 J jnl
Neurocomputing
Sotiris K. Tasoulis, Charalampos N. Doukas, Vassilis P. Plagianakos, Ilias Maglogiannis
2012 ed.
SETN
Ilias Maglogiannis, Vassilis P. Plagianakos, Ioannis P. Vlahavas
2012 conf
SETN
Sotiris K. Tasoulis, Dimitris K. Tasoulis, Vassilis P. Plagianakos
2012 B conf
IEEE Congress on Evolutionary Computation
Sotiris K. Tasoulis, Michael G. Epitropakis, Vassilis P. Plagianakos, Dimitris K. Tasoulis
2012 J jnl
Inf. Sci.
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2012 B conf
IEEE Congress on Evolutionary Computation
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2012 conf
SETN
Michael G. Epitropakis, Dimitris K. Tasoulis, Nicos G. Pavlidis, Vassilis P. Plagianakos, Michael N. Vrahatis
2012 B conf
IEEE Congress on Evolutionary Computation
Michael G. Epitropakis, Dimitris K. Tasoulis, Nicos G. Pavlidis, Vassilis P. Plagianakos, Michael N. Vrahatis
2012 conf
AIAI (1)
Sotiris K. Tasoulis, Ilias Maglogiannis, Vassilis P. Plagianakos
2011 conf
PETRA
Vassilis Pigadas, Charalampos Doukas, Vassilis P. Plagianakos, Ilias Maglogiannis
2011 J jnl
IEEE Trans. Evol. Comput.
Michael G. Epitropakis, Dimitris K. Tasoulis, Nicos G. Pavlidis, Vassilis P. Plagianakos, Michael N. Vrahatis
2011 conf
SDE
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2011 Misc conf
AIAI
Sotiris K. Tasoulis, Charalampos N. Doukas, Ilias Maglogiannis, Vassilis P. Plagianakos
2011 conf
EMBC
Sotiris K. Tasoulis, Charalampos N. Doukas, Ilias Maglogiannis, Vassilis P. Plagianakos
2010 J jnl
Pattern Recognit.
Sotiris K. Tasoulis, Dimitris K. Tasoulis, Vassilis P. Plagianakos
2010 B conf
IEEE Congress on Evolutionary Computation
Sotiris K. Tasoulis, Dimitris K. Tasoulis, Vassilis P. Plagianakos
2010 B conf
IEEE Congress on Evolutionary Computation
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2010 J jnl
Appl. Soft Comput.
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2010 conf
SETN
Sotiris K. Tasoulis, Charalampos N. Doukas, Ilias Maglogiannis, Vassilis P. Plagianakos
2009 B conf
IEEE Congress on Evolutionary Computation
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2009 conf
CIBB
Sotiris K. Tasoulis, Vassilis P. Plagianakos, Dimitris K. Tasoulis
2008 B conf
IEEE Congress on Evolutionary Computation
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2008 ch.
Computational Intelligence in Bioinformatics
Dimitris K. Tasoulis, Vassilis P. Plagianakos, Michael N. Vrahatis
2008 A conf
GECCO
Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2007 B conf
IEEE Congress on Evolutionary Computation
Nicos G. Pavlidis, E. G. Pavlidis, Michael G. Epitropakis, Vassilis P. Plagianakos, Michael N. Vrahatis
2006 J jnl
Artif. Intell. Medicine
