• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.365-369
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• 2006

Multiple Message Authentication Codes can be represented by one of the Short MAC, Bloom Filter or Compressed Bloom Filler to reduce communication overheads. However, this will inevitably increase false positive rate (fpr) which is a false authentication probability of adversarial messages in trade-off of communication efficiency. While the simple short MAC scheme has the lowest fpr, one cannot choose arbitrary authenticator size. Bloom filter, randomized data structure often used for membership queries, can represent multiple MACs more flexibly with slightly higher fpr. Furthermore, compressed Bloom filter has the same fpr with the short MAC while maintaining its flexibility. Through our detailed analysis, we show that pros and cons of the three schemes are scenario specific. Therefore one can choose appropriate scheme under given parameters to achieve both communication efficiency and security based on our results.

• Journal of Broadcast Engineering
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• v.24 no.7
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• pp.1266-1275
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• 2019

With increasing criminal rates and number of CCTVs, much attention has been paid to intelligent surveillance system on the horizon. Object detection and tracking algorithms have been developed to reduce false alarms and accurately help security agents immediately response to undesirable changes in video clips such as crimes and accidents. Many studies have proposed a variety of algorithms to improve accuracy of detecting and tracking objects outside tunnels. The proposed methods might not work well in a tunnel because of low illuminance significantly susceptible to tail and warning lights of driving vehicles. The detection performance has rarely been tested against the tunnel environment. This study investigated a feasibility of object detection and tracking in an actual tunnel environment by utilizing YOLOv3 and Kernelized Correlation Filter. We tested 40 actual video clips to differentiate pedestrians and motorcycles to evaluate the performance of our algorithm. The experimental results showed significant difference in detection between pedestrians and motorcycles without false positive rates. Our findings are expected to provide a stepping stone of developing efficient detection algorithms suitable for tunnel environment and encouraging other researchers to glean reliable tracking data for smarter and safer City.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.260-267
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• 2021

In this paper, we intend to use a new mixed model of YoloV5 and DeepSort. For fire detection, we want to increase the accuracy by automatically extracting the characteristics of the flame in the image from the training data and using it. In addition, the high false alarm rate, which is a problem of fire detection, is to be solved by using this new mixed model. To confirm the results of this paper, we tested indoors and outdoors, respectively. Looking at the indoor test results, the accuracy of YoloV5 was 75% at 253Frame and 77% at 527Frame, and the YoloV5+DeepSort model showed the same accuracy at 75% at 253 frames and 77% at 527 frames. However, it was confirmed that the smoke and fire detection errors that appeared in YoloV5 disappeared. In addition, as a result of outdoor testing, the YoloV5 model had an accuracy of 75% in detecting fire, but an error in detecting a human face as smoke appeared. However, as a result of applying the YoloV5+DeepSort model, it appeared the same as YoloV5 with an accuracy of 75%, but it was confirmed that the false positive phenomenon disappeared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.919-929
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• 2013

Recently, a variety of IT technologies are applied to the vehicle. However, some vehicle-IT technologies without security considerations may cause security problems. Specially, some researches about a smart key system applied to automobiles for authentication show that the system is insecure from replay attacks and modification attacks using a wireless signal of the smart key. Thus, in this paper, we propose an authentication method for the driver by using driving patterns. Nowadays, we can obtain driving patterns using the In-vehicle network data. In our authentication model, we make driving ppatterns of car owner using standard normal distribution and apply these patterns to driver authentication. To validate our model, we perform an k-fold cross validation test using In-vehicle network data and obtain the result(true positive rate 0.7/false positive rate is 0.35). Considering to our result, it turns out that our model is more secure than existing 'what you have' authentication models such as the smart key if the authentication result is sent to the car owner through mobile networks.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• The Korean Journal of Pain
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• v.14 no.1
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• pp.26-31
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• 2001

