• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1670-1675
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• 2020

In this study, to provide the basis for establishing effective network based countermeasures against DRDoS(Distributed Reflection Denial of Service) attacks, we propose a new 'DRDoS attack multi-level detection method' that identifies the network based characteristics of DRDoS and applies probability and statistical techniques. The proposed method removes the limit to which normal traffic can be indiscriminately blocked by unlimited competition in network bandwidth by amplification of reflectors, which is characteristic of DRDoS. This means that by comparing 'Server to Server' and 'Outbound Session Incremental' for it, accurate DRDoS identification and detection is possible and only statistical and probabilistic thresholds are applied to traffic. Thus, network-based information security systems can take advantage of this to completely eliminate DRDoS attack frames. Therefore, it is expected that this study will contribute greatly to identifying and responding to DRDoS attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1046-1052
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• 2021

Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.527-545
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• 2022

In order to effectively detect APT attacks performed by well-organized adversaries, we implemented a system to detect attacks by reconstructing attack chains of APT attacks. Our attack chain-based APT attack detection system consists of 'events collection and indexing' part which collects various events generated from hosts and network monitoring tools, 'unit attack detection' part which detects unit-level attacks defined in MITRE ATT&CK^® techniques, and 'attack chain reconstruction' part which reconstructs attack chains by performing causality analysis based on provenance graphs. To evaluate our system, we implemented a test-bed and conducted several simulated attack scenarios provided by MITRE ATT&CK Evaluation program. As a result of the experiment, we were able to confirm that our system effectively reconstructed the attack chains for the simulated attack scenarios. Using the system implemented in this study, rather than to understand attacks as fragmentary parts, it will be possible to understand and respond to attacks from the perspective of progress of attacks.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.59-66
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• 2022

Students can successfully connect with one another thanks to the introduction of Web 2.0 and the tools and technology linked with it. The fact that rising digital tools are systematically influencing the education system is not a secret. The purpose of the research article efficiently evaluates the influence of incorporation of media in the activities of the scientific library of the higher education institution. The research Methodology is the Concepts, techniques, and procedures to effectively inculcate primary and secondary data to conduct the research effortlessly. It's worth noting that in this case, quantitative primary research was provided in the form of a survey. The researchers have proposed a survey in order to successfully instil a comprehensive view on the "incorporation of media in the operations of the scientific library of higher education institutions." As a result, fifty-one higher education institution principals were asked to attend this session. This is necessary to understand that they are both well-educated and cognizant of the impact of technology innovation on schooling. As a result, the researchers were able to gain a comprehensive view of this situation thanks to this survey. The results effectively showed that most of the participants believe that social media plays a vital role in shaping up higher education and at the same time they believe that the libraries of famous educational institutions must adapt as per the new educational trend so that teachers and students both can tap into its benefit.The practical significance of the result is manoeuvred by the efficient survey analysis and at the same time, peer-reviewed journals have been employed to put forward authentic information. Therefore, efficient insight regarding this topic has been gathered by the researchers.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.111-118
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• 2022

This research article presents the work that is related to the application of different machine learning based similarity techniques on religious text for identifying similarities and differences among its various translations. The dataset includes 10 different English translations of verses (Arabic: Ayah) of two Surahs (chapters) namely, Al-Humazah and An-Nasr. The quantitative similarity values for different translations for the same verse were calculated by using the cosine similarity and semantic similarity. The corpus went through two series of experiments: before pre-processing and after pre-processing. In order to determine the performance of machine learning based similarities, human annotated similarities between translations of two Surahs (chapters) namely Al-Humazah and An-Nasr were recorded to construct the ground truth. The average difference between the human annotated similarity and the cosine similarity for Surah (chapter) Al-Humazah was found to be 1.38 per verse (ayah) per pair of translation. After pre-processing, the average difference increased to 2.24. Moreover, the average difference between human annotated similarity and semantic similarity for Surah (chapter) Al-Humazah was found to be 0.09 per verse (Ayah) per pair of translation. After pre-processing, it increased to 0.78. For the Surah (chapter) An-Nasr, before preprocessing, the average difference between human annotated similarity and cosine similarity was found to be 1.93 per verse (Ayah), per pair of translation. And. After pre-processing, the average difference further increased to 2.47. The average difference between the human annotated similarity and the semantic similarity for Surah An-Nasr before preprocessing was found to be 0.93 and after pre-processing, it was reduced to 0.87 per verse (ayah) per pair of translation. The results showed that as expected, the semantic similarity was proven to be better measurement indicator for calculation of the word meaning.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.105-112
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• 2022

The development and spread of IT-technologies has raised interest in teaching programming pupils. The article deals with problems related to programming and ways to overcome them. The importance of programming skills is emphasized, as this process promotes the formation of algorithmic thinking of pupils. The authors determined the level of pupils' interest to programing learning depending on the age. The analysis has showed that the natural interest of younger pupils in programming is decreasing over the years and in the most productive period of its study is minimized. It is revealed that senior school pupils are characterized by low level of interest in the study of programming; lack of motivation; the presence of psychological blocks on their own abilities in the context of programming; law level of computer science understanding. To overcome these problems, we conducted the second stage of the experiment, which was based on a change in the approach to programing learning, which involved pupils of non-specialized classes of senior school (experimental group). During the study of programming, special attention was paid to the motivational and psychological component, as well as the use of game technologies and teamwork of pupils. The results of the pedagogical experiment on studying the effectiveness of teaching programming for pupils of nonspecialized classes are presented. Improvement of the results provided the use of social and cognitive motives; application of verbal and non-verbal, external and internal means; communicative attacks; stimulation and psychological setting; game techniques, independent work and reflection, teamwork. The positive effect of the implemented methods is shown by the results verified by the methods of mathematical statistics in the experimental and control groups of pupils.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.461-467
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• 2022

Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobouy signal transmission and reception system, collected signals are modulated and transmitted using techniques such as frequency division modulation or Gaussian frequency shift keying, and received and demodulated by an aircraft or a ship. However, this method has the disadvantage of the large amount of information to be transmitted and low security due to relatively simple modulation and demodulation methods. Therefore, in this paper, we propose a method that uses an autoencoder to encode a transmission signal into a low-dimensional latent vector to transmit the latent vector to an aircraft or ship and decode the received latent vector to improve signal security and to reduce the amount of transmission information by approximately a factor of a hundred compared to the conventional method. As a result of confirming the sample spectrogram reconstructed by the proposed method through simulation, it was confirmed that the original signal could be restored from a low-dimensional latent vector.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.