• International journal of advanced smart convergence
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• v.9 no.1
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• pp.113-120
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• 2020

Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.1044-1047
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• 2013

Wireless Mesh Network (WMN) provides effective Internet Service accesses to users by utilizing multi-rate and multi-channel. In multi-rate networks, the Rate Anomaly (RA) problem occurs, the problem that low-rate link degrades the performance of high-rate link. In this paper we propose Rate Gap Minimum Channel Assignment (RGM-CA) protocol that select the minimal rate gap parent node and assign the channel in order to mitigates the rate anomaly problem. RDM-CA protocol is efficient because it consider rate anomaly, channel diversity and node connectivity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1152-1159
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• 2010

Wireless Mesh Networks (WMNs) provides Internet accesses to users by forming backbone networks via wireless links. A key problem of WMN is network capacity. For this, multi-channel and multi-rate functions of IEEE 802.11 can be utilized. Depending on channel assignments, multi-channel determines node connectivity and channel diversity. Also, in IEEE 802.11 multi-rate networks, the rate anomaly problem occurs, the phenomenon that low-rate links degrades the performance of high-rate links. In this paper, we propose rate separating multi-channel (RSMC) protocols that improves the node connectivity and channel diversity, and mitigates the rate anomaly problem. RSMC increases the channel diversity by forming tree-based WMNs and decreases the rate anomaly by separating different rate links on the tree via channels. In addition, it uses network connectivity (NC) algorithm to increase the node connectivity. Through simulations, we demonstrate that the RSMC shows improved performance than existing multi-channel protocols in terms of aggregate throughput, node connectivity, channel diversity.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.341-345
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• 2010

WMN is one of efficient solutions to provide Internet services for users by forming wireless backbone networks with wireless links. The dominant technology for WMNs is the IEEE 802.11, which provides multi-channel and multi-rate capabilities. One of important issues in WMNs is the network capacity and it is essential to design a multi-channel protocol that leverages the network capacity. However, when wireless links that use different data rates operate on the common channel, the performance of high-rate links is severely degraded by the presence of the low-rate links, which is often referred as performance anomaly. In this paper, we propose a Tree-based Channel Assignment (TreeCA) protocol to mitigate the performance anomaly problem by distributing data rates over multiple channels. TreeCA performs channel assignments based on the tree WMN architecture to accommodate the Internet traffics efficiently. Parent nodes on the tree distribute their child nodes over multiple channels so that the performance anomaly is reduced. Through simulations, we observed that the proposed TreeCA outperforms the existing multi-channel protocols for WMNs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.391-394
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• 2004

Change of paradigm of network attack technique was begun by fast extension of the latest Internet and new attack form is appearing. But, Most intrusion detection systems detect informed attack type because is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, visibilitys to apply human immunity mechanism are appearing. In this paper, we create self-file from normal behavior profile about network packet and embody self recognition algorithm to use self-nonself discrimination in the human immune system to detect anomaly behavior. Sense change because monitors self-file creating anomaly detector based on Negative Selection Algorithm that is self recognition algorithm's one and detects anomaly behavior. And we achieve simulation to use DARPA Network Dataset and verify effectiveness of algorithm through the anomaly detection rate.

• Korean Journal of Head & Neck Oncology
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• v.10 no.2
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• pp.122-127
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• 1994

One hundred fifty four cases of branchial anomaly treated from January 1987 to July 1993 were analysed to determine clinical features, embryologic and anatomic types of the branchial cleft anomaly, to investigate the differences between adults and pediatrics, and to establish the appropriate treatment plan. The male to female ratio was not signifiacntly different in pediatric and adult patients. The mean symptom duration was 0.5 years(range 0.08-14 years) in pediatric patients and 1.67 years (0.7-7 years) in adult patients. The clinical presentations of these anomalies were lateral neck mass in 112(72.7%), infected discharge in 22(14.3%), non-infected discharge in 6(3.9%), and abscess in 14 cases(9.l%). Sites of the lesions were upper third of the neck in 93(60.3%), infraauricular in 35(22.7%), middle third of the neck in 17(11.0%) and inferior third of the neck in 9 cases(5.8%). The anatomic types were cystic form in 117(75.9%), sinus in 24(15.5%), and fistula in 13 cases(8.4%). Embryologic classification were 124 second branchial cleft anomalies(80.5%), 29 first branchial cleft anomalies(18.8%), and 1 third branchial cleft anomaly(0.6%). Immediate surgery under the uncontrolled infection in 17 cases result in 82.4% recurrent rate(14 cases), and 17.6% cure rate(3 cases). Delayed surgery under the controlled infection in 8 cases recurrent rate(1 case), and 87.5% cure rate(7 cases). In summary, the most common branchial cleft anomaly is second type cyst both in pediatric and adult group, delayed surgical exterpation after infection control with I & D or antibiotics may give a good chance for care and may reduce the recurrence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3093-3115
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• 2020

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2362-2378
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• 2017

To mitigate the performance degradation caused by performance anomaly, a number of channel assignment algorithms have been proposed for multi-rate wireless mesh networks. However, network conditions have not been fully considered for routing process in these algorithms. In this paper, a joint scheme called Multi-rate Dijkstra's Shortest path - Rate Separated (MDSRS) is proposed, combining routing metrics and channel assignment algorithm. In MDSRS, the routing metric are determined through the synthesized deliberations of link costs and rate matches; then the rate separated channel assignment is operated based on the determined routing metric. In this way, the competitions between high and low rate links are avoided, and performance anomaly problem is settled, and the network capacity is efficiently improved. Theoretical analysis and NS-3 simulation results indicate that, the proposed MDSRS can significantly improve the network throughput, and decrease the average end-to-end delay as well as packet loss probability. Performance improvements could be achieved even in the heavy load network conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1A
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• pp.73-81
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• 2011

IEEE 802.11 PHY and MAC layer provide multiple channels and data rates. To improve the performance of IEEE 802.11 multi-radio ad hoc networks, it is required to utilize available channels and data rates efficiently. However, in IEEE 802.11 multi-rate networks, the rate anomaly (RA) problem occurs that the network performance is severely degraded as low-rate links affect high-rate links. Hence, in this paper, we propose a model-based rate separation (MRS) algorithm that uses multiple channels to separate different data rate links so that the RA problem is mitigated. MRS algorithm utilizes an existing throughput model that estimates the throughput of IEEE 802.11 single-hop networks to separate low-rate links and high-rate links. Through simulations, we demonstrate that the MRS algorithm shows improved network performance compared with existing algorithms in multi-radio ad hoc networks.