作者：
Mucahit SoyluResul DasInonu University
Department of Organized Industrial Zone Vocational School Computer Programming Malatya Turkiye Firat University
Faculty of Technology Department of Software Engineering 23119 Elazig Turkiye
This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the...
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This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed into a format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes in the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped to reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.
This study investigated the effects of maltodextrin-based nanoemulsions as fat substitutes in cookies, focusing on the oxidative stability and physical properties. Full-fat cookies (control, C) and 50% fat-reduced coo...
This study investigated the effects of maltodextrin-based nanoemulsions as fat substitutes in cookies, focusing on the oxidative stability and physical properties. Full-fat cookies (control, C) and 50% fat-reduced cookies with nanoemulsions (FC) were produced. The addition of nanoemulsions increased the cookie diameter from 46.3 mm (control) to 56.1 mm and reduced the thickness, resulting in a desirable texture. Initial hardness values (30.3 and 45.8 N) were lower in nanoemulsion samples and remained reduced over a 90 day storage period. Black cumin oil-loaded nanoemulsions provided the lowest peroxide values (1.7, 2.7, and 2.4 mequiv O2/kg), maintaining oxidative stability during storage. Final free fatty acid (FFA) values ranged from 0.23% to 0.44% after storage. Thiobarbituric acid (TBA) values indicated slower lipid oxidation, with values ranging from 1.47 to 2.51 mg MDA/kg on day 0 and increasing to a maximum of 4.13 mg MDA/kg by day 90 in fat-reduced cookies. Among the tested formulations, nanoemulsions enriched with black cumin oil demonstrated the highest effectiveness, yielding enhanced oxidative stability and improved quality characteristics. This study presents an innovative strategy by utilizing maltodextrin-based nanoemulsions containing naturally antioxidant-rich oils as fat replacers, offering a clean-label alternative to improve the oxidative resilience and physical quality of cookies.
This research focuses on hyperparameter optimization for LSTM to forecast SARS-CoV-2 infection cases in the Russian Federation, aiming to determine the best combination of parameters for a well-fitting model. Using L...
This research focuses on hyperparameter optimization for LSTM to forecast SARS-CoV-2 infection cases in the Russian Federation, aiming to determine the best combination of parameters for a well-fitting model. Using LSTM’s capability to analyze relationships within time series data, a bidirectional LSTM-based method is introduced for predicting daily infection cases. The study evaluates nearly 10 unique forecasting models and conducts a comprehensive analysis and comparison of their results. The Bidirectional LSTM model proves to be a reliable approach for forecasting daily SARS-CoV-2 infection cases in Russia, displaying the highest prediction accuracy among the tested models.
In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study prop...
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In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for com...
In this paper, we introduce an approach via regularization and Homotopy way for resolving the inverse Cauchy problem of the Laplace of system partial differential equation which appears in the wave propagation for communication networks. We considered the method of Homotopy Perturbation Metheod (HPM) for solving the integral equations of the first kind named Fredholm. In order to formulate the Laplace equation into the first-kind integral equation (Fredholm) the Fourier series used. Then the discretization method used to reduce the integral equation into a linear operator equation for the first kind. It is clear that this kind of problem is callsified as an ill-posed and the direct way to solve it unacceptably. Tikhonov’s regularization method with Homotopy Perturbation algorithm used for obtaing the approximation solution for the Laplace differential equation. Finally, the numerical example is proposed.
time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time s...
time series Due to better algorithms, more accessible data, and higher computing power over the past ten years, forecasting has become more popular. It is used in a variety of industries, including as financial time series, weather forecasting, and medical diagnostics. In this study, we provide a model of the mechanism governing attention, which enables attended input to be provided to the model in place of actual input. In order for the model to produce more precise predictions, it seeks to demonstrate a fresh perspective on the data. The experiments were conducted with the (encoder-decoder) LSTM model as well to demonstrate the usefulness and superiority of the suggested strategy. The obtained results demonstrate that, when compared to the (encoder-decoder) LSTM base model, the proposed approach could reduce the mean square error (RMSE=9819.05), relative root mean square error (RRMSE=99.09), and coefficient of determination (R Square=0.96). The obtained results support the suggested approach’s efficacy, superiority, and importance in predicting SARS-CoV-2 infection cases.
the application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substa...
the application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substantiated on the example of servicing several unequally important targets in an autonomous mode.
The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets...
The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.
An article presents an approach for cyberattack detection based on genetic algorithms is presented. The method allows detecting both known and unknown cyberattacks. The method has the heuristic nature and is based on ...
ISBN:
(数字)9781728199573
ISBN:
(纸本)9781728199580
An article presents an approach for cyberattack detection based on genetic algorithms is presented. The method allows detecting both known and unknown cyberattacks. The method has the heuristic nature and is based on the collected data about the cyberattacks. It makes it possible to give an answer about the cyberattacks' existence in the computer networks and its hosts. Developed attack detection approach consists of training and detection stages. The mechanism of attack detection system is based on the cyberattacks' features gathering from network or hosts, extracting the subset of acquired set and generation the attacks' detection rules. Genetic algorithms are used for the minimization of the feature set, which allows effective using of the system resources for attacks detection. In order to detect the attacks, the proposed technique involves the rule generation. The attacks' features are described by the set of sub-rules. It is suggested to use the feature with the smallest domain for generating the minimal set for rules. It is possible to select the optimal feature after all selected features which were discovered while applying the genetic algorithm. The sub-rule set is used with the aim to reduce false positive rate.
The Hamming neural network is an effective tool for solving the problems of recognition and classification of discrete objects whose components are encoded with the binary bipolar alphabet, and the difference between ...
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ISBN:
(数字)9781728193526
ISBN:
(纸本)9781728193533
The Hamming neural network is an effective tool for solving the problems of recognition and classification of discrete objects whose components are encoded with the binary bipolar alphabet, and the difference between the number of identical bipolar components of the compared objects (vectors images) and the Hamming distance between them (Hamming distance is the number of mismatched bits in the binary vectors being compared) is used as the objects proximity measures. However, the Hamming neural network cannot be used to solve these problems in case the components of the compared objects (vectors) are encoded with the binary alphabet. It also cannot be used to evaluate the affinity (proximity) of objects (binary vectors) with Jaccard, Sokal and Michener, Kulzinsky functions, etc. In this regard, a generalized Hamming neural network architecture has been developed. It consists of two main blocks, which can vary being relatively independent on each other. The first block, consisting of one layer of neurons, calculates the proximity measures of the input image and the reference ones stored in the neuron relations weights of this block. Unlike the Hamming neural network, this block can calculate various proximity measures and signals about the magnitude of these proximity measures from the output of the first block neurons which are followed to the inputs of the second block elements. In the Hamming neural network, the Maxnet neural network is used as the second block, which gives out one maximum signal from the outputs of the first block neurons. If the inputs of the Maxnet network receive not only one but several identical maximum signals, then the second block, and, consequently, the Hamming network, cannot recognize the input vector, which is at the same minimum Hamming distance from two or more reference images stored in the first block. The proposed generalized architecture of the Hamming neural network allows using neural networks instead of the Maxnet network, whi
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