Dimitris K. Tasoulis, Panagiota Spyridonos, Nicos G. Pavlidis, Vassilis P. Plagianakos, Panagiota Ravazoula, George Nikiforidis, Michael N. Vrahatis
2006 J jnl
Int. J. Bifurc. Chaos
Nicos G. Pavlidis, Dimitris K. Tasoulis, Vassilis P. Plagianakos, Michael N. Vrahatis
2006 Misc conf
AIAI
Dimitris K. Tasoulis, Vassilis P. Plagianakos, Michael N. Vrahatis
2006 J jnl
Comput. Methods Programs Biomed.
Vassilis P. Plagianakos, George D. Magoulas, Michael N. Vrahatis
2006 J jnl
Neural Comput. Appl.
Vassilis P. Plagianakos, George D. Magoulas, Michael N. Vrahatis
2006 J jnl
Oper. Res.
Nicos G. Pavlidis, Vassilis P. Plagianakos, Dimitris K. Tasoulis, Michael N. Vrahatis
2006 B conf
IEEE Congress on Evolutionary Computation
Nicos G. Pavlidis, Vassilis P. Plagianakos, Dimitris K. Tasoulis, Michael N. Vrahatis
2006 J jnl
Comput. Biol. Medicine
Dimitris K. Tasoulis, Vassilis P. Plagianakos, Michael N. Vrahatis
2005 B conf
Congress on Evolutionary Computation
Dimitris K. Tasoulis, Vassilis P. Plagianakos, Michael N. Vrahatis
2005 B conf
IJCNN
Vassilis P. Plagianakos, Dimitris K. Tasoulis, Michael N. Vrahatis
2005 B conf
IJCNN
Nicos G. Pavlidis, O. K. Tasoulis, Vassilis P. Plagianakos, George Nikiforidis, Michael N. Vrahatis
2004 J jnl
Appl. Soft Comput.
George D. Magoulas, Vassilis P. Plagianakos, Michael N. Vrahatis
2004 Misc conf
ICAISC
Dimitris K. Tasoulis, Liviu Vladutu, Vassilis P. Plagianakos, Anastasios Bezerianos, Michael N. Vrahatis
2004 B conf
IEEE Congress on Evolutionary Computation
Dimitris K. Tasoulis, Nicos G. Pavlidis, Vassilis P. Plagianakos, Michael N. Vrahatis
2004 B conf
IEEE Congress on Evolutionary Computation
Konstantinos E. Parsopoulos, Dimitris K. Tasoulis, Nicos G. Pavlidis, Vassilis P. Plagianakos, Michael N. Vrahatis
2002 J jnl
IEEE Trans. Neural Networks
Vassilis P. Plagianakos, George D. Magoulas, Michael N. Vrahatis
2002 J jnl
IEEE Trans. Neural Networks
George D. Magoulas, Vassilis P. Plagianakos, Michael N. Vrahatis
2002 J jnl
Nat. Comput.
Vassilis P. Plagianakos, Michael N. Vrahatis
2000 conf
IJCNN (1)
George D. Magoulas, Vassilis P. Plagianakos, Michael N. Vrahatis
2000 J jnl
Neural Process. Lett.
Michael N. Vrahatis, George D. Magoulas, Vassilis P. Plagianakos
2000 conf
IJCNN (5)
Vassilis P. Plagianakos, Michael N. Vrahatis
1999 B conf
IJCNN
Michael N. Vrahatis, George D. Magoulas, Vassilis P. Plagianakos
1999 conf
ICECS
George D. Magoulas, Vassilis P. Plagianakos, Michael N. Vrahatis
1999 B conf
CEC
Vassilis P. Plagianakos, Michael N. Vrahatis
1999 conf
ICECS
Vassilis P. Plagianakos, George D. Magoulas, Michael N. Vrahatis
1999 B conf
IJCNN
George D. Magoulas, Vassilis P. Plagianakos, Michael N. Vrahatis
redb/extractors/yara.py
← Index redb/extractors/yara.py python
"""
YARA Extractor - Scans binary files with YARA rules and stores matches in ClickHouse.

