Bodies.dissemination-committee March 2021

bodies.dissemination-committee@internal.sparta.eu

6 participants
7 discussions

[SPARTA - bodies.dissemination-committee] article publishing

by Algimantas Venckauskas

Dear all, I would like to inform you that the following SPARTA paper has been published: Damaševičius, Robertas; Venčkauskas, Algimantas; Toldinas, Jevgenijus; Grigaliūnas, Šarūnas. 2021. "Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection" Electronics 10, no. 4: 485. https://doi.org/10.3390/electronics10040485 https://www.mdpi.com/2079-9292/10/4/485 All the best, Algimantas Venčkauskas ______________________________________________________ Abstract The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of penetration into the information systems where confidential information is processed is malware. An attacker injects malware into a computer system, after which he has full or partial access to critical information in the information system. This paper proposes an ensemble classification-based methodology for malware detection. The first-stage classification is performed by a stacked ensemble of dense (fully connected) and convolutional neural networks (CNN), while the final stage classification is performed by a meta-learner. For a meta-learner, we explore and compare 14 classifiers. For a baseline comparison, 13 machine learning methods are used: K-Nearest Neighbors, Linear Support Vector Machine (SVM), Radial basis function (RBF) SVM, Random Forest, AdaBoost, Decision Tree, ExtraTrees, Linear Discriminant Analysis, Logistic, Neural Net, Passive Classifier, Ridge Classifier and Stochastic Gradient Descent classifier. We present the results of experiments performed on the Classification of Malware with PE headers (ClaMP) dataset. The best performance is achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner.

4 years, 2 months

[SPARTA - bodies.dissemination-committee] Submission

by Algimantas Venckauskas

Dear all, We have submitted the paper „Technical Threat Intelligence Analytics: What and How to Visualize for Analytic Process“ to the 24th International Conference ELECTRONICS 2020 Abstract: Visual Analytics uses data visualization methods for enabling compelling analysis of data by engaging graphical and visual representation. In the domain of cybersecurity, convincing visual representation of data enables to ascertain valuable observations that allow the domain experts to construct efficient cyberattack mitigation strategies and provide useful decision support. In this paper, we present a survey of the visual analytics tools and methods in the domain of cybersecurity. We explore and discuss Technical Threat Intelligence visualization tools using the Five Question Method. We conclude the analysis of the works using Moody’s Physics of Notations, and VIS4ML ontology as a methodological background of visual analytics process. This paper is still under evaluation. If it gets accepted, we will acknowledge SPARTA. Best, Algimantas Venčkauskas Kauno technologijos universitetas

4 years, 3 months

[SPARTA - bodies.dissemination-committee] Submission

by Raimundas Matulevicius

Hello, We have submitted a paper on “Development of the Information Security Management System Standard for Public Sector Organisations in Estonia” to the 24th International Conference on Business Information Systems. If accepted we will acknowledge the SPARTA project. Please see its abstract below. Best greetings, Raimundas and Mari Title: Development of the Information Security Management System Standard for Public Sector Organisations in Estonia Authors: Mari Seeba, Raimundas Matulevicius, Ilmar Toom Abstract. Standardisation gives us a common understanding or processes to do something in a commonly accepted way. In information security management, it means to achieve the appropriate security level in the context of known and unknown risks. Each government’s goal should be to provide digital services to its citizens with the acceptable level of confidentiality, integrity and availability. This study elicits the EU countries’ requirements for information security management system (ISMS) standards and provides the standards’ comparison requirements. The Estonian case is an example to illustrate the method when choosing or developing the appropriate ISMS standard to public sector organisations. -------- Information Security Research Group: <https://infosec.cs.ut.ee> -------- Dr. Raimundas Matulevičius, Professor of Information Security Institute of Computer Science University of Tartu Narva mnt 18, 51009 Tartu Estonia

