February 2023
Algorithmization of the Software Testing System based on Finite Automata
2022, vol.14 , no.1, pp. 77-86
Article [2022-01-07]
Software testing is one of the key stages in the development of a digital device based on a microcontroller. The article discusses the features of the implementation of algorithms for the functioning of the microcontroller software testing system based on finite automata. An enlarged block diagram of a testing system at the software and hardware levels is presented, a model of such a system using the theory of finite automata is presented. A detailed finite automaton for the testing process model has been formed, its states have been determined and algorithms for operation are given.
M. M. Zozulya, O. Ja. Kravets, I. V. Atlasov, I. A. Aksenov, L. M. Bozhko, P. A. Rahman. Algorithmization of the Software Testing System based on Finite Automata. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 77-86.
Convolutional and Long Short-Term Memory Neural Networks Based Models for Remaining Useful Life Prediction
2022, vol.14 , no.1, pp. 61-76
Article [2022-01-06]
The development of industry leads to the growth of the complexity of equipment used in enterprises. Corrective and preventive maintenance are replaced by predictive maintenance. Predicting the remaining useful life of equipment with high accuracy allows carry out repairs or replacement of equipment in terms maximally near to its
failures. It will allow increase the reliability and safety of systems, and reduce maintenance costs. In Industry 4.0 conception the most preferable are approaches based on processing of large amounts of data using machine learning methods. In this study it is proposed deep learning models based on convolutional and long short-term memory neural network which allow to improve prediction accuracy. The high efficiency of proposed models is demonstrated by comparison with other models used in predicting of remaining useful life of aero engines.
Katerina M. Gritsyuk, Vera I. Gritsyuk. Convolutional and Long Short-Term Memory Neural Networks Based Models for Remaining Useful Life Prediction. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 61-76.
Predictive Analytics for Energy Consumption in Smart Homes with Fog and Cloud Computing Using Support Vector Regression
2022, vol.14 , no.1, pp. 49-60
Article [2022-01-05]
Predict energy consumption in smart homes is our objective in this work. We defined the two predictive analytics techniques, and we will be tested in this study. We presented the Linear Regression and the Support Vector Regression data mining techniques. Implemented those two machine learning models with the appropriate techniques, starting by cleaning and preparing the data then we visualize it so that we can uncover hidden information about the behavior of the smart home appliances using the energy consumption feature. Afterwards, implementing the two regression models to predict the whole house energy consumption. Compared the performances of the two techniques. The results achieved are promising and proves the reliability of the IoT smart home platform.
Sofiene Haboubi, Oussama Bben Salem. Predictive Analytics for Energy Consumption in Smart Homes with Fog and Cloud Computing Using Support Vector Regression. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 49-60.
An Empirical Study of User Interface Testing Tools
2022, vol.14 , no.1, pp. 37-48
Article [2022-01-04]
Software testing is gaining much more importance in the software development life cycle. Many companies are investing in several testing methods and especially in test automation. Nevertheless, it is quite difficult to choose the best tool or framework to use. In this study, we will do an evaluation and comparison of Katalon Studio, Selenium and UFT in a web application. This is the preliminary work to achieve some solid outcomes in order to propose and create a new general framework for test and code optimisation.
Elis Pelivani, Adrian Besimi, Betim Cico. An Empirical Study of User Interface Testing Tools. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 37-48.
The Algorithms of Strategic Financial Management
2022, vol.14 , no.1, pp. 29-36
Article [2022-01-03]
This article discusses the task of strategic financial management of investments portfolio. The information model of investment contributions in tree-diagrams is constrected. Efficient algorithms for managing investment financial transactions are built. the tools aplyd in this development based on the generalizing breadth-first search algorithms and forward approch of dynamical programing method. Relevant pseudocodes have been developed. For software implementation priority queuing aproches with a stacks owerflows are duscessed.
Manana Chumburidze, Nana Shonia. The Algorithms of Strategic Financial Management. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 29-36.
