Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539491
Zhilin Fu, Satya Chan, Sooyoung Kim
In this paper, we propose a complexity reduced soft interference cancellation minimum mean squared error (SIC-MMSE) detection scheme for coded massive MIMO systems. The presented method works efficiently when the channel matrix becomes asymptotically orthogonal with a sufficiently large number of receive antennas at the base station. With such a characteristic, the conventional SIC-MMSE detection method can be simplified to a layer independent matrix inversion process, and further complexity reduction is achieved by introducing the Neumann series expansion (NSE) method. The simulation results investigated in this paper reveal that the proposed method can efficiently reduce the complexity without appreciable performance degradation.
{"title":"Efficient SIC-MMSE Detection Using Neumann Series Expansion","authors":"Zhilin Fu, Satya Chan, Sooyoung Kim","doi":"10.1109/ICTC.2018.8539491","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539491","url":null,"abstract":"In this paper, we propose a complexity reduced soft interference cancellation minimum mean squared error (SIC-MMSE) detection scheme for coded massive MIMO systems. The presented method works efficiently when the channel matrix becomes asymptotically orthogonal with a sufficiently large number of receive antennas at the base station. With such a characteristic, the conventional SIC-MMSE detection method can be simplified to a layer independent matrix inversion process, and further complexity reduction is achieved by introducing the Neumann series expansion (NSE) method. The simulation results investigated in this paper reveal that the proposed method can efficiently reduce the complexity without appreciable performance degradation.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128587572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539426
Ho-chul Shin, Jae-Y. Lee
In this study, we have developed abstracted pedestrian behavior representation and classification method for pedestrian video surveillance system. An effective intelligent surveillance system can be constructed if the high-resolution surveillance image information is efficiently summarized. The motion of the pedestrian is represented by a multi-layer grid map using a detector and a tracker. A normal pattern and anomalous pattern database were constructed and classified using the CNN classifier. With the abstracted pedestrian data and CNN network, the abnormal situation can be detected up to recall 92.0%, precision 99.9%.
{"title":"Pedestrian Video Data Abstraction and Classification for Surveillance System","authors":"Ho-chul Shin, Jae-Y. Lee","doi":"10.1109/ICTC.2018.8539426","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539426","url":null,"abstract":"In this study, we have developed abstracted pedestrian behavior representation and classification method for pedestrian video surveillance system. An effective intelligent surveillance system can be constructed if the high-resolution surveillance image information is efficiently summarized. The motion of the pedestrian is represented by a multi-layer grid map using a detector and a tracker. A normal pattern and anomalous pattern database were constructed and classified using the CNN classifier. With the abstracted pedestrian data and CNN network, the abnormal situation can be detected up to recall 92.0%, precision 99.9%.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124774630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539518
Seung-Jun Han, Jungyu Kang, Yongwoo Jo, Dongjin Lee, Jeongdan Choi
One of the essential technologies required for environmental recognition of an autonomous vehicle is a localization technique that recognizes the position and orientation of the vehicle. In contrast to previous localization techniques that generate map data from sensor data itself, there is an increasing number of studies using high definition (HD) digital maps. The map-based localization technology consists of predicting the position of the next step through the ego-motion of the vehicle and determining the position through map matching. In this paper, we propose a robust ego-motion estimation and map matching technology for robust vehicle localization. First, we propose a visual odometry (VO) model for robust ego-motion estimation and a vehicle planar motion model based on in-vehicle sensors to improve the robustness of VO in the absence of image features. We also introduce a new line segmentation matching model and a geometric correction method of extracted road marking from an inverse perspective mapping (IPM) for robust map matching techniques. The technology proposed in this paper has been verified in various ways through real autonomous vehicles and successfully acquired the autonomous driving license of the Republic of Korea.
