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국회도서관 홈으로 정보검색 소장정보 검색

결과 내 검색

동의어 포함

권호기사

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 목차
흉부 볼륨 CT영상에서 Weighted Integration Loss을 이용한 폐암 분할 알고리즘 연구 = A study on lung cancer segmentation algorithm using weighted integration loss on volumetric chest CT image 정진교, 김영재, 김광기 p. 625-632
LSTM 및 정보이득 기반의 악성 안드로이드 앱 탐지연구 = A study on detection of malicious android apps based on LSTM and information gain 안유림, 홍승아, 김지연, 최은정 p. 641-649
통신부대 임무수행을 위한 전장온톨로지 = Battlefield ontology for communication unit mission 이유진, 이경호 p. 667-672
폭염 대응전략 수립을 위한 폭염위험도 시각화 플랫폼 = The hazard viz-platform for the establishment of heatwave response strategies 김미연 p. 683-699
가상현실 HMD기기의 시각체계 분석을 위한 시각 알고리즘 구축 = Construction of visual algorithms for the visual system analysis of virtual reality HMD devices : through interactive visual system analysis that appears in media art : 미디어 아트에서 나타나는 인터렉티브형 시각체계 분석을 통해 임상국 p. 721-727
3D 모델링 기반 빌딩관제시스템의 설계 및 구현 = Design and implementation of building control system based 3D modeling 문상호, 김병목, 이계은 p. 673-682
블록체인 환경에서의 PGP 인증 시스템 = PGP certification system in blockchain environments 김대한, 서경룡 p. 658-666
TFT-LCD영상에서 결함 가능성에 따른 순차적 결함영역 분할 = Sequential defect region segmentation according to defect possibility in TFT-LCD image 장충환, 이승민, 박길흠 p. 633-640
악성코드의 이미지 기반 딥러닝을 위한 전처리 방법 설계 및 개발 = Design and implementation of a pre-processing method for image-based deep learning of malware 박지현, 김태옥, 신유림, 김지연, 최은정 p. 650-657
스토리텔링 지원을 위한 효율적인 VR 콘텐츠 저작도구 개발 = Development of efficient VR contents writing tools for support storytelling 이양민, 이재기 p. 700-709
지진해일로 인한 해안 침수 분석을 위한 셀 오토마타 기반의 시뮬레이션 모델 개발 = A tsunami simulation model based on cellular automata for analyzing coastal inundation : case study of Gwangalli Beach : 광안리 해변 사례 연구 주재우, 주준모, 김동민, 이동훈, 최선한 p. 710-720

참고문헌 (23건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Jin-Gul Joo, In-Seon Jeong, and Seung-Ho Kang, “An Optimal Feature Selection Method to Detect Malwares in Real Time Using Machine Learning,” Journal of Korea Multimedia Society, Vol. 22, No. 2, pp. 203-209, 2019. 미소장
2 M.K. Alzaylaee, S.Y. Yerima, and S. Sezer, “DL-Droid: Deep Learning Based Android Malware Detection Using Real Devices,”Computers and Security, Vol. 89, No. 101663, pp. 1-11, 2020. 미소장
3 Z. Yuan, Y. Lu, Z. Wang, and Y. Xue, “Droid-Sec: Deep Learning in Android Malware Detection,” ACM Special Interest Group on Data Communication Computer Communication Review, Vol. 44, No. 4, pp. 371-372, 2014. 미소장
4 A.Y, Saleh and C. Francis, “A Deep Learning Approach to Malware Detection in Android Platform,” International Journal of Innovative Technology and Exploring Engineering, Vol. 8, No. 8, pp. 1043-1048, 2019. 미소장
5 X. Xiao, S. Zhang, and F. Mercaldo, “Android Malware Detection Based on System Call Sequences and LSTM,” Multimedia Tool and Applications, Vol. 78, No. 4, pp. 3979-3999, 2019. 미소장
6 L. Shiqi, L. Zhiyuan, N. Bo, W. Huanhuan, S. Hua, and Y. Yong, “Android Malware Analysis and Detection Based on Attention-CNNLSTM,”Journal of Computers, Vol. 14, No. 1, pp. 31-43, 2019. 미소장
7 S. Vanjire and M. Lakshmi, “FNN and Auto Encoder Deep Learning-based Algorithm for Android Cyber Security,” International Journal of Recent Technology and Engineering, Vol. 8, No. 5, pp. 3292-3296, 2020. 미소장
8 A. Naway and Y. Li, “Using Deep Neural Network for Android Malware Detection,”International Journal of Advanced Studies in Computer Science and Engineering