Service Level Agreement Aware Energy Optimized Scheduling Algorithm for Cloud Computing Environment
2022, vol.14 , no.1, pp. 17-28
Article [2022-01-02]
This paper presents a heterogenous cloud computing environment for provisioning real-time (dynamic workload) services in a cloud computing environment. Moreover, this work also presents SLA Aware Energy Optimized (SAEO) Scheduling Algorithm to execute dynamic workload applications like the data intensive and scientific applications. The main aim of the SAEO is to bring good tradeoffs in minimizing computation time and energy consumption by employing Dynamic Voltage Frequency Scaling (DVFS) effectively utilizing system resource of cloud. SAEO achieves much better performance than existing DVFS-based scheduling in terms of computation time and energy efficiency.
Murgesh V. Jambigi, M. V. Vijay Kumar, D. V. Ashoka, Prabha R. Service Level Agreement Aware Energy Optimized Scheduling Algorithm for Cloud Computing Environment. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 17-28.
Estimating End-to-End Delay on a Networking Environment Using Developed Framework
2022, vol.14 , no.1, pp. 3-16
Article [2022-01-01]
Any designer wants to perform performance analysis to calculate and exploit performance, in this paper, the main aim is to compute end-to-end delay which also known as “latency” at beginning phase before start working
on the implementation to ensure that the proposed design is efficient. This analysis is especially needed in real-time embedded systems and considered to be crucial. Herein, an implemented model, framework, is used to link between a Finite State Machine and a Mathematical Analytical method to compute the expected average end-to-end delay for different layers of abstractions on any network topology using a network simulator. A Hierarchical Generic FSM “HGFSM” was developed earlier to estimate the desired performance metric which is represented by the end-to-end latency in this paper. Several modifications and adjustments were made to the HGFSM and the mathematical model to be suitable for this research. Cisco Packet Tracer ver. 7.3 was used in this paper to show how that performance metric, which is latency, was computed using the implemented framework. In addition, MATLAB was used as a simulator tool along with Cisco Packet Tracer to show the effectiveness of using that framework in finding the desired metric
Ahmed Alsheikhy. Estimating End-to-End Delay on a Networking Environment Using Developed Framework. International Journal on Information Technologies and Security, vol.14 , no.1, 2022, pp. 3-16.
Issue No. 1 (Vol. 14), 2022
The use of fractal dimension (FD) analysis in detection of anomalies, sabotages, and malicious acts in a cyber-physical system using Higuchi’s algorithm
2022, vol.14 , no.2, pp. 111-121
Article [2022-02-10]
In recent years, cyber-physical systems (CPSs) have received a lot of attention because of their wide range of applications. However, because CPSs rely heavily on communication networks, they are vulnerable to deliberate cyberattacks. As a result, there are a wide variety of attack detection methods that have been proposed to protect CPSs. This research examines the behavior of malicious acts and anomalies within the cyber physical system using Higuchi Fractal Dimension (HFD). The model was constructed and evaluated using the HFD algorithm. The model categorizes various types of anomalous events, such as sabotage, cyber-attacks, and hardware failures. Furthermore, our proposed model notifies the operator by calculating the probability of an anomaly occurring, which aids in the mitigation process. A real-world dataset is used to train and test the model.
Marwan Albahar, Mohammed Thanoon, Abdulaziz Albahr. The use of fractal dimension (FD) analysis in detection of anomalies, sabotages, and malicious acts in a cyber-physical system using Higuchi’s algorithm. International Journal on Information Technologies and Security, vol.14 , no.2, 2022, pp. 111-121.
Simulation modelling of artificial neural networks for the purpose of steganalysis
2022, vol.14 , no.2, pp. 99-110
Article [2022-02-09]
This paper is considered to be a continuation of previous publications devoted to applications of simulation modelling in cybersecurity and in particular the use of Artificial Neural Networks (ANNs) with Backpropagation of Error for digital recognition (IJITS, No4,2021). It aims to present advanced steganographic methods that are applicable for the purposes of steganalysis to ensure the information protection. The empirical study and the experimental analysis made contribute to explaining various steganographic approaches that are applicable in security.
Yoana Ivanova. Simulation modelling of artificial neural networks for the purpose of steganalysis. International Journal on Information Technologies and Security, vol.14 , no.2, 2022, pp. 99-110.