• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.1-10
• /
• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.494-510
• /
• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Journal of Korea Water Resources Association
• /
• v.54 no.12
• /
• pp.1305-1316
• /
• 2021

In this study, a new empirical formula for 2D transverse dispersion coefficient was developed using the results of previous tracer test studies, and the performance of the formula was evaluated. Since many tracer test studies have been conducted under the conditions where the width-to-depth ratio is less than 50, the existing empirical formulas developed using these imbalanced tracer test results have limitations in applying to rivers with a width-to-depth ratio greater than 50. Therefore, in order to develop an empirical formula for transverse dispersion coefficient using the imbalanced tracer test data, the Synthetic Minority Oversampling TEchnique (SMOTE) was used to oversample new data representing the properties of the existing tracer test data. The hydraulic data and the transverse dispersion coefficients in conditions of width-to-depth ratio greater than 50 were oversampled using the SMOTE. The reliability of the oversampled data was evaluated using the ROC (Receiver Operating Characteristic) curve. The empirical formula of transverse dispersion coefficient was developed including the oversampled data, and the performance of the results were compared with the empirical formulas suggested in previous studies using R². From the comparison results, the value of R² was 0.81 for the range of W/H < 50 and 0.92 for 50 < W/H, which were improved accuracy compared to the previous studies.

• KNOM Review
• /
• v.23 no.2
• /
• pp.18-28
• /
• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.347-364
• /
• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2009.10a
• /
• pp.461-462
• /
• 2009

• Proceedings of the IEEK Conference
• /
• 2002.06e
• /
• pp.87-90
• /
• 2002

This paper describes design technique of switched-capacitor 1V delta-sigma modulator. To solve the incomplete switching operation at low voltage, bootstrapping technique is used. For PMOS input pair of 1V operational amplifier, simple common mode level down technique is used. Designed 2nd order single loop modulator has an oversampling ratio of 64 and obtains a peak SNR of 71dB, a dynamic range of 73 dB with the power consumption of 350uW at 1V power supply.

• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.175-178
• /
• 2002

본 논문에서는 디지털 오디오 증폭기의 필수적인 부분인, PCM신호를 PW신호로 변환하는 기법에 대하여 고찰한다. 비교적 낮은 해상도의 PWM 신호로 변환할 때 발생하는 문제점들을 살펴보고, oversampling과 통과 대역에서 잡음을 억제하는 noise shaping을 적용한 기법에 대하여 살펴본다. 본 논문에서는 디지털 오디오 증폭기에 적합한 몇 가지 noise shaping 필터를 소개하고, 그들을 사용하였을 때 오디오 품질을 평가하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.629-632
• /
• 2021

머신러닝의 성능 저하에 크게 영향을 미치는 데이터 불균형은 데이터를 증강하거나 제거하여 해결할 수 있다. 본 논문에서는 지도학습에서 쓰이는 정답 데이터를 기반으로 새로운 데이터 증강기법인 CDBSMOTE을 제안한다. CDBSMOTE을 사용하면 임의의 값을 사용하지 않고, 기존의 데이터 증강기법의 문제점이었던 과적합을 최소화하며 지도학습 데이터를 효과적으로 증강시킬 수 있다.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.9
• /
• pp.125-139
• /
• 2014

In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression (SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.