Multi-Carrier Steganographic Algorithm Using File Fragmentation of FAT FS

Steganography is considered to be not only a science, but also a craft of concealing ongoing communication by hiding messages in unsuspicious cover documents, such as texts, digital images, audio and video sequences. Its essential feature is the constant search for - often exceptionally creative - possibilities of concealing information. In computers, steganography often uses secondary memory and exchangeable memory media utilising file systems. This paper deals with the current state of the issues related to information hiding by means of hard disks, being the most important source of forensic data. This paper focuses on information hiding using the File Allocation Table (FAT) file system. It also proposes a novel multi-carrier algorithm of hiding information in file fragmentation. The algorithm provides flexibility of encoding the information to be hidden and makes steps toward optimization that allows reduction of interference with the current state of the file system, represented by the statistical values of the file fragmentation parameters

Adaptive Aggregation of Flow Records

This paper explores the problem of processing the immense volume of measurement data arising during network traffic monitoring. Due to the ever-increasing demands of current networks, observing accurate information about every single flow is virtually infeasible. In many cases the existing methods for the reduction of flow records are still not sufficient enough. Since the accurate knowledge of flows termed as "heavy-hitters" suffices to fulfill most of the monitoring purposes, we decided to aggregate the flow records pertaining to non-heavy-hitters. However, due to the ever-changing nature of traffic, their identification is a challenge. To overcome this challenge, our proposed approach - the adaptive aggregation of flow records - automatically adjusts its operation to the actual traffic load and to the monitoring requirements. Preliminary experiments in existing network topologies showed that adaptive aggregation efficiently reduces the number of flow records, while a significant proportion of traffic details is preserved

Securing Distributed Computer Systems Using an Advanced Sophisticated Hybrid Honeypot Technology

Computer system security is the fastest developing segment in information technology. The conventional approach to system security is mostly aimed at protecting the system, while current trends are focusing on more aggressive forms of protection against potential attackers and intruders. One of the forms of protection is also the application of advanced technology based on the principle of baits - honeypots. Honeypots are specialized devices aimed at slowing down or diverting the attention of attackers from the critical system resources to allow future examination of the methods and tools used by the attackers. Currently, most honeypots are being configured and managed statically. This paper deals with the design of a sophisticated hybrid honeypot and its properties having in mind enhancing computer system security. The architecture of a sophisticated hybrid honeypot is represented by a single device capable of adapting to a constantly changing environment by using active and passive scanning techniques, which mitigate the disadvantages of low-interaction and high-interaction honeypots. The low-interaction honeypot serves as a proxy for multiple IP addresses and filters out traffic beyond concern, while the high-interaction honeypot provides an optimum level of interaction. The proposed architecture employing the prototype of a hybrid honeypot featuring autonomous operation should represent a security mechanism minimizing the disadvantages of intrusion detection systems and can be used as a solution to increase the security of a distributed computer system rapidly, both autonomously and in real-time

Comparison of Filter Techniques for Two-Step Feature Selection

In the last decade, the processing of the high dimensional data became inevitable task in many areas of research and daily life. Feature selection (FS), as part of the data processing methodology, is an important step in knowledge discovery. This paper proposes nine variation of two-step feature selection approach with filter FS employed in the first step and exhaustive search in the second step. The performance of the proposed methods is comparatively analysed from the stability and predictive performance point of view. As the obtained results indicate the choice of the filter FS in the first stage has strong influence on the resulting stability. Here, the choice of univariate Pearson correlation coefficient based FS method appears to provide the most stable results

Agent-Based Model of the Spectrum Auctions with Sensing Imperfections in Dynamic Spectrum Access Networks

