Bodies.dissemination-committee April 2021

bodies.dissemination-committee@internal.sparta.eu

4 participants
5 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] [Fwd: Request for SPARTA acknowledgment in paper]

by mchoras＠itti.com.pl

Dear All, The following paper (indicated some time) ago is now accepted to ICCS (Core A) conference. Title: The Methods and Approaches of Explainable Artificial Intelligence By: Szczepanski, Choras, Pawlicki, Pawlicka. Abstract: Since its creation, Artificial Intelligence has found innumerable applications and become ubiquitous in everyday lives. Increasingly, intelligent systems are being trusted with decision making; from making unnoticeable, minor choices to determining people’s fates, e.g. as part of predictive policing. This fact raises serious concerns about the explainability of the systems, that is constructing the solutions is such a way that enables humans to comprehend their results. This paper introduces the concept of Explainable Artificial Intelligence (xAI), along with its principles and methodology. Then, it proposes the general taxonomy of the xAI solutions, followed by an overview of some of the market products that utilize it. We acknowledged SPARTA. It should appear in June/July. Kind Regards, Prof. Michal Choras

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

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Bodies.dissemination-committee April 2021