Background: Sympathetic blocks with local anesthetics are used to differentiate sympathetically- maintained pain (SMP) from sympathetically-independent pain (SIP). However, systemic lidocaine is also used in the management of neuropathic pain. Therefore, there may be possibility of a false positive response in relieving their pain by systemic absorption of lidocaine following a diagnostic sympathetic block in patients with SIP. In this study, we measured the plasma lidocaine concentrations after a stellate ganglion block (SGB) using three volumes of 1% lidocaine. Methods: This prospective, crossover study was performed in 3 patients who experience sudden hearing loss and in 4 volunteers. Each person received SGB three times using three different volumes (6 ml, 12 ml and 16 ml) of 1% lidocaine at one week intervals. SGB was performed using a 23 G butterfly needle via a paratracheal approach by two persons. Two ml of venous blood was obtained from a prepared contra-lateral sided venous route at 1, 3, 5, 7, 10, 20 and 60 min after SGB. Plasma lidocaine level was analyzed by immunoassay. Results: Mean plasma lidocaine concentrations correlated well with the volumes of 1% lidocaine used in SGB; larger volumes showed higher concentrations (P < 0.01). Mean peak plasma concentrations were $1.08{\pm}0.18$ in 6 ml, $1.90{\pm}0.47$ in the 12 ml and $2.74{\pm}0.67{\mu}g/ml$ in the 16 ml groups (P < 0.01). The mean time to reach peak plasma concentration was not significantly different between the three groups. Conclusions: The peak plasma lidocaine concentrations in SGB using large volume were found to be similar to that of IV lidocaine infusion in the management of neuropathic pain. These data suggest that diagnostic sympathetic block may result in many false positive responses for SMP. Part of its effect may be related to systemic local anesthetic absorption and not to a sympathetic block. Therefore, physicians may be required to use optimal volumes and minimal concentration of local anesthetic in diagnostic sympathetic block procedures and also make a careful assessment of the performance of a permanent sympathetic block.

• Journal of Trauma and Injury
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• v.31 no.3
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• pp.151-158
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• 2018

Purpose: The regional emergency medical centers manage the patients with major blunt trauma according to the process appropriate to each hospital rather than standardized protocol of the major trauma centers. The primary purpose of this study is to evaluate the effectiveness and influence on prognosis of additional cervical-thoracic-lumbar-spine computed tomography (CTL-spine CT) scan in diagnosis of spinal injury from the victim of major blunt trauma with impaired consciousness. Methods: The study included patients visited the urban emergency medical center with major blunt trauma who were over 18 years of age from January 2013 to December 2016. Data were collected from retrospective review of medical records. Sensitivity, specificity, positive predictive value, and negative predictive value were measured for evaluation of the performance of diagnostic methods. Results: One hundred patients with Glasgow coma scale ${\leq}13$ underwent additional CTL-spine CT scan. Mechanism of injury was in the following order: driver, pedestrian traffic accident, fall and passenger accident. Thirty-one patients were diagnosed of spinal injury, six of them underwent surgical management. The sensitivity of chest, abdomen and pelvis CT (CAP CT) was 72%, specificity 97%, false positive rate 3%, false negative rate 28% and diagnostic accuracy 87%. Eleven patients were not diagnosed of spinal injury with CAP CT and C-spine lateral view, but all of them were diagnosed of stable fractures. Conclusions: C-spine CT scan be actively considered in the initial examination process. When CAP CT scan is performed in major blunt trauma patients with impaired consciousness, CTL-spine CT scan or simple spinal radiography has no significant effect on the prognosis of the patient and can be performed if necessary.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.351-359
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• 2022

Objectives: The Regional CardioCerebroVascular Center (RCCVC) project was initiated to improve clinical outcomes for patients with acute myocardial infarction or stroke in non-capital areas of Korea. The purpose of this study was to evaluate the outcomes and issues identified by the Busan RCCVC project in the treatment of ST-segment elevation myocardial infarction (STEMI). Methods: Among the patients who were registered in the Korean Registry of Acute Myocardial Infarction for the RCCVC project between 2007 and 2019, those who underwent percutaneous coronary intervention (PCI) for STEMI at the Busan RCCVC were selected, and their medical data were compared with a historical cohort. Results: In total, 1161 patients were selected for the analysis. Ten years after the implementation of the Busan RCCVC project, the median door-to-balloon time was reduced from 86 (interquartile range [IQR], 64-116) to 54 (IQR, 44-61) minutes, and the median symptom-to-balloon time was reduced from 256 (IQR, 180-407) to 189 (IQR, 118-305) minutes (p<0.001). Inversely, the false-positive PCI team activation rate increased from 0.6% to 21.4% (p<0.001). However, the 1-year cardiovascular death and major adverse cardiac event rates did not change. Even after 10 years, approximately 75% of the patients had a symptom-to-balloon time over 120 minutes, and approximately 50% of the patients underwent inter-hospital transfer for primary PCI. Conclusions: A decade after the implementation of the Busan RCCVC project, although time parameters for early reperfusion therapy for STEMI improved, at the cost of an increased false-positive PCI team activation rate, survival outcomes were unchanged.