This extractor uses the yara-x library to scan samples against a collection of
YARA rules located in the 'yara/' folder at the project root.

Supports both:
- Pre-compiled rules (.yarac) for faster loading
- Source rules (.yar/.yara) compiled on-the-fly

Schema Design:
- yara_rules: Rule metadata stored once per unique rule (deduplicated by rule_id)
- yara_matches: Sample-rule matches with binary sha256 for efficiency
"""
import inspect
import json
import os
import re
import threading
import time
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

import xxhash
import yara_x

from redb.extractors.enum import Tag
from redb.extractors.extractor import Extractor
from redb.models.dataclasses import YaraMatch, YaraRule


# Default compiled rules filename (can be overridden via YARA_COMPILED_RULES env var)
COMPILED_RULES_FILENAME = os.getenv("YARA_COMPILED_RULES", "compiled_rules.yarac")

# Default source collection name
DEFAULT_SOURCE_COLLECTION = os.getenv("YARA_SOURCE_COLLECTION", "default")


def canonicalize_rule(rule_text: str) -> str:
    """
    Canonicalize YARA rule text for consistent hashing.

    Strips comments and normalizes whitespace, but excludes metadata
    so rules with same logic but different metadata get the same ID.
    """
    # Strip single-line comments
    text = re.sub(r'//.*$', '', rule_text, flags=re.MULTILINE)
    # Strip multi-line comments
    text = re.sub(r'/\*.*?\*/', '', text, flags=re.DOTALL)
    # Strip meta section (keep only strings/condition)
    text = re.sub(r'meta\s*:\s*[^}]+(?=strings|condition|})', '', text, flags=re.DOTALL)
    # Normalize whitespace
    text = ' '.join(text.split())
    return text


def generate_rule_id(rule_text: str) -> int:
    """
    Generate a unique rule_id from canonicalized rule content.

    Returns:
        UInt64 hash of the canonical rule content
    """
    canonical = canonicalize_rule(rule_text)
    return xxhash.xxh64(canonical.encode('utf-8')).intdigest()


def sha256_hex_to_binary(hex_str: str) -> bytes:
    """Convert SHA256 hex string to binary (32 bytes)."""
    return bytes.fromhex(hex_str)


def sha256_binary_to_hex(binary: bytes) -> str:
    """Convert SHA256 binary (32 bytes) to hex string."""
    return binary.hex()


def parse_yara_rules(file_content: str) -> List[Dict[str, Any]]:
    """
    Parse individual YARA rules from file content.

    Handles files with multiple rules and extracts:
    - rule_name: The rule identifier
    - rule_tags: List of tags (from 'rule Name : tag1 tag2 {')
    - rule_meta: Dict of metadata key-value pairs
    - rule_text: Full rule source code

    Args:
        file_content: Raw content of a .yar/.yara file

    Returns:
        List of dicts, each containing rule_name, rule_tags, rule_meta, rule_text
    """
    rules = []

    # Pattern to match rule declarations: rule Name or rule Name : tags
    # We need to find rule boundaries by tracking braces
    rule_pattern = re.compile(
        r'(?:^|\n)\s*((?:private\s+|global\s+)*rule\s+(\w+)\s*(?::\s*([^{]*))?\s*\{)',
        re.MULTILINE
    )

    matches = list(rule_pattern.finditer(file_content))

    for i, match in enumerate(matches):
        rule_start = match.start(1)  # Start of 'rule ...'
        rule_name = match.group(2)
        tags_str = match.group(3) or ""
        rule_tags = [t.strip() for t in tags_str.split() if t.strip()]

        # Find the matching closing brace by counting braces
        brace_count = 0
        rule_end = match.end()
        in_string = False
        escape_next = False

        for j, char in enumerate(file_content[match.end() - 1:], start=match.end() - 1):
            if escape_next:
                escape_next = False
                continue
            if char == '\\':
                escape_next = True
                continue
            if char == '"' and not escape_next:
                in_string = not in_string
                continue
            if in_string:
                continue
            if char == '{':
                brace_count += 1
            elif char == '}':
                brace_count -= 1
                if brace_count == 0:
                    rule_end = j + 1
                    break

        rule_text = file_content[rule_start:rule_end].strip()

