1 Mar
2021
1 Mar
'21
6:52 a.m.
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.
29 Apr
29 Apr
5:17 p.m.
Dear all,
I would like to inform you that the following SPARTA paper has been published:
Jusas, Vacius; Butkiene, Rita; Venčkauskas, Algimantas; Burbaite, Renata; Gudoniene,
Daina; Grigaliūnas, Šarūnas; Andone, Diana. 2021. "Models for Administration to
Ensure the Successful Transition to Distance Learning during the Pandemic"
Sustainability 13, no. 9: 4751. https://doi.org/10.3390/su13094751
All the best,
Algimantas Venčkauskas
______________________________________________________
Abstract
Lack of guidelines for implementing distance learning, lack of infrastructure, lack of
competencies, and security-related problems were the challenges met during the pandemic.
These challenges firstly fall on the administration of a higher education institution. To
assist in solving the challenges of the pandemic for the administration of a higher
education institution, the paper presents several models for the organization of the
processes of distance learning. These models are as follows: a conceptual model of
distance learning, a model of strategic planning of distance learning, a model of the
assessment before the start of distance learning, a model of the preparation for distance
learning, and a model of the process of distance learning and remote work. Student
profile, lecturer profile, organizational environment, assessment, and planning of the
infrastructure of information and communication technology (ICT), assessment and planning
of the virtual learning environment, and assessment of distance learning competencies of
participants of the study process are also considered. The developed models are based on
five main processes of instructional design, i.e., analysis, design, development,
implementation, and evaluation. The models provide guidelines for the administration of
higher education institutions on the preparation and delivery of distance learning during
the pandemic. The models were validated by 10 experts from different higher education
institutions. The feasibility of the data collection instrument was determined by
Cronbach’s alpha coefficient that is above 0.9.
29 Jul
29 Jul
3:48 p.m.
Dear all,
I would like to inform you that the following SPARTA paper has been published:
Morkevicius, Nerijus, Algimantas Venčkauskas, Nerijus Šatkauskas, and Jevgenijus Toldinas.
2021. "Method for Dynamic Service Orchestration in Fog Computing" Electronics
10, no. 15: 1796. https://doi.org/10.3390/electronics10151796
https://www.mdpi.com/2079-9292/10/15/1796
All the best,
Algimantas Venčkauskas
______________________________________________________
Abstract
Fog computing is meant to deal with the problems which cloud computing cannot solve alone.
As the fog is closer to a user, it can improve some very important QoS characteristics,
such as a latency and availability. One of the challenges in the fog architecture is
heterogeneous constrained devices and the dynamic nature of the end devices, which
requires a dynamic service orchestration to provide an efficient service placement inside
the fog nodes. An optimization method is needed to ensure the required level of QoS while
requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle
of the whole IoT system. A two-stage multi-objective optimization method to find the best
placement of services among available fog nodes is presented in this paper. A Pareto set
of non-dominated possible service distributions is found using the integer multi-objective
particle swarm optimization method. Then, the analytical hierarchy process is used to
choose the best service distribution according to the application-specific judgment
matrix. An illustrative scenario with experimental results is presented to demonstrate
characteristics of the proposed method.
10 Aug
10 Aug
5:58 a.m.
Dear all,
I would like to inform you that the following SPARTA paper has been published:
Toldinas, Jevgenijus, Algimantas Venčkauskas, Robertas Damaševičius, Šarūnas Grigaliūnas,
Nerijus Morkevičius, and Edgaras Baranauskas. 2021. "A Novel Approach for Network
Intrusion Detection Using Multistage Deep Learning Image Recognition" Electronics 10,
no. 15: 1854. https://doi.org/10.3390/electronics10151854
All the best,
Algimantas Venčkauskas
______________________________________________________
Abstract
The current rise in hacking and computer network attacks throughout the world has
heightened the demand for improved intrusion detection and prevention solutions. The
intrusion detection system (IDS) is critical in identifying abnormalities and assaults on
the network, which have grown in size and pervasiveness. The paper proposes a novel
approach for network intrusion detection using multistage deep learning image recognition.
The network features are transformed into four-channel (Red, Green, Blue, and Alpha)
images. The images then are used for classification to train and test the pre-trained deep
learning model ResNet50. The proposed approach is evaluated using two publicly available
benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed
approach achieves 99.8% accuracy in the detection of the generic attack. On the BOUN DDos
dataset, the suggested approach achieves 99.7% accuracy in the detection of the DDos
attack and 99.7% accuracy in the detection of the normal traffic..
1522
days inactive
1684
days old
bodies.dissemination-committee@internal.sparta.eu
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participants (1)
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Algimantas Venckauskas