• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2674-2697
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• 2019

Identity authentication is a crucial line of defense for network security, and passwords are still the mainstream of identity authentication. So far trawling password attacking has been extensively studied, but the research related with personal information is always sporadic. Probabilistic context-free grammar (PCFG) and Markov chain-based models perform greatly well in trawling guessing. In this paper we propose a systematic targeted attacking model based on structure partition and string reorganization by migrating the above two models to targeted attacking, denoted as TG-SPSR. In structure partition phase, besides dividing passwords to basic structure similar to PCFG, we additionally define a trajectory-based keyboard pattern in the basic grammar and introduce index bits to accurately characterize the position of special characters. Moreover, we also construct a BiLSTM recurrent neural network classifier to characterize the behavior of password reuse and modification after defining nine kinds of modification rules. Extensive experimental results indicate that in online attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 275%, and respectively outperforms its foremost counterparts, Personal-PCFG, TarGuess-I, by about 70% and 19%; In offline attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 90%, outperforms Personal-PCFG and TarGuess-I by 85% and 30%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.13-30
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• 2023

Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people's perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people's reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used-support vector machine (SVM), naïve Bayes (NB), logistic regression (LOGR), and random forest (RF)-in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.531-534
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• 2022

승차 공유, 카풀, 렌터카의 이용률이 증가하면서 많은 사용자가 동일한 차량에 로컬 액세스 할 수 있는 시나리오가 더욱 보편화됨에 따라 차량 네트워크에 대한 공격 가능성이 커지고 있다. 차량용 CAN Bus Network에 대한 DoS(Denial of Service), Fuzzy Attack 및 Replay Attack과 같은 공격은 일부 ECU(Electronic Controller Unit) 비활성 및 작동 불능 상태를 유발한다. 에어백, 제동 시스템과 같은 필수 시스템이 작동 불가 상태가 되어 운전자에게 치명적인 결과를 초래할 수 있다. 차량 네트워크 침입 탐지를 위하여 많은 연구가 진행되고 있으나, 기존 화이트리스트를 이용한 탐지 방법은 새로운 유형의 공격이 발생하거나 희소성이 높은 공격일 때 탐지하기 어렵다. 본 논문에서는 인공신경망 기반의 CAN 버스 네트워크 침입 탐지 기법을 제안한다. 제안하는 침입 탐지 기법은 2단계로 나누어 진다. 1단계에서 정상 패킷 분포를 학습한 VAE 모형이 이상 탐지를 수행한다. 이상 패킷으로 판정될 경우, 2단계에서 인코더로부터 추출된 잠재변수와 VAE의 재구성 오차를 이용하여 공격 유형을 분류한다. 분류 결과의 신뢰점수(Confidence score)가 임계치보다 낮을 경우 학습하지 않은 공격으로 판단한다. 본 연구 결과물은 정보보호 연구·개발 데이터 첼린지 2019 대회의 차량 이상징후 탐지 트랙에서 제공하는 정상 및 3종의 차량 공격시도 패킷 데이터를 대상으로 성능을 평가하였다. 실험을 통해 자동차 제조사의 규칙이나 정책을 사전에 정의하지 않더라도 낮은 오탐율로 비정상 패킷을 탐지해 낼 수 있음을 확인할 수 있다.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1725-1732
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• 2024

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.109-118
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• 2022

The data of the missing section among the vertex surface sea temperature observation data was imputed using the Bidirectional Recurrent Neural Network(BiRNN). Among artificial intelligence techniques, Recurrent Neural Networks (RNNs), which are commonly used for time series data, only estimate in the direction of time flow or in the reverse direction to the missing estimation position, so the estimation performance is poor in the long-term missing section. On the other hand, in this study, estimation performance can be improved even for long-term missing data by estimating in both directions before and after the missing section. Also, by using all available data around the observation point (sea surface temperature, temperature, wind field, atmospheric pressure, humidity), the imputation performance was further improved by estimating the imputation data from these correlations together. For performance verification, a statistical model, Multivariate Imputation by Chained Equations (MICE), a machine learning-based Random Forest model, and an RNN model using Long Short-Term Memory (LSTM) were compared. For imputation of long-term missing for 7 days, the average accuracy of the BiRNN/statistical models is 70.8%/61.2%, respectively, and the average error is 0.28 degrees/0.44 degrees, respectively, so the BiRNN model performs better than other models. By applying a temporal decay factor representing the missing pattern, it is judged that the BiRNN technique has better imputation performance than the existing method as the missing section becomes longer.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.138-144
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• 2018

A video is a vivid medium similar to human's visual-linguistic experiences, since it can inculcate a sequence of situations, actions or dialogues that can be told as a story. In this study, we propose story learning/regeneration frameworks from videos with successive event order supervision for contextual coherence. The supervision induces each episode to have a form of trajectory in the latent space, which constructs a composite representation of ordering and semantics. In this study, we incorporated the use of kids videos as a training data. Some of the advantages associated with the kids videos include omnibus style, simple/explicit storyline in short, chronological narrative order, and relatively limited number of characters and spatial environments. We build the encoder-decoder structure with successive event order embedding, and train bi-directional LSTMs as sequence models considering multi-step sequence prediction. Using a series of approximately 200 episodes of kids videos named 'Pororo the Little Penguin', we give empirical results for story regeneration tasks and SEOE. In addition, each episode shows a trajectory-like shape on the latent space of the model, which gives the geometric information for the sequence models.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.