        # Extract metadata from rule
        meta = {}
        meta_match = re.search(
            r'meta\s*:\s*(.*?)(?=strings\s*:|condition\s*:|$)',
            rule_text,
            re.DOTALL
        )
        if meta_match:
            meta_text = meta_match.group(1)
            for line in meta_text.strip().split('\n'):
                if '=' in line:
                    key, _, value = line.partition('=')
                    key = key.strip()
                    value = value.strip().strip('"\'')
                    if key and not key.startswith('//'):
                        meta[key] = value

        rules.append({
            'rule_name': rule_name,
            'rule_tags': rule_tags,
            'rule_meta': meta,
            'rule_text': rule_text,
        })

    return rules


class YaraBatchInsertBuffer:
    """
    Thread-safe buffer for batching YARA match inserts.

    Collects matches from multiple samples and flushes to ClickHouse
    when batch_size is reached or flush_interval expires.
    """

    def __init__(
        self,
        exporter,
        index_prefix: str,
        batch_size: int = 1000,
        flush_interval: int = 30,
    ):
        self.exporter = exporter
        self.index_prefix = index_prefix
        self.batch_size = batch_size
        self.flush_interval = flush_interval

        self.matches_buffer: List[List] = []
        self.rules_buffer: Dict[int, List] = {}  # rule_id -> rule_data (deduplicated)
        self.lock = threading.Lock()
        self.last_flush = time.time()

        # Start background flush timer
        self._stop_timer = False
        self._timer_thread = threading.Thread(target=self._flush_timer, daemon=True)
        self._timer_thread.start()

    def add(self, sha256_binary: bytes, matches: List[Tuple], rules: Dict[int, List]) -> None:
        """
        Add matches and rules to buffer.

        Args:
            sha256_binary: Binary SHA256 (32 bytes)
            matches: List of match tuples (sha256_binary, rule_id, rule_name, scan_date, match_strings)
            rules: Dict of rule_id -> rule_data tuples
        """
        with self.lock:
            self.matches_buffer.extend(matches)
            # Merge rules (deduplicated by rule_id)
            for rule_id, rule_data in rules.items():
                if rule_id not in self.rules_buffer:
                    self.rules_buffer[rule_id] = rule_data

            if len(self.matches_buffer) >= self.batch_size:
                self._flush_locked()

    def _flush_locked(self) -> None:
        """Flush buffer (must hold lock)."""
        if not self.matches_buffer:
            return

        try:
            # Insert rules first (deduplicated)
            if self.rules_buffer:
                rules_data = list(self.rules_buffer.values())
                self.exporter.batch_insert(
                    table='yara_rules',
                    rows=rules_data,
                    column_names=[
                        'rule_id', 'rule_name', 'source_collection',
                        'ingested_at', 'rule_text', 'rule_meta', 'rule_tags'
                    ],
                    column_type_names=[
                        'UInt64', 'String', 'LowCardinality(String)',
                        "DateTime64(3, 'UTC')", 'String', 'JSON', 'Array(LowCardinality(String))'
                    ]
                )

            # Insert matches
            self.exporter.batch_insert(
                table='yara_matches',
                rows=self.matches_buffer,
                column_names=[
                    'sha256', 'rule_id', 'rule_name',
                    'scan_date', 'match_strings'
                ],
                column_type_names=[
                    'FixedString(32)', 'UInt64', 'LowCardinality(String)',
                    "DateTime64(3, 'UTC')", 'Array(String)'
                ]
            )

            print(f"[YARA] Flushed {len(self.matches_buffer)} matches and {len(self.rules_buffer)} rules")

        except Exception as e:
            print(f"[YARA] Error flushing batch: {e}")

        # Clear buffers
        self.matches_buffer = []
        self.rules_buffer = {}
        self.last_flush = time.time()

    def flush(self) -> None:
        """Public flush (acquires lock)."""
        with self.lock:
            self._flush_locked()

    def _flush_timer(self) -> None:
        """Background timer for periodic flushes."""
        while not self._stop_timer:
            time.sleep(5)  # Check every 5 seconds
            with self.lock:
                if time.time() - self.last_flush > self.flush_interval and self.matches_buffer:
                    self._flush_locked()

    def stop(self) -> None:
        """Stop the background timer and flush remaining data."""
        self._stop_timer = True
        self.flush()


class YaraExtractor(Extractor):
    """
    Extractor that scans binary files with YARA rules.

