• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.89-96
• /
• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• Coupled systems mechanics
• /
• v.12 no.5
• /
• pp.429-444
• /
• 2023

The comprehension and structural modeling of masonry constructions is fundamental to safeguard the integrity of built cultural assets and intervene through adequate actions, especially in earthquake-prone regions. Despite the availability of several modeling strategies and modern computing power, modeling masonry remains a great challenge because of still demanding computational efforts, constraints in performing destructive or semi-destructive in-situ tests, and material uncertainties. This paper investigates the shear behavior of masonry walls by applying a plane-stress FE continuum model with the Modified Masonry-like Material (MMLM). Epistemic uncertainty affecting input parameters of the MMLM is considered in a probabilistic framework. After appointing a suitable probability density function to input quantities according to prior engineering knowledge, uncertainties are propagated to outputs relying on gPCE-based surrogate models to considerably speed up the forward problem-solving. The sensitivity of the response to input parameters is evaluated through the computation of Sobol' indices pointing out the parameters more worthy to be further investigated, when dealing with the seismic assessment of masonry buildings. Finally, masonry mechanical properties are calibrated in a probabilistic setting with the Bayesian approach to the inverse problem based on the available measurements obtained from the experimental load-displacement curves provided by shear compression in-situ tests.

• Journal of Korean Association for Spatial Structures
• /
• v.23 no.4
• /
• pp.81-88
• /
• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Asian Journal for Public Opinion Research
• /
• v.12 no.1
• /
• pp.1-27
• /
• 2024

This study investigates the interplay between transformational leadership, personality, and work stress in public organizations, focusing on public servants in Putrajaya, Kuala Lumpur. Data from 702 public servants in Putrajaya, Kuala Lumpur, out of 800 distributed questionnaires, were analyzed. Rigorous analysis employed a structural equation model (SEM) with partial least squares (PLS-SEM) methodology. Our empirical analysis challenges the conventional belief of a positive correlation between transformational leadership and work stress in public organizations, revealing no direct link between transformational leadership and increased work stress in this context. Our study highlights a significant positive correlation between personality and work stress among public servants, emphasizing the role of individual personality characteristics in shaping work-related stress levels. Finally, personality emerged as a crucial mediator in the transformational leadership-work stress relationship, indicating that transformational leadership indirectly influences work stress through its impact on personality. This underscores the importance of considering personality as a mediating factor in understanding the transformational leadership-work stress dynamic in public organizations. In summary, our research provides insights into the relationship between transformational leadership, personality, and work stress in public organizations. These findings challenge conventional assumptions, emphasize individual differences in stress levels, and underscore the mediating role of personality in addressing work stress among public servants.

• Asia pacific journal of information systems
• /
• v.32 no.1
• /
• pp.125-150
• /
• 2022

Technology companies launched their intelligent personal assistants (IPAs). IPAs not only provide individuals with a convenient way to interact with technology but also offer them the opportunity to interact with AI in a useful and meaningful form. Therefore, the global IPAs have experienced tremendous growth over the past decade. But maintaining continuous usage intention is still a massive challenge for developers and marketers and previous technology adoption models are not enough to explain continuous usage intention of IPAs. Thus, we adopted the bi-dimensional perspectives of utilitarian and hedonic value in this research model, and investigated how three characteristics of IPAs - anthropomorphism, autonomy, and intelligence - affect utilitarian value and hedonic value, which in turn continuous usage intentions. 227 data were collected from IPA users. The results showed that IPAs' continuous usage intention is significantly determined by both utilitarian and hedonic value, with the hedonic value being more prominent. In addition, the results showed that anthropomorphism and intelligence are the most important antecedents of utilitarian and hedonistic value. The results also illustrated that autonomy is a crucial predictor of utilitarian value rather than hedonistic value. Our work contributes to current research by widening the theoretical understanding of the effect of IPA characteristics on continuous usage intention through bi-dimensional values. Our paper also provides IPAs' developer and marketer guidelines for enhancing continuous usage intention.