{"title":"Robust Ego-motion Estimation and Map Matching Technique for Autonomous Vehicle Localization with High Definition Digital Map","authors":"Seung-Jun Han, Jungyu Kang, Yongwoo Jo, Dongjin Lee, Jeongdan Choi","doi":"10.1109/ICTC.2018.8539518","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539518","url":null,"abstract":"One of the essential technologies required for environmental recognition of an autonomous vehicle is a localization technique that recognizes the position and orientation of the vehicle. In contrast to previous localization techniques that generate map data from sensor data itself, there is an increasing number of studies using high definition (HD) digital maps. The map-based localization technology consists of predicting the position of the next step through the ego-motion of the vehicle and determining the position through map matching. In this paper, we propose a robust ego-motion estimation and map matching technology for robust vehicle localization. First, we propose a visual odometry (VO) model for robust ego-motion estimation and a vehicle planar motion model based on in-vehicle sensors to improve the robustness of VO in the absence of image features. We also introduce a new line segmentation matching model and a geometric correction method of extracted road marking from an inverse perspective mapping (IPM) for robust map matching techniques. The technology proposed in this paper has been verified in various ways through real autonomous vehicles and successfully acquired the autonomous driving license of the Republic of Korea.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130077930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539695
Amina Fellan, Christian Schellenberger, Marc Zimmermann, H. Schotten
Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.
{"title":"Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0","authors":"Amina Fellan, Christian Schellenberger, Marc Zimmermann, H. Schotten","doi":"10.1109/ICTC.2018.8539695","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539695","url":null,"abstract":"Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128715924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539498
Lakju Sung, Sangmi Noh, D. Cho
As millimeter-wave (mmWave) communication based on massive multiple input multiple output (MIMO) system has promised as a new technology, beamforming techniques are studied actively to overcome high path-loss. Since antenna element patterns and beam patterns are distorted because of mutual coupling between antennas, it is important to consider practical antenna element radiation pattern when beamforming are studied and used to reflect practical environment. By knowing practical antenna element patterns, mutual coupling problems can be solved and beamforming techniques can be studied more flexibly considering practical environments. It is, however, very difficult to measure antenna element patterns because of small peak gain compared to beam gain. Therefore, we propose an antenna element pattern estimation algorithm based on array beam pattern which can be measured relatively easily.
{"title":"Antenna Element Pattern Estimation based on Array Beam Pattern","authors":"Lakju Sung, Sangmi Noh, D. Cho","doi":"10.1109/ICTC.2018.8539498","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539498","url":null,"abstract":"As millimeter-wave (mmWave) communication based on massive multiple input multiple output (MIMO) system has promised as a new technology, beamforming techniques are studied actively to overcome high path-loss. Since antenna element patterns and beam patterns are distorted because of mutual coupling between antennas, it is important to consider practical antenna element radiation pattern when beamforming are studied and used to reflect practical environment. By knowing practical antenna element patterns, mutual coupling problems can be solved and beamforming techniques can be studied more flexibly considering practical environments. It is, however, very difficult to measure antenna element patterns because of small peak gain compared to beam gain. Therefore, we propose an antenna element pattern estimation algorithm based on array beam pattern which can be measured relatively easily.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121891811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539495
Jihye Moon, Sanghun Kim
Voice signal is an indicator finding a progression of diseases such as nerve disorder and muscle dysfunction. To improve the performance of medical diagnosis system using the voice signal, this paper suggests a new feature extraction method which combines higher-order statistics (HOS) and higher-order differential energy operator (DEO). For the experiment, Saarbruecken Voice Database (SVD) was used, and 687 healthy voice samples and 263 pathological voice samples which consist of Cysts, Paralysis, and Polyp were selected. In addition, the OpenSmile script which provides 6,373 features was used for comparison with our new features. To decide the most effective features, Gradient Boosting was conducted as a feature selector. Finally, 20 features including 15 combinations of HOS and DEO were chosen, and deep neural network(DNN) was trained using the new features. The best accuracy of 87.4% was obtained, which exceeds the best accuracy of 84.5% with the existing features. The finding suggests a possibility that the pathological voice can be efficiently detected with only statistical information without heavy computations such as convolutional neural networks. Due to the simple structure, we expect this approach will be easily applied to a variety of mobile systems.