    The YARA rules are loaded from the 'yara/' folder at the project root.
    Each matching rule is stored in ClickHouse with its metadata.

    Supports pre-compiled rules for faster loading:
    - If 'yara/compiled_rules.yarac' exists, it will be loaded directly
    - Otherwise, all .yar/.yara files are compiled and cached in memory
    - Use YaraExtractor.compile_and_save() to pre-compile rules
    """

    # Class-level cache for compiled YARA rules
    _compiled_rules = None
    _rules_path = None

    def __init__(
        self,
        filepath: str,
        log: Any,
        exporters: Optional[List] = None,
        index_prefix: Optional[str] = None,
        known_benign: bool = False,
        known_malicious: bool = False,
    ):
        super().__init__(
            filepath,
            log,
            exporters,
            index_prefix,
            known_benign,
            known_malicious,
        )
        self.matches: List[YaraMatch] = []
        self.rules: Dict[str, YaraRule] = {}  # rule_name -> YaraRule (deduplicated)

    @classmethod
    def get_yara_rules_path(cls) -> Path:
        """Get the path to the YARA rules directory."""
        # Default to 'yara/' in project root
        project_root = Path(__file__).parent.parent.parent
        default_path = project_root / "yara"

        # Allow override via environment variable
        rules_path = os.getenv("YARA_RULES_PATH", str(default_path))
        return Path(rules_path)

    @classmethod
    def get_compiled_rules_path(cls) -> Path:
        """Get the path to the pre-compiled rules file."""
        return cls.get_yara_rules_path() / COMPILED_RULES_FILENAME

    @classmethod
    def load_compiled_rules(cls) -> Optional[yara_x.Rules]:
        """
        Load pre-compiled YARA rules from .yarac file.

        Returns:
            Compiled Rules object or None if file doesn't exist
        """
        compiled_path = cls.get_compiled_rules_path()
        if not compiled_path.exists():
            return None

        try:
            with open(compiled_path, "rb") as f:
                rules = yara_x.Rules.deserialize_from(f)
            # Only log in debug mode (non-prod) to avoid spamming in bulk processing
            if os.getenv("SERVER_ENV") != "prod":
                print(f"[DEBUG] Loaded pre-compiled YARA rules from {compiled_path}")
            return rules
        except Exception as e:
            print(f"[WARNING] Failed to load compiled rules from {compiled_path}: {e}")
            return None

    @classmethod
    def compile_rules_from_source(cls) -> Optional[yara_x.Rules]:
        """
        Compile all YARA rules from source .yar/.yara files.

        Returns:
            Compiled Rules object or None if no rules found
        """
        rules_path = cls.get_yara_rules_path()

        if not rules_path.exists():
            return None

        # Find all .yar and .yara files recursively
        rule_files = []
        for ext in ["*.yar", "*.yara"]:
            rule_files.extend(rules_path.rglob(ext))

        if not rule_files:
            return None

        print(f"[INFO] Compiling {len(rule_files)} YARA rule files from {rules_path}")

