• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.151-160
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• 2023

The Hadith is the second source of Islamic jurisprudence after Qur'an. Both sources are indispensable for muslims to practice Islam. All Ahadith are collected and are written. But most books of Hadith contain Ahadith that can be weak or rejected. So, quite a long time, scholars of Hadith have defined laws, rules and principles of Hadith to know the correct Hadith (Sahih) from the fair (Hassen) and weak (Dhaif). Unfortunately, the application of these rules, laws and principles is done manually by the specialists or students until now. The work presented in this paper is part of the automatic treatment of Hadith, and more specifically, it aims to automatically process the chain of narrators (Hadith Isnad) to find its different components and affect for each component its own tag using a statistical method: the Hidden Markov Models (HMM). This method is a power abstraction for times series data and a robust tool for representing probability distributions over sequences of observations. In this paper, we describe an important tool in the Hadith isnad processing: A chunker with HMM. The role of this tool is to decompose the chain of narrators (Isnad) and determine the tag of each part of Isnad (POI). First, we have compiled a tagset containing 13 tags. Then, we have used these tags to manually conceive a corpus of 100 chains of narrators from "Sahih Alboukhari" and we have extracted a lexicon from this corpus. This lexicon is a set of XML documents based on HPSG features and it contains the information of 134 narrators. After that, we have designed and implemented an analyzer based on HMM that permit to assign for each part of Isnad its proper tag and for each narrator its features. The system was tested on 2661 not duplicated Isnad from "Sahih Alboukhari". The obtained result achieved F-scores of 93%.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.1-8
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• 2023

An Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication system has drawn wide attention for its high transmission rate and high spectrum efficiency in not only radio but also Underwater Acoustic (UWA) applications. Because of the narrow sub-carrier spacing of OFDM, orthogonality between sub-carriers is easily affected by Doppler effect caused by the movement of transmitter or receiver. Previously, Doppler compensation signal processing algorithm for Desired propagation path was proposed. However, other Doppler shifts caused by delayed Undesired signal arriving from different directions cannot be perfectly compensated. Then Receiver Bit Error Rate (BER) is degraded by Inter-Carrier-Interference (ICI) caused in the case of Multi-path Doppler channel. To mitigate the ICI effect, a modified Delay and Doppler Profiler (mDDP), which estimates not only attenuation, relative delay and Doppler shift but also sampling clock shift of each multi-path component, is proposed. Based on the outputs of mDDP, an ICI canceling multi-tap equalizer is also proposed. Computer simulated performances of one-tap equalizer with the conventional Time domain linear interpolated Channel Transfer Function (CTF) estimator, multi-tap equalizer based on mDDP are compared. According to the simulation results, BER improvement has been observed. Especially, in the condition of 16QAM modulation, transmitting vessel speed of 6m/s, two-path multipath channel with direct path and ocean surface reflection path; more than one order of magnitude BER reduction has been observed at CNR=30dB.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.207-215
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• 2024

Cloud computing is now used by most companies, business centres and academic institutions to embrace new computer technology. Cloud Service Providers (CSPs) are limited to certain services, missing some of the assets requested by their customers, it means that different clouds need to interconnect to share resources and interoperate between them. The clouds may be interconnected in different characteristics and systems, and the network may be vulnerable to volatility or interference. While information technology and cloud computing are also advancing to accommodate the growing worldwide application, criminals use cyberspace to perform cybercrimes. Cloud services deployment is becoming highly prone to threats and intrusions. The unauthorised access or destruction of records yields significant catastrophic losses to organisations or agencies. Human intervention and Physical devices are not enough for protection and monitoring of cloud services; therefore, there is a need for more efficient design for cyber defence that is adaptable, flexible, robust and able to detect dangerous cybercrime such as a Denial of Service (DOS) and Distributed Denial of Service (DDOS) in heterogeneous cloud computing platforms and make essential real-time decisions for forensic investigation. This paper aims to develop a framework for digital forensic for the detection of cybercrime in a joined heterogeneous cloud setup. We developed a Digital Forensics model in this paper that can function in heterogeneous joint clouds. We used Unified Modeling Language (UML) specifically activity diagram in designing the proposed framework, then for deployment, we used an architectural modelling system in developing a framework. We developed an activity diagram that can accommodate the variability and complexities of the clouds when handling inter-cloud resources.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.158-168
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• 2024

There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.133-139
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• 2024

The educational situation, which resulted from the announced self-isolation regime, intensified the forced decisions on the organization of the distance educational process. The study is topical because of the provision of distance learning based on the experience of Kyiv National University of Culture and Arts. The study was conducted in three stages. Systemic, socio-communicative, competence approaches, sociological methods (questionnaires and interviews) were chosen as methodological tools of the research. The results of a survey of teachers and entrants to higher education institutions on the topic "Using social networks and digital platforms for online classes under the conditions of quarantine restrictions" allowed to scientifically substantiate the need for deeper knowledge of such tools as Google Meet (79%), Zoom (13.78%) and Google Classroom (11.62%), which are preferred by entrants. Almost a third of entrants (34.26%) noted the lack of scientific and methodological support for learning the subjects. The study showed high efficiency of messengers in distance education. The study found that in the process of organizing communication in the student-teacher system, it is necessary to take into account the priority of Telegram on the basis of which it is necessary to implement a chatbot for convenient and effective exchange of information about the educational process. Further research should focus on the effectiveness of the use of Telegram. The effectiveness of using chatbots should also be considered. Chatbots can be used to automate routine components of the learning process.

