• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.3
• /
• pp.282-288
• /
• 2005

In this paper, we propose a new intrusion detection model, which keeps advantages of existing misuse detection model and anomaly detection model and resolves their problems. This new intrusion detection system, named to MMIDS, was designed to satisfy all the following requirements : 1) Fast detection of new types of attack unknown to the system; 2) Provision of detail information about the detected types of attack; 3) cost-effective maintenance due to fast and efficient learning and update; 4) incrementality and scalability of system. The fast and efficient training and updating faculties of proposed novel multi-class SVM which is a core component of MMIDS provide cost-effective maintenance of intrusion detection system. According to the experimental results, our method can provide superior performance in separating similar patterns and detailed separation capability of MMIDS is relatively good.

• Journal of Internet of Things and Convergence
• /
• v.8 no.5
• /
• pp.119-125
• /
• 2022

With the recent increase in dementia patients due to aging, measures to prevent their wandering behavior and disappearance are urgently needed. To solve this problem, various authentication methods and location detection techniques have been introduced, but the security problem of personal authentication and a system that can check indoor and outdoor overall was lacking. In order to solve this problem, various authentication methods and location detection techniques have been introduced, but it was difficult to find a system that can check the security problem of personal authentication and indoor/outdoor overall. In this study, we intend to propose a system that can identify personal authentication, basic health status, and overall location indoors and outdoors by using wristband-type wearable devices in a private blockchain environment. In this system, personal authentication uses ECG, which is difficult to forge and highly personally identifiable, Bluetooth beacon that is easy to use with low power, non-contact and automatic transmission and reception indoors, and DGPS that corrects the pseudorange error of GPS satellites outdoors. It is intended to detect wandering behavior and abnormal signs by locating the patient. Through this, it is intended to contribute to the prompt response and prevention of disappearance in case of wandering behavior and abnormal symptoms of dementia patients living at home or in nursing homes.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.2_1
• /
• pp.169-176
• /
• 2022

Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.4
• /
• pp.763-773
• /
• 2017

In recent years, personal information leakage and technology leakage accidents are frequently occurring. According to the survey, the most important part of this spill is the 'insider' within the organization, and the leakage of technology by insiders is considered to be an increasingly important issue because it causes huge damage to the organization. In this paper, we try to learn the normal behavior of employees using machine learning to prevent insider threats, and to investigate how to detect abnormal behavior. Experiments on the detection of abnormal behavior by implementing an Autoencoder composed of Recurrent Neural Network suitable for learning time series data among the neural network models were conducted and the validity of this method was verified.

• Journal of Internet of Things and Convergence
• /
• v.7 no.3
• /
• pp.69-74
• /
• 2021

With the recent development of biometric authentication technology, the user authentication techniques using biometric authentication are increasing. Various problems arised in certification techniques that use various existing methods such as ID/PW. Therefore, recently, a method of improving security by introducing biometric authentication as secondary authentication has been used. In this thesis, proposal of the user authentication system that can detect user identification and anomalies using ECGs that are extremely difficult to falsify through the electrical biometric signals from the heart among various biometric authentication devices is studied. The system detects user anomalies by comparing ECG data received from a wrist-mounted wearable device-type ECG measurement tool with identification and ECG data stored in blockchain form on the database and identifying the user's location through a beacon system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.5
• /
• pp.1009-1015
• /
• 2017

Recently, the wide spread of IoT (Internet of Things) based technology enables the accumulation of big biometric data on livestock. The availability of big data allows the application of diverse machine learning based algorithm in the field of agriculture, which significantly enhances the productivity of farms. In this paper, we propose an abnormal livestock detection algorithm based on deep learning, which is the one of the most prominent machine learning algorithm. In our proposed scheme, the livestock are divided into two clusters which are normal and abnormal (disease) whose biometric data has different characteristics. Then a deep neural network is used to classify these two clusters based on the biometric data. By using our proposed scheme, the normal and abnormal livestock can be identified based on big biometric data, even though the detailed stochastic characteristics of biometric data are unknown, which is beneficial to prevent epidemic such as mouth-and-foot disease.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.1
• /
• pp.25-38
• /
• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.3
• /
• pp.1-7
• /
• 2020

With the increase in cyber attacks, automated IDS using machine learning is being studied. According to recent research, the IDS using the recursive learning model shows high detection performance. However, the simple application of the recursive model may be difficult to reflect the associated session characteristics, as the overlapping session environment may degrade the performance. In this paper, we designed the session management module and applied it to LSTM (Long Short-Term Memory) recursive model. For the experiment, the CSE-CIC-IDS 2018 dataset is used and increased the normal session ratio to reduce the association of mal-session. The results show that the proposed model is able to maintain high detection performance even in the environment where session relevance is difficult to find.

• International Journal of Aeronautical and Space Sciences
• /
• v.16 no.1
• /
• pp.89-101
• /
• 2015

To satisfy civil aviation requirements using the Global Navigation Satellite System (GNSS), it is important to guarantee system integrity. In this work, we propose a fault detection algorithm for GNSS ephemeris anomalies. The basic principle concerns baseline length estimation with GNSS measurements (pseudorange, broadcasted ephemerides). The estimated baseline length is subtracted from the true baseline length, computed using the exact surveyed ground antenna positions. If this subtracted value differs by more than a given threshold, this indicates that an ephemeris anomaly has been detected. This algorithm is suitable for detecting Type A ephemeris failure, and more advantageous for use with multiple stations with various long baseline vectors. The principles of the algorithm, sensitivity analysis, minimum detectable error (MDE), and protection level derivation are described and we verify the sensitivity analysis and algorithm availability based on real GPS data in Korea. Consequently, this algorithm is appropriate for GNSS regional implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.4
• /
• pp.1426-1447
• /
• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.