        # Compile all rules using yara-x compiler
        compiler = yara_x.Compiler()
        compiled_count = 0
        failed_count = 0

        for rule_file in rule_files:
            try:
                with open(rule_file, "r", encoding="utf-8") as f:
                    rule_content = f.read()
                # Use the relative path from rules_path as namespace
                namespace = str(rule_file.relative_to(rules_path).parent)
                if namespace == ".":
                    namespace = "default"
                compiler.new_namespace(namespace)
                compiler.add_source(rule_content)
                compiled_count += 1
            except Exception as e:
                # Log warning but continue with other rules
                print(f"[WARNING] Failed to compile YARA rule {rule_file}: {e}")
                failed_count += 1
                continue

        try:
            rules = compiler.build()
            print(f"[INFO] Successfully compiled {compiled_count} rule files ({failed_count} failed)")
            return rules
        except Exception as e:
            print(f"[ERROR] Failed to build YARA rules: {e}")
            return None

    @classmethod
    def compile_rules(cls, force_reload: bool = False) -> Optional[yara_x.Rules]:
        """
        Get compiled YARA rules, loading from cache, .yarac file, or compiling from source.

        Priority:
        1. Return cached rules if available
        2. Load pre-compiled .yarac file if it exists
        3. Compile from source .yar/.yara files

        Args:
            force_reload: If True, ignore cache and reload rules

        Returns:
            Compiled YARA rules or None if no rules found
        """
        rules_path = cls.get_yara_rules_path()

        # Return cached rules if available and path hasn't changed
        if (
            cls._compiled_rules is not None
            and cls._rules_path == rules_path
            and not force_reload
        ):
            return cls._compiled_rules

        # Try loading pre-compiled rules first
        rules = cls.load_compiled_rules()

        # If no pre-compiled rules, compile from source
        if rules is None:
            rules = cls.compile_rules_from_source()

        # Cache the rules
        if rules is not None:
            cls._compiled_rules = rules
            cls._rules_path = rules_path

        return rules

    @classmethod
    def compile_and_save(cls, output_path: Optional[Path] = None) -> bool:
        """
        Compile all YARA rules from source and save to a .yarac file.

        This is useful for pre-compiling rules for faster loading in production.

        Args:
            output_path: Path to save compiled rules (default: yara/compiled_rules.yarac)

        Returns:
            True if successful, False otherwise
        """
        if output_path is None:
            output_path = cls.get_compiled_rules_path()

        # Force compile from source
        rules = cls.compile_rules_from_source()
        if rules is None:
            print("[ERROR] No rules to compile")
            return False

        try:
            # Ensure parent directory exists
            output_path.parent.mkdir(parents=True, exist_ok=True)

            with open(output_path, "wb") as f:
                rules.serialize_into(f)

            print(f"[INFO] Saved compiled YARA rules to {output_path}")
            return True
        except Exception as e:
            print(f"[ERROR] Failed to save compiled rules: {e}")
            return False

    def _extract_yara_matches(self) -> Tuple[List[YaraMatch], Dict[str, YaraRule]]:
        """
        Scan the binary with compiled YARA rules.

        Returns:
            Tuple of (matches list, rules dict) for each matching rule
        """
        self.log.debug(inspect.currentframe().f_code.co_name)

        compiled_rules = self.compile_rules()
        if compiled_rules is None:
            self.log.warning("No YARA rules found or compiled")
            return [], {}

        matches = []
        rules = {}

        try:
            # Scan the binary data
            scan_results = compiled_rules.scan(self.binary)

            # Process each matching rule
            for rule in scan_results.matching_rules:
                rule_name = rule.identifier

                # Extract rule metadata and store rule (deduplicated by name)
                if rule_name not in rules:
                    meta = {}
                    for identifier, value in rule.metadata:
                        meta[identifier] = str(value)
                    rules[rule_name] = YaraRule(
                        rule_name=rule_name,
                        rule_tags=list(rule.tags),
                        rule_meta=meta,
                    )

                # Extract matched string identifiers
                matched_strings = []
                for pattern in rule.patterns:
                    for match in pattern.matches:
                        matched_strings.append(pattern.identifier)

                # Remove duplicates from matched strings
                matched_strings = list(set(matched_strings))

                yara_match = YaraMatch(
                    rule_name=rule_name,
                    match_strings=matched_strings,
                )
                matches.append(yara_match)

                self.log.debug(
                    f"YARA match: {rule_name} (tags: {list(rule.tags)})"
                )

        except Exception as e:
            self.log.error(f"Error scanning with YARA: {e}")
            return [], {}

        return matches, rules

    def extract(self) -> Optional[List[YaraMatch]]:
        """
        Extract YARA matches from the binary.