• Convergence Security Journal
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• v.23 no.4
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• pp.122-129
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• 2023

The paradigm of warfare is undergoing a revolutionary transformation due to the advancements in technology brought forth by the Fourth Industrial Revolution. Specifically, the U.S. military has introduced the concept of mosaic warfare as a means of military innovation, aiming to integrate diverse resources and capabilities, including various weapons, platforms, information systems, and artificial intelligence. This integration enhances the ability to conduct agile operations and respond effectively to dynamic situations. The incorporation of mosaic warfare could facilitate efficient and rapid command and control by integrating AI staff with human commanders. Ukrainian military operations have already employed mosaic warfare in response to Russian aggression. This paper focuses on the mosaic war fare concept, which is being proposed as a model for future warfare, and suggests the strategy for introducing the Korean mosaic warfare concept in light of the changing battlefield paradigm.

• Journal of Platform Technology
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• v.11 no.5
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• pp.37-48
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• 2023

Since the announcement of the Financial Services Commission's 'Token Securities Issuance and Distribution System Improvement Plan' in February 2023, financial institutions, securities firms, and blockchain companies have been actively considering implementing IT platforms, but they are facing difficulties in selecting IT platforms for token securities because related legal regulations have not yet been clearly established. As a result, the need for rational and systematic criteria for the selection of blockchain-based token securities IT platforms has emerged, and this study explores and evaluates the key factors of token securities IT platform selection. Four factors were identified as the top-level factors, including 'maturity of the platform', 'operation and management of the platform', 'cost of introducing and maintaining the platform', and 'regulatory compliance for token securities', and 17 factors were identified as sub-level factors, including 'diversity', 'user authentication management', 'Adoption Costs', and 'financial regulations'. Among the 17 sub-factors, 'government financial regulation' and 'personal information protection' are selected as important factors, and the results of this study can help related organizations and financial companies make strategic decisions by providing systematic decision-making criteria for selecting token securities IT platforms.

• Convergence Security Journal
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• v.21 no.4
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• pp.77-85
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• 2021

Cyber protection is an activity that protects the information systems we operate from cyber attacks and threats. To know the level of protection of the currently operating cyber protection system, it is necessary to update the current state of attack technology by reflecting the constantly evolving cyber threats and to analyze whether it is possible to respond with the protection function. Therefore, in this paper, we analyze the relationship between the attack procedures and defense types of the cyber kill chain with the defense technology(Mitigation ID) of MITRE and present the cyber protection level for each military unit type with a focus on defensive cyber activities. In the future, it is expected that the level of cyber protection will be improved through real-time analysis of the response capabilities of cyber protection systems operating in the defense sector to visualize the level of protection for each unit, investigate unknown cyber threats, and actively complement vulnerabilities.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.1-16
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• 2024

Due to its complexity and high diagnosis and treatment costs, heart attack (HA) is the top cause of death globally. Heart failure's widespread effect and high morbidity and death rates make accurate and fast prognosis and diagnosis crucial. Due to the complexity of medical data, early and accurate prediction of HA is difficult. Healthcare providers must evaluate data quickly and accurately to intervene. This novel hybrid approach predicts HA using Long Short-Term Memory (LSTM) networks, Deep belief networks (DBNs) with attention mechanism, and robust data mining to fill this essential gap. HA is predicted using Kaggle, PhysioNet, and UCI datasets. Wearable sensor data, ECG signals, and demographic and clinical data provide a solid analytical base. To maintain consistency, ECG signals are normalized and segmented after thorough cleaning to remove missing values and noise. Feature extraction employs complex approaches like Principal Component Analysis (PCA) and Autoencoders to pick time-domain (MNN, SDNN, RMSSD, PNN50) and frequency-domain (PSD at VLF, LF, HF bands) characteristics. The hybrid model architecture uses LSTM networks for sequence learning and DBNs for feature representation and selection to create a robust and comprehensive prediction model. Accuracy, precision, recall, F1-score, and ROC-AUC are measured after cross-entropy loss and SGD optimization. The LSTM-DBN model outperforms predictive methods in accuracy, sensitivity, and specificity. The findings show that several data sources and powerful algorithms can improve heart attack predictions. The proposed architecture performed well on many datasets, with an accuracy rate of 96.00%, sensitivity of 98%, AUC of 0.98, and F1-score of 0.97. High performance proves this system's dependability. Moreover, the proposed approach is outperformed compared to state-of-the-art systems.