• Journal of the Korean Society of Systems Engineering
• /
• v.20 no.spc1
• /
• pp.30-44
• /
• 2024

The DMAIC methodology, which is most familiar to battery production system developers, is partially inadequate in its conformity to utilize battery production system tasks, so it is necessary to improve the function and structure of the methodology, but many battery production system developers use the DMAIC method based on experience, causing side effects such as confusion, delay in tasks, and insufficient performance during tasks. Accordingly, we intend to conduct an empirical study to improve the "efficiency improvement and conformity evaluation method" so that the DMAIC methodology can be used more reasonably and easily. Using the three-stage research model, we derive components that affect conformity through literature and questionnaire surveys in the first stage, use relational characteristics between components in the second stage to confirm the effect on conformity, and use the relational characteristics in the third stage to confirm the possibility of improving efficiency by applying them to the DMAIC methodology in actual cases. Finally, the "Conformity Assessment Index (CAI) equation" based on relational characteristics is established to enable effective conformity evaluation and continuous improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.6
• /
• pp.1462-1477
• /
• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.4
• /
• pp.437-445
• /
• 2024

In future complex and diverse battlefield situations, the existing command system faces the challenge of delayed human judgement of strategy and low objectivity. This paper proposes an artificial intelligence model that takes situation information and course of action simulation results as input and automatically assigns scores to various evaluation elements and a comprehensive score. This tool is expected to assist the commander in making decisions, reduce the time required for making judgments, and promote impartial decision-making.

• Smart Media Journal
• /
• v.12 no.2
• /
• pp.56-65
• /
• 2023

In broadcasts such as news and coverage programs, voice is modulated to protect the identity of the informant. Adjusting the pitch is commonly used voice modulation method, which allows easy voice restoration to the original voice by adjusting the pitch. Therefore, since broadcast voice modulation methods cannot properly protect the identity of the speaker and are vulnerable to security, a new voice modulation method is needed to replace them. In this paper, using the Lightweight speech de-identification model as the evaluation target model, we compare speech de-identification performance with broadcast voice modulation method using pitch modulation. Among the six modulation methods in the Lightweight speech de-identification model, we experimented on the de-identification performance of Korean speech as a human test and EER(Equal Error Rate) test compared with broadcast voice modulation using three modulation methods: McAdams, Resampling, and Vocal Tract Length Normalization(VTLN). Experimental results show VTLN modulation methods performed higher de-identification performance in both human tests and EER tests. As a result, the modulation methods of the Lightweight model for Korean speech has sufficient de-identification performance and will be able to replace the security-weak broadcast voice modulation.

• Journal of Microbiology and Biotechnology
• /
• v.10 no.3
• /
• pp.399-404
• /
• 2000

The adjuvant effect of a muramyl dipeptide (MDP) derivative, MDP-Lys(L18), on enhancing of antitumor immunity induced by X-irradiated tumor cells against highly metastatic B16-BL6 melanoma cells was examined in mice. Mice immunized intradermally (i.d.) with a mixture of X-irradiated B16-BL6 cells and MDP-Lys (L18) [Vac+MDP-Lys (L18)] followed by an intravenous (i.v.)inoculation of $10^4$ viable tumor cells 7 days after immunization, showed a significant inhibition of experimental lung metastasis of B16-BL6 melanoma cells. The most effective immunization for the prophylactic inhibition of tumor metastasis was obtained from the mixture of $100{\;}\mu\textrm{g}$ of MDP-Lys (L18) and $10^4$ X-irradiatied tumor vaccine. Furthermore, immunization of mice with Vac+MDP-Lys(L18), 3 days after tumor challenge, resulted in a significant inhibition of lung metastasis of B16-BL6 melanoma cells in an experimental lung metastasis model. Similarly, the administration of Vac+MDP-Lys(L18), 1 or 7 days after tumor removal, markedly inhibited tumor metastasis of B16-BL6 in a spontaneous lung metastasis model. When Vac+MDP-Lys (L18) was i.d. administered 3 days after subcutaneous (s.c.) inoculation of tumor cells ($5{\times}10^5/site$) on the back, mice treated with Vac+MDP-Lys(L18) showed inhibition of significantly tumor growth on day 20. These results suggest that MDP-Lys (L18) is able to enhance antitumor activity induced by X-irradiated tumor vaccine to reduce lung metastasis of tumor cells, and is a potent immunomodulating agent which may be applied prophylactically as well as therapeutically to treatment of cancer metastasis.