• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.2
• /
• pp.626-643
• /
• 2023

As mobile forensics has emerged as an essential technique, the demand for technology development, education and training is increasing, wherein images are used. Academic societies in South Korea and national institutions in the US and the UK are leading the Mobile Forensic Image development. However, compared with disks, images developed in a mobile environment are few cases and have less active research, causing a waste of time, money, and manpower. Mobile Forensic Images are also difficult to trust owing to insufficient verification processes. Additionally, in South Korea, there are legal issues involving the Telecommunications Business Act and the Act on the Protection and Use of Location Information. Therefore, in this study, we requested a review of a standard model for the development of Mobile Forensic Image from experts and designed an 11-step development model. The steps of the model are as follows: a. setting of design directions, b. scenario design, c. selection of analysis techniques, d. review of legal issues, e. creation of virtual information, f. configuring system settings, g. performing imaging as per scenarios, h. Developing a checklist, i. internal verification, j. external verification, and k. confirmation of validity. Finally, we identified the differences between the mobile and disk environments and discussed the institutional efforts of South Korea. This study will also provide a guideline for the development of professional quality verification and proficiency tests as well as technology and talent-nurturing tools. We propose a method that can be used as a guide to secure pan-national trust in forensic examiners and tools. We expect this study to strengthen the mobile forensics capabilities of forensic examiners and researchers. This research will be used for the verification and evaluation of individuals and institutions, contributing to national security, eventually.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.6
• /
• pp.1706-1727
• /
• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Industry Promotion Research
• /
• v.8 no.1
• /
• pp.35-45
• /
• 2023

This paper addressed the analysis of the trend and direction of the US defense strategy based on their third offset strategy and presented the practical policy implication of ensuring the security of South Korea appropriately in the future national defense environment. The countermeasures for the development ability of advanced weapon systems and secure core technologies for Korea were presented in consideration of the US third offset strategy for the future national defense environment. First, to carry out the innovation of national defense in Korea based on artificial intelligence(AI), the long-term basis strategy for the operation of the unmanned robot and autonomous weapon system should be suggested. Second, the platform for AI has to be developed to obtain the development of algorithms and computing abilities for securing the collection/storage/management of national defense data. Lastly, advanced components and core technologies are identified, which the Korean government can join to develop with the US on a basis of the Korea-US alliance, and the technical cooperation with the US should be stronger.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.12
• /
• pp.3383-3397
• /
• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.6
• /
• pp.39-51
• /
• 2024

This paper proposes a novel approach to enhance the security of container-based systems by analyzing system calls to dynamically detect race conditions without modifying the kernel. Container escape attacks allow attackers to break out of a container's isolation and access other systems, utilizing vulnerabilities such as race conditions that can occur in parallel computing environments. To effectively detect and defend against such attacks, this study utilizes eBPF to observe system call patterns during attack attempts and employs a AdaBoost model to detect them. For this purpose, system calls invoked during the attacks such as Dirty COW and Dirty Cred from popular applications such as MongoDB, PostgreSQL, and Redis, were used as training data. The experimental results show that this method achieved a precision of 99.55%, a recall of 99.68%, and an F1-score of 99.62%, with the system overhead of 8%.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.1
• /
• pp.41-49
• /
• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

• Journal of the Korea Society for Simulation
• /
• v.33 no.2
• /
• pp.27-35
• /
• 2024

We aimed to shed light on the issue of perpetuating societal disparities by analyzing the impact of inherent biases present in datasets used for training artificial intelligence models on the predictions generated by Artificial Intelligence(AI). Therefore, to examine the influence of data bias on AI models, we constructed an original dataset containing biases related to gender wage gaps and subsequently created a de-identified dataset. Additionally, by utilizing the decision tree algorithm, we compared the outputs of AI models trained on both the original and de-identified datasets, aiming to analyze how data de-identification affects the biases in the results produced by artificial intelligence models. Through this, our goal was to highlight the significant role of data de-identification not only in safeguarding individual privacy but also in addressing biases within the data.