Cognitive radio (CR) is the underlying platform for the application of dynamic spectrum access (DSA) networks. Although the auction theory and spectrum trading mechanisms have been discussed in the CR related works, their joint techno-economic impact on the efficiency of distributed CR networks has not been researched yet. In this paper we assume heterogeneous primary channels with network availability statistics unknown to each secondary user (SU) terminal. In order to detect the idle primary user (PU) network channels, the SU terminals trigger regularly the spectrum sensing mechanism and make the cooperative decision regarding the channel status at the fusion center. The imperfections of the spectrum mechanism create the possibility of the channel collision, resulting in the existence of the risk (in terms of user collision) in the network. The spectrum trading within SU network is governed by the application of the sealed-bid first-price auction, which takes into account the channel valuation as well as the statistical probability of the risk existence. In order to maximize the long-term payoff, the SU terminals take an advantage of the reinforcement comparison strategy. The results demonstrate that in the investigated model, total revenue and total payoff of the SU operator (auctioneer) and SU terminals (bidders) are characterized by the existence of the global optimum, thus there exists the optimal sensing time guaranteeing the optimum economic factors for both SU operator and SU terminals

Economic Development, CO2 Emissions and Energy Use Nexus-Evidence from the Danube Region Countries

The aim of this study is to examine the empirical cointegration, long-run and short-run dynamics and causal relationships between carbon emissions, energy consumption and economic growth in 14 Danube region countries over the period of 1990–2019. The autoregressive distributed lag (ARDL) bounds testing methodology was applied for each of the examined variables as a dependent variable. Limited by the length of the time series, we excluded two countries from the analysis and obtained valid results for the others for 26 of 36 ARDL models. The ARDL bounds reliably confirmed long-run cointegration between carbon emissions, energy consumption and economic growth in Austria, Czechia, Slovakia, and Slovenia. Economic growth and energy consumption have a significant impact on carbon emissions in the long-run in all of these four countries; in the short-run, the impact of economic growth is significant in Austria. Likewise, when examining cointegration between energy consumption, carbon emissions, and economic growth in the short-run, a significant contribution of CO2 emissions on energy consumptions for seven countries was found as a result of nine valid models. The results contribute to the information base essential for making responsible and informed decisions by policymakers and other stakeholders in individual countries. Moreover, they can serve as a platform for mutual cooperation and cohesion among countries in this region

Impact of Renewable Energy Sources and Nuclear Energy on CO₂ Emissions Reductions—The Case of the EU Countries

The aim of this work is to analyse the dependence of carbon dioxide (CO2) emissions on total energy consumption, the energy produced from renewable sources, the energy produced in nuclear power plants and the gross domestic product (GDP) in 22 European countries, over the period 1992–2019. The fully modified ordinary least squares model (FMOLS) and dynamic OLS (DOLS) were used to estimate the long-term cointegration relationship between the variables. First differenced (FD) general moments methods (GMM) were used in the estimation of short-run relationship dynamics. The results suggest that energy produced from renewable sources causes a reduction in CO2 emissions per capita. On the other hand, total energy consumption increases CO2 emissions in the long run. Although the mitigation effect of nuclear power was not found to be significant across the entire block of countries studied, a closer look at countries utilising nuclear energy reveals that nuclear energy positively affects the reduction in CO2 emissions. Economic growth also has a positive effect on the reduction in CO2 emissions, which confirms the decoupling of economic development from environmental impacts. These findings are crucial for understanding the causality between these variables and the adoption of new or revision of existing policies and strategies promoting the carbon-neutral and green economy at the EU and national level

Impact of Renewable Energy Sources and Nuclear Energy on CO2 Emissions Reductions—The Case of the EU Countries