4 years, 5 months

[SPARTA - bodies.dissemination-committee] Request for SPARTA acknowledgment in paper

by maryline Laurent

Dear All, We submitted Sparta paper (attached) to a journal in January 2020. Title: Securing Organization’s Data: A Role-Based Authorized Keyword Search Scheme with Efficient Decryption By: Nazatul Haque Sultan, Maryline Laurent, Vijay Varadharajan. If accepted we plan to acknowledge SPARTA. Kind Regards, Prof. Maryline Laurent Maryline Laurent Professor, Télécom SudParis Head of R3S team, CNRS UMR5157 SAMOVAR lab Cofounder of the chair Values and Policies of Personal Information 9 rue Charles Fourier, 91011 EVRY +33 (0)160764442

4 years, 6 months

Re: [SPARTA - bodies.dissemination-committee] [SPARTA - project.consortium] SPARTA PUBLISHED PAPERS

by Olga Navickienė

Dear colleagues, we have noticed there is missing LKA publication https://www.researchgate.net/publication/339165707_Information_Sharing_in_C… DOI: 10.34190/EWS.20.034 in the list https://sparta.technikon.com/03-WPs/WP12-Dissemination-and-communication/Sc… Kindly ask you to let us know if we need to provide more information about it. Best regards, Olga Navickienė General Jonas Žemaitis Military Academy of Lithuania (LKA) 2021-02-19, pn, 12:47 Catarina Valente <catarina.valente(a)inov.pt> rašė: > Dear Olga, > > Thank you for your e-mail. The publication is now on the SPARTA website. > > Best Regards, > Catarina Valente > > On 18 Feb 2021, at 10:47, Olga Navickienė <olga.navickiene(a)lka.lt> wrote: > > Dear Catarina, > > sorry for the delay. LKA has one more published paper in an Open Access: > > https://www.researchgate.net/publication/339165707_Information_Sharing_in_C… > DOI: 10.34190/EWS.20.034 > Number of reads: 328 > > Let me know if you need any further information. > > All best, > Olga > Dr. Olga Navickienė > Head of Research Projects Support Unit > General Jonas Žemaitis Military Academy of Lithuania > Phone: +370 5 210 3591 > Mobile: +370 672 72602 > > <http://www.lka.lt/lt/titulinis.html> > <https://www.facebook.com/KaroAkademija/> > <https://www.youtube.com/channel/UCwDc7dsEQFc4lIakZqbSiig> > > In cases where letters and/or their annexes provide or contain personal > data, please be kind to make sure (as a recipient of data) that the > provided details will be used and any other processing activities will be > carried out solely for the purpose(s) indicated in the documents being > sent. You must also be aware of the fact that you have to apply appropriate > technical and organisational personal data protection measures and strictly > adhere to the provisions of Regulation (EU) 2016/679 of the European > Parliament and of the Council of 27 April 2016 on the protection of natural > persons with regard to the processing of personal data and on the free > movement of such data, and repealing Directive 95/46/EC (General Data > Protection Regulation) and of other EU and the Republic of Lithuania > legal acts which regulate personal data protection. > > > 2021-01-21, kt, 16:03 Catarina Valente <catarina.valente(a)inov.pt> rašė: > >> Dear all, >> >> This e-mail is directed at the authors of the delivered papers since the >> beginning of the project. We are finalising the deliverable 12.4 and we >> found some inconsistencies. Note that the papers delivered within the >> project *must have open access*. >> >> The rules of the open data policy are here: >> >> https://ec.europa.eu/research/participants/docs/h2020-funding-guide/grants/… >> and >> >> https://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-c… >> >> There is some margin not to have the documents publics (like if it is a >> book or a proceeding), but that’s not something always clear to know and >> the best would be to have everything public either directly or a >> certain delay after the publication. >> >> We therefore ask you to provide us the following information: >> >> - The CORE ranking of the conference or journal where your paper was >> published. >> - The number of citations and downloads/ reads of your paper >> - The document I sent you attached highlights the papers that are not >> public. We ask you to tell us if, when and where will they be public? >> with the authors of the document that we can’t find publicly, if, when >> and where they we’ll be public? >> >> *We ask you to answer as soon as you can, until the Monday 25 maximum. * >> >> Catarina Valente >> SPARTA Communication Officer >> >> >> -- >> project.consortium mailing list >> project.consortium(a)server.sparta.eu >> http://server.sparta.eu/cgi-bin/mailman/listinfo/project.consortium > > >