{"title":"An approach on a combination of higher-order statistics and higher-order differential energy operator for detecting pathological voice with machine learning","authors":"Jihye Moon, Sanghun Kim","doi":"10.1109/ICTC.2018.8539495","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539495","url":null,"abstract":"Voice signal is an indicator finding a progression of diseases such as nerve disorder and muscle dysfunction. To improve the performance of medical diagnosis system using the voice signal, this paper suggests a new feature extraction method which combines higher-order statistics (HOS) and higher-order differential energy operator (DEO). For the experiment, Saarbruecken Voice Database (SVD) was used, and 687 healthy voice samples and 263 pathological voice samples which consist of Cysts, Paralysis, and Polyp were selected. In addition, the OpenSmile script which provides 6,373 features was used for comparison with our new features. To decide the most effective features, Gradient Boosting was conducted as a feature selector. Finally, 20 features including 15 combinations of HOS and DEO were chosen, and deep neural network(DNN) was trained using the new features. The best accuracy of 87.4% was obtained, which exceeds the best accuracy of 84.5% with the existing features. The finding suggests a possibility that the pathological voice can be efficiently detected with only statistical information without heavy computations such as convolutional neural networks. Due to the simple structure, we expect this approach will be easily applied to a variety of mobile systems.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115984437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of the Internet and the innovative attacks, information security has become an important issue for system administrators and users. Because the traditional intrusion detection system is based on misuse detection technology, the disadvantage is that it needs constant updating of the feature database to cope with attacks from variant malware. This paper proposes a framework of deep learning model by using the TensorFlow platform and utilizes the NSL-KDD data set for training and testing the proposed framework. Experimental results show the proposed methodology can effectively classify malicious traffic categories.
{"title":"Classification of Malicious Traffic Using TensorFlow Machine Learning","authors":"Li-Der Chou, Chia-Wei Tseng, Meng-Sheng Lai, Wei-Yu Chen, Kuo-Chung Chen, Chia-Kuan Yen, Tsung-Fu Ou, Wei-Hsiang Tsai, Yi-Hsuan Chiu","doi":"10.1109/ICTC.2018.8539685","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539685","url":null,"abstract":"With the rapid development of the Internet and the innovative attacks, information security has become an important issue for system administrators and users. Because the traditional intrusion detection system is based on misuse detection technology, the disadvantage is that it needs constant updating of the feature database to cope with attacks from variant malware. This paper proposes a framework of deep learning model by using the TensorFlow platform and utilizes the NSL-KDD data set for training and testing the proposed framework. Experimental results show the proposed methodology can effectively classify malicious traffic categories.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116170262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539696
Muhammad Hanif, Choonhwa Lee
In the past decade, there has been a boom in the volume of data and in the popularity of cloud applications with industry and academia keenly interested in big data analytics, streaming application, and social networking applications. This led to the emergence of real-time distributed stream processing systems such as Flink, Storm, Dataflow, and Samza. These systems process complex queries on streaming data sets to be distributed across multiple worker nodes in a cluster. Few of them provide adequate supports to adapt the topologies of stream processing tasks to changing input workload. We present an intelligent and efficient topology adjustment scheme which allow Flink framework to refine its topology on the basis of incoming workload. It is designed to increase the overall performance by making the refining of topology robust according to incoming workload streams on the fly, while maintaining SLA constraints. Apache Flink distributed processing engine is used as testbed in the paper. Our preliminary results indicate that the proposed system outperforms the existing default framework.