• Journal of Information Processing Systems
• /
• v.20 no.3
• /
• pp.375-390
• /
• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Korean Journal of Social Welfare Studies
• /
• v.44 no.3
• /
• pp.177-206
• /
• 2013

The Korean government has implemented the senior employment policy as a direct job creation policy since 2004. A realistic discussion of policy alternatives and orientation for this has been given little attention even though senior employment policy has been carried out for the last 10 years and it will be expanded next year. This study tries to examine active labor market policy especially focusing on direct job creation programs and policies for the disadvantaged low-income elderly in OECD countries, and then it suggests some developmental alternatives for senior employment policy based on the study's results. The main results from this analysis are summarized in two points. Firstly, except pension policies, employment policy for older workers in the OECD countries is highly proportional to the tackling of objective factors reducing the demand for older workers (wage subsidies, reduced social security contribution rate etc). And the strategies of improving employability have not been relatively important and direct job creation policy has been marginal. Secondly, employment support policies for the low-income elderly can be divided into three types: support for the low-income elderly, alleviating early retirement and support for full employment according to the criteria which are determined by policy objectives and the social economic index. Korea's employment support policies belong to the type of direct job creation among them. This seems to be due to the fact that the rate of elderly poverty is extremely high and an income security system has not been developed in Korea. However, the policy objective is still uncertain. Therefore, this policy needs to set up clear objectives and establish a proper system for the achievement of its goals. If we focus on the strength of its employment characteristics, we need to modify the policy's plan in the perspective of labor market policy. But if we intend to keep both of the current objectives, it is better for this policy to be divided into two parts: social participation and income supplements. Or it also may be a solution to transform the system into an employment service, a training system which supports participants to move into unsubsidized jobs such as SCSEP in the U. S.

• Journal of National Security and Military Science
• /
• s.15
• /
• pp.57-86
• /
• 2018

The ROKAF has been training for a number of exercise for victory in the war, but the lack of aviation ammunition has become a big issue every year. However, due to the limitation of defense resources, there are many difficulties in securing and stockpiling ammunition for the war readiness. Therefore, there is a need to find a way to secure aviation ammunition for war readiness in a more economical way, so In this study, we analyze the precedent research case and the case of the reduction of the purchase cost of weapon system of other countries, and then I have suggested a plan that is appropriate for our situation. As a result of examining previous research cases for this study, there were data that KIDA studied in 2012, Precision-guided weapons acquisition cost reduction measures pursued by US Air Force And the use of procurement agencies that are being implemented by NATO member countries. Based on this study, the following four measures were proposed to reduce the purchase cost of aviation ammunition. First, the mutual aid support agreement was developed to sign the ammunition joint operation agreement. Second, join the NATO Support & Procurement Agency (NSPA) Third, it builds a purchasing community centered on the countries operating the same ammunition Fourth, participating in the US Air Force's new purchase plan for ammunition and purchase it jointly. The main contents of these four measures are as follows. 1. the mutual aid support agreement was developed to sign the ammunition joint operation agreement. Korea has signed agreements on mutual logistics support with 14 countries including the United States, Israel, Indonesia, Singapore, Australia, and Taiwan. The main purpose of these agreements is mutual support of munitions and materials, also supporting the training of the peace time and promoting exchange and cooperation. However, it is expected that there will be many difficulties in requesting or supporting mutual support in actual situation because the target or scope of mutual aid of ammunition is not clearly specified. Thus, a separate agreement on the mutual co-operation of more specific and expanded concepts of aviation ammunition is needed based on the current mutual aid support agreements 2. join the NATO Support & Procurement Agency (NSPA) In the case of NATO, there is a system in which member countries purchase munitions at a low cost using munitions purchase agencies. It is the NATO Purchasing Agency (NSPA) whose mission is to receive the purchasing requirements of the Member Nations and to purchase them quickly and efficiently and effectively to the Member Nations. NSPA's business includes the Ammunition Support Partnership (ASP), which provides ammunition purchase and disarming services. Although Korea is not a member of NATO, NSPA is gradually expanding the scope of joint procurement of munitions, and it is expected that Korea will be able to join as a member. 3. it builds a purchasing community centered on the countries operating the same ammunition By benchmarking the NSPA system, this study suggested ways to build a purchasing community with countries such as Southeast Asia, Australia, and the Middle East. First, it is necessary to review prospectively how to purchase ammunition by constructing ammunition purchasing community centered on countries using same kind of ammunition. 4. participating in the US Air Force's new purchase plan for ammunition When developing or purchasing weapons systems, joint participation by several countries can reduce acquisition costs. Therefore, if the US Air Force is planning to acquire aviation ammunition by applying it to the purchase of aviation ammunition, we will be able to significantly reduce the purchase cost by participating in this plan. Finally, there are some limitations to the method presented in this study, but starting from this study, I hope that the research on these methods will be actively pursued in the future.