The aim of this work is to analyse the dependence of carbon dioxide (CO2) emissions on total energy consumption, the energy produced from renewable sources, the energy produced in nuclear power plants and the gross domestic product (GDP) in 22 European countries, over the period 1992–2019. The fully modified ordinary least squares model (FMOLS) and dynamic OLS (DOLS) were used to estimate the long-term cointegration relationship between the variables. First differenced (FD) general moments methods (GMM) were used in the estimation of short-run relationship dynamics. The results suggest that energy produced from renewable sources causes a reduction in CO2 emissions per capita. On the other hand, total energy consumption increases CO2 emissions in the long run. Although the mitigation effect of nuclear power was not found to be significant across the entire block of countries studied, a closer look at countries utilising nuclear energy reveals that nuclear energy positively affects the reduction in CO2 emissions. Economic growth also has a positive effect on the reduction in CO2 emissions, which confirms the decoupling of economic development from environmental impacts. These findings are crucial for understanding the causality between these variables and the adoption of new or revision of existing policies and strategies promoting the carbon-neutral and green economy at the EU and national level

Zależność emisji CO2 od zużycia energii i wzrostu gospodarczego w unii europejskiej: model progowy

This work aims to analyse the dependence of carbon dioxide (CO2) emissions on primary energy consumption at different Gross Domestic Product (GDP) levels in 28 European countries. Data for the years 1995-2019 were used to develop the models. Random Effects, Fixed Effects, a nonlinear panel threshold model and a continuous kink model were used in the panel data analysis. The work shows that the dependence of CO2 emissions on energy consumption varies at different levels of GDP. The model with two threshold values, which determine three modes of behaviour, proves to be the most suitable. As GDP levels increase, the regression coefficient of the dependence of CO2 emissions on energy consumption decreases. Understanding the relationship between these variables is essen-tial for informed and evidence-based decision-making and adopting new or revision of existing energy and climate policies and strategies at the EU and national levels.Niniejsza praca ma na celu analizę zależności emisji dwutlenku węgla (CO2) od zużycia energii pierwotnej przy różnych poziomach produktu krajowego brutto (PKB) w 28 krajach europejskich. Do opracowania modeli wykorzystano dane z lat 1995-2019. W analizie danych panelowych zastosowano efekty losowe, efekty stałe, nieliniowy model progu panelu i model ciągłego załamania. Praca pokazuje, że zależność emisji CO2 od zużycia energii jest różna na różnych poziomach PKB. Najbardziej odpowiedni okazuje się model z dwiema wartościami progowymi, które określają trzy sposoby zachowania. Wraz ze wzrostem poziomu PKB maleje współczynnik regresji zależności emisji CO2 od zużycia energii. Zrozumienie związku między tymi zmiennymi ma zasadnicze znaczenie dla świadomego i opartego na dowodach podejmowania decyzji oraz przyjmowania nowych lub rewizji istniejących polityk i strategii w zakresie energii i klimatu na szczeblu UE i krajowym

CSVO: Clustered Sparse Voxel Octrees—A Hierarchical Data Structure for Geometry Representation of Voxelized 3D Scenes

When representing the geometry of voxelized three-dimensional scenes (especially if they have been voxelized to high resolutions) in a naive—uncompressed—form, one may end up using vast amounts of data. These can easily attack the available memory capacity of the graphics card, the operating memory or even secondary storage of computer. A viable solution to this problem is to use domain-specific hierarchical data structures, based on octant trees or directed acyclic graphs, which, among other advantages, provide a compact binary representation that can thus be considered to be their compressed encoding. These data structures include—inter alia—sparse voxel octrees, sparse voxel directed acyclic graphs and symmetry-aware sparse voxel directed acyclic graphs. The paper deals with the proposal of a new domain-specific hierarchical data structure: the clustered sparse voxel octrees. It is designed to represent the geometry of voxelized three-dimensional scenes and can be constructed using the out-of-core algorithm proposed in the paper. The advantage of the presented data structure is in its compact binary representation, achieved by omitting a significant number of pointers to child nodes (82.55% in case of Angel Lucy model in 1283 voxels resolution) and by using a wider range of child node pointer lengths, including 8b, 16b and 32b. We achieved from 6.57 to 6.82 times more compact encoding, compared to sparse voxel octrees, whose all node components were 32b aligned, and from 4.11 to 4.27 times more compact encoding, when not all node components were 32b aligned