{"title":"An Efficient Topology Refining Scheme for Apache Flink","authors":"Muhammad Hanif, Choonhwa Lee","doi":"10.1109/ICTC.2018.8539696","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539696","url":null,"abstract":"In the past decade, there has been a boom in the volume of data and in the popularity of cloud applications with industry and academia keenly interested in big data analytics, streaming application, and social networking applications. This led to the emergence of real-time distributed stream processing systems such as Flink, Storm, Dataflow, and Samza. These systems process complex queries on streaming data sets to be distributed across multiple worker nodes in a cluster. Few of them provide adequate supports to adapt the topologies of stream processing tasks to changing input workload. We present an intelligent and efficient topology adjustment scheme which allow Flink framework to refine its topology on the basis of incoming workload. It is designed to increase the overall performance by making the refining of topology robust according to incoming workload streams on the fly, while maintaining SLA constraints. Apache Flink distributed processing engine is used as testbed in the paper. Our preliminary results indicate that the proposed system outperforms the existing default framework.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120893478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539508
Yuki Yoshida, Yoshihiro Ito
IoT (Internet of Things) that everything is connected to the Internet and communicates is beginning to spread, and network control is required to respond to the rapid increase in traffic volume by IoT. However, it is difficult to realize such control with only existing protocols, and it takes time and cost to reform the protocol. Therefore, reference [1] suggests using IoT devices as SDN equipment and proposes TCP multi-pathization method with SDN which selects paths based on packet length and traffic volume of each path.On the other hand, due to the upgraded performance of IoT devices, it is expected to use Web services even on IoT devices, and it is conceivable to apply the [1] method to Web services. However, reference [1] does not validate the effectiveness of using Web services. In this paper, we aim to investigate the applicability of [1] method to Web services, and measure and compare the HTTP throughput and the response time by experiment. Experimental results show that this method is suitable for using Web services.
万物互联互通的物联网(Internet of Things)开始普及，需要网络控制来应对物联网带来的流量的快速增长。然而，仅依靠现有协议很难实现这种控制，并且对协议进行改革需要时间和成本。因此文献[1]建议使用物联网设备作为SDN设备，并提出了TCP多路径化方法，通过SDN根据每条路径的数据包长度和流量选择路径。另一方面，由于物联网设备性能的提升，甚至在物联网设备上也有望使用Web服务，可以想象将[1]方法应用于Web服务。但是，参考文献[1]并没有验证使用Web服务的有效性。本文旨在研究[1]方法在Web服务中的适用性，并通过实验对HTTP吞吐量和响应时间进行测量和比较。实验结果表明，该方法适用于Web服务的使用。
{"title":"Application of TCP Multi-Pathization Method with SDN by IoT Devices to Web Service","authors":"Yuki Yoshida, Yoshihiro Ito","doi":"10.1109/ICTC.2018.8539508","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539508","url":null,"abstract":"IoT (Internet of Things) that everything is connected to the Internet and communicates is beginning to spread, and network control is required to respond to the rapid increase in traffic volume by IoT. However, it is difficult to realize such control with only existing protocols, and it takes time and cost to reform the protocol. Therefore, reference [1] suggests using IoT devices as SDN equipment and proposes TCP multi-pathization method with SDN which selects paths based on packet length and traffic volume of each path.On the other hand, due to the upgraded performance of IoT devices, it is expected to use Web services even on IoT devices, and it is conceivable to apply the [1] method to Web services. However, reference [1] does not validate the effectiveness of using Web services. In this paper, we aim to investigate the applicability of [1] method to Web services, and measure and compare the HTTP throughput and the response time by experiment. Experimental results show that this method is suitable for using Web services.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/ICTC.2018.8539650
Sunoh Choi, Youngsoo Kim, Jonghyun Kim
Hundreds of thousands of new malicious files are being created every day. Existing pattern-based vaccine engines cannot detect these new malicious files. To solve these problems, artificial intelligence based malicious file detection methods have been proposed. However, artificial intelligence based malicious file detection method has a disadvantage that takes long time because it requires dynamic analysis. We can use similarity hashes to solve these problems and find similar files. However, it also takes a long time to compare similarity hashes when there are many files. To solve this problem, this paper proposes a method to generate similarity hash index.
{"title":"Similarity Hash Index","authors":"Sunoh Choi, Youngsoo Kim, Jonghyun Kim","doi":"10.1109/ICTC.2018.8539650","DOIUrl":"https://doi.org/10.1109/ICTC.2018.8539650","url":null,"abstract":"Hundreds of thousands of new malicious files are being created every day. Existing pattern-based vaccine engines cannot detect these new malicious files. To solve these problems, artificial intelligence based malicious file detection methods have been proposed. However, artificial intelligence based malicious file detection method has a disadvantage that takes long time because it requires dynamic analysis. We can use similarity hashes to solve these problems and find similar files. However, it also takes a long time to compare similarity hashes when there are many files. To solve this problem, this paper proposes a method to generate similarity hash index.","PeriodicalId":417962,"journal":{"name":"2018 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122241726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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