4 years, 6 months

[SPARTA - bodies.dissemination-committee] Submission of paper

by dutertre jean-max

Dear colleagues, I would like to let you know that we submitted a new SPARTA paper to the Microelectronics Reliability journal (https://www.journals.elsevier.com/microelectronics-reliability <https://www.journals.elsevier.com/microelectronics-reliability>). It acknowledges the support of SPARTA. It extends from laser to EM perturbations the results of our previous SPARTA papers accepted at NordSec2019 and DTIS 2020. Please find attached the submitted paper. With my best regards, Jean-Max Title: Experimental Analysis of the Electromagnetic Instruction Skip Fault Model and Consequences for Software Countermeasures Authors: Menu, Alexandre and Dutertre, Jean-Max and Potin, Olivier and Rigaud, Jean-Baptiste and Danger, Jean-Luc Journal: Microelectronics Reliability journal -------------------------------------------------------------- Jean-Max Dutertre Responsable département SAS Professeur Département Systèmes et Architectures Sécurisés - SAS Ecole Nationale Supérieure des Mines de Saint-Etienne Centre Microélectronique de Provence - Campus Georges Charpak Provence 880, av. de Mimet 13541 Gardanne Bureau A.2.23 Tel : +33 (0)4 42 61 67 36 Fax : +33 (0)4 42 61 65 92 E-mail: dutertre(a)emse.fr <mailto:dutertre@emse.fr> Web : www.emse.fr/~dutertre <http://www.emse.fr/~dutertre> --------------------------------------------------------------

4 years, 7 months

[SPARTA - bodies.dissemination-committee] Submission

by Xabier Etxeberria Barrio

Hello, We have submitted a paper on “Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment” to Neural Computing and Applications journal. If accepted we will acknowledge the SPARTA project. Please see its abstract below. Best wishes, Xabi Title: Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment Authors: Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Jon Egaña-Zubia, Raul Orduna-Urrutia Abstract. In recent years, Deep Neural Network models have been developed in different fields, where they have brought many advances. However, they have also started to be used in tasks where risk is critical. A misdiagnosis of these models can lead to serious accidents or even death. This concern has led to an interest among researchers to study possible attacks on these models, discovering a long list of vulnerabilities, from which every model should be defended. The adversarial example attack is a widely known attack among researchers, who have developed several defenses to avoid such a threat. However, these defenses are as opaque as a deep neural network model, how they work is still unknown. This is why visualizing how they change the behavior of the target model is interesting in order to understand more precisely how the performance of the defended model is being modified. For this work, some defenses, against adversarial example attack, have been selected in order to visualize the behavior modification of each of them in the defended model. Adversarial training, dimensionality reduction and prediction similarity were the selected defenses, which have been developed using a model composed by convolution neural network layers and dense neural network layers. In each defense, the behavior of the original model has been compared with the behavior of the defended model, representing the target model by a graph in a visualization. -- <https://www.vicomtech.org/> Xabier Etxeberria Barrio Researcher | Investigador xetxeberria(a)vicomtech.org +[34] 943 30 92 30 Digital Security | Seguridad digital <https://www.linkedin.com/company/vicomtech> <https://www.youtube.com/user/VICOMTech> <https://twitter.com/@Vicomtech> member of: <https://graphicsvision.ai/> La información que contiene este mensaje y sus adjuntos son confidenciales y están dirigidos exclusivamente a sus destinatarios. Si recibe este mensaje por error, se ruega nos lo comunique y proceda a su borrado. The information contained in this electronic message is intended only for the personal and confidential use of the recipients designated in the original message. If you have received this communication in error, please notify us immediately by replying to the message and deleting it from your computer.

4 years, 7 months

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Bodies.dissemination-committee March 2021