        Returns:
            List of YaraMatch objects or None on error
        """
        self.log.debug(inspect.currentframe().f_code.co_name)
        try:
            self.matches, self.rules = self._extract_yara_matches()
            return self.matches if self.matches else None
        except Exception as e:
            self.log.error(f"Error extracting YARA matches: {e}")
            return None

    def tag(self) -> str:
        """Return the tag for this extractor."""
        return Tag.YARA.value

    def get_clickhouse_table(self) -> str:
        """Return the ClickHouse table name for YARA matches."""
        return "yara_matches"

    def get_clickhouse_tables(self) -> Dict[str, str]:
        """Return all ClickHouse table names for multi-table support."""
        return {
            'rules': 'yara_rules',
            'matches': 'yara_matches',
        }

    def prepare_export_data(self, exporter_type: str) -> Any:
        """
        Prepare data for export to the specified exporter type.

        Uses optimized normalized schema with two tables:
        - yara_rules: Rule metadata with rule_id (UInt64 hash)
        - yara_matches: Matches with binary sha256 (32 bytes) and rule_id

        Args:
            exporter_type: The type of exporter (e.g., 'ClickHouseExporter')

        Returns:
            Data formatted for the specified exporter
        """

        if exporter_type == "ClickHouseExporter":
            if not self.matches:
                return None

            current_time = datetime.now(timezone.utc)
            source_collection = DEFAULT_SOURCE_COLLECTION

            # Convert sha256 hex to binary (32 bytes)
            sha256_binary = sha256_hex_to_binary(self.sha256)

            # Build rules data from self.rules (deduplicated by rule_id)
            rules_data = {}  # rule_id -> rule_data
            for rule_name, rule in self.rules.items():
                rule_content = f"rule {rule_name} {{ }}"  # Simplified for now
                rule_id = generate_rule_id(rule_content)
                if rule_id not in rules_data:
                    rules_data[rule_id] = [
                        rule_id,
                        rule.rule_name,
                        source_collection,
                        current_time,  # ingested_at
                        "",  # rule_text (empty for now, populated during sync)
                        json.dumps(rule.rule_meta),
                        rule.rule_tags,
                    ]

            # Build matches data
            matches_data = []
            for match in self.matches:
                rule_content = f"rule {match.rule_name} {{ }}"
                rule_id = generate_rule_id(rule_content)

                matches_data.append([
                    sha256_binary,  # Binary sha256 (32 bytes)
                    rule_id,
                    match.rule_name,
                    current_time,  # scan_date
                    match.match_strings,
                ])

            return {
                'multi_table': True,
                'rules': {
                    'table': 'yara_rules',
                    'data': list(rules_data.values()),
                    'column_names': [
                        'rule_id',
                        'rule_name',
                        'source_collection',
                        'ingested_at',
                        'rule_text',
                        'rule_meta',
                        'rule_tags',
                    ],
                    'column_type_names': [
                        'UInt64',
                        'String',
                        'LowCardinality(String)',
                        "DateTime64(3, 'UTC')",
                        'String',
                        'JSON',
                        'Array(LowCardinality(String))',
                    ],
                },
                'matches': {
                    'table': 'yara_matches',
                    'data': matches_data,
                    'column_names': [
                        'sha256',
                        'rule_id',
                        'rule_name',
                        'scan_date',
                        'match_strings',
                    ],
                    'column_type_names': [
                        'FixedString(32)',  # Binary sha256
                        'UInt64',
                        'LowCardinality(String)',
                        "DateTime64(3, 'UTC')",
                        'Array(String)',
                    ],
                },
            }

        return None


def sync_rules_to_db(source_collection: str = None) -> bool:
    """
    Sync all YARA rules from source files to the database.

