CCSRI
Research

Kyonggi University's Contents Convergence Software Research Institute
is a Gyeonggi-do Regional Research Center (GRRC)
that conducts various academic/exchange activities in connection
with local industry and academic institutions,
creates intellectual property in the software engineering field
for crime prevention, and contribute to social safety.
Demand Survey Method for commercialization of Police Science Technology and Equipment
Myeonggi Hong;Junho Park;Sungju Hong;JeongHyeon Chang, (2023.)
KSII Transactions on Internet and Information Systems, vol. 7, no. 2, pp. 609-625

Traffic classification using distributions of latent space in software-defined networks: An experimental evaluation
Jang, Y., Kim, N., & Lee, B. D. (2023.)
Engineering Applications of Artificial Intelligence, vol. 119, 105736.
With the emergence of new Internet services and the drastic increase in Internet traffic, traffic classification has become increasingly important to effectively satisfy the quality of service to users. The traffic classification system should be resilient and operate smoothly regardless of network conditions or performance and should be capable of handling various classes of Internet services. This paper proposes a traffic classification method in a software-defined network environment that employs a variational autoencoder (VAE) to accomplish this. The proposed method trains the VAE using six statistical features and extracts the distributions of latent features for the flows in each service class. Furthermore, it classifies the query traffic by comparing the distributions of latent features for the query traffic with the learned distributions of the service classes. For the experiment, the statistical features of network flows were collected from real-world domestic and overseas Internet services for training and testing. According to the experimental results, the proposed method has an average accuracy of 89%. This accuracy was 52%, 47%, 39%, 59%, and 26% higher than conventional statistics-based classification methods, MLP, AE+MLP, VAE+MLP, and SVM, respectively. This result clearly suggests that probability distributions of latent features, rather than specific values for latent features, can be used as more stable features.

Explainable Anomaly Detection Using Vision Transformer Based SVDD
Baek, Ji-Won & Chung, Kyungyong. (2023.)
Computers, Materials & Continua. vol. 74, no. 6573-6586.
Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships. It is possible to offer the explainable basis of decision-making for inference results. Through the causality of risk factors that have an ambiguous association in big medical data, it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status. In addition, the technique makes it possible to accurately predict disease risk for anomaly detection. Vision transformer for anomaly detection from image data makes classification through MLP. Unfortunately, in MLP, a vector value depends on patch sequence information, and thus a weight changes. This should solve the problem that there is a difference in the result value according to the change in the weight. In addition, since the deep learning model is a black box model, there is a problem that it is difficult to interpret the results determined by the model. Therefore, there is a need for an explainable method for the part where the disease exists. To solve the problem, this study proposes explainable anomaly detection using vision transformer-based Deep Support Vector Data Description (SVDD). The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision transformer. In order to draw the explain-ability of model results, it visualizes normal parts through Grad-CAM. In health data, both medical staff and patients are able to identify abnormal parts easily. In addition, it is possible to improve the reliability of models and medical staff. For performance evaluation normal/abnormal classification accuracy and f-measure are evaluated, according to whether to apply SVDD. Evaluation Results The results of classification by applying the proposed SVDD are evaluated excellently. Therefore, through the proposed method, it is possible to improve the reliability of decision-making by identifying the location of the disease and deriving consistent results.

The Impact of Prior Victim-Police Interactions on Subsequent Police Utilization for Intimate Partner Violence
이진아, 장정현, 황의갑 and 이승욱. (2022.)
한국범죄학, 16(3), 57-78.

비정형 데이터 수집과 TF-IDF를 통한 아동학대 분석 및 키워드 추출
이예은 and 장정현. (2022.)
한국범죄심리연구, 18(4), 171-182.