    This ensures all rules exist in yara_rules table before scanning begins.
    Should be run before batch scanning or in container initialization.

    Args:
        source_collection: Source collection name (default from env)

    Returns:
        True if successful, False otherwise
    """
    from redb import settings

    source_collection = source_collection or DEFAULT_SOURCE_COLLECTION
    rules_path = YaraExtractor.get_yara_rules_path()

    if not rules_path.exists():
        print(f"[ERROR] Rules path does not exist: {rules_path}")
        return False

    # Find all rule files
    rule_files = []
    for ext in ["*.yar", "*.yara"]:
        rule_files.extend(rules_path.rglob(ext))

    if not rule_files:
        print(f"[INFO] No rule files found in {rules_path}")
        return True

    print(f"[INFO] Syncing {len(rule_files)} rule files to database...")

    current_time = datetime.now(timezone.utc)
    rules_data = []
    total_rules = 0

    for rule_file in rule_files:
        try:
            with open(rule_file, "r", encoding="utf-8") as f:
                file_content = f.read()

            # Parse individual rules from file (handles multiple rules per file)
            parsed_rules = parse_yara_rules(file_content)

            for rule in parsed_rules:
                rule_id = generate_rule_id(rule['rule_text'])

                rules_data.append([
                    rule_id,
                    rule['rule_name'],
                    source_collection,
                    current_time,
                    rule['rule_text'],
                    json.dumps(rule['rule_meta']),
                    rule['rule_tags'],
                ])
                total_rules += 1

        except Exception as e:
            print(f"[WARNING] Failed to parse rule file {rule_file}: {e}")
            continue

    print(f"[INFO] Parsed {total_rules} individual rules from {len(rule_files)} files")

    if not rules_data:
        print("[INFO] No rules to sync")
        return True

    # Insert rules to database
    try:
        client = settings.create_clickhouse_client()
        table = "yara_rules"

        client.insert(
            table,
            rules_data,
            column_names=[
                'rule_id', 'rule_name', 'source_collection',
                'ingested_at', 'rule_text', 'rule_meta', 'rule_tags'
            ],
            column_type_names=[
                'UInt64', 'String', 'LowCardinality(String)',
                "DateTime64(3, 'UTC')", 'String', 'JSON', 'Array(LowCardinality(String))'
            ]
        )

        print(f"[INFO] Successfully synced {len(rules_data)} rules to {table}")
        client.close()
        return True

    except Exception as e:
        print(f"[ERROR] Failed to sync rules to database: {e}")
        return False


# CLI utility for pre-compiling rules and syncing
if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="YARA Rules Management Utility")
    parser.add_argument(
        "--compile",
        action="store_true",
        help="Compile all YARA rules and save to .yarac file",
    )
    parser.add_argument(
        "--sync-rules",
        action="store_true",
        help="Sync all YARA rules to the database (run before batch scanning)",
    )
    parser.add_argument(
        "--output",
        type=str,
        help="Output path for compiled rules (default: yara/compiled_rules.yarac)",
    )
    parser.add_argument(
        "--rules-path",
        type=str,
        help="Path to YARA rules directory (default: yara/)",
    )
    parser.add_argument(
        "--source-collection",
        type=str,
        help="Source collection name for rules (default: from YARA_SOURCE_COLLECTION env)",
    )

    args = parser.parse_args()

    if args.rules_path:
        os.environ["YARA_RULES_PATH"] = args.rules_path

    if args.compile:
        output_path = Path(args.output) if args.output else None
        success = YaraExtractor.compile_and_save(output_path)
        if not success:
            exit(1)

    if args.sync_rules:
        success = sync_rules_to_db(args.source_collection)
        if not success:
            exit(1)

    if not args.compile and not args.sync_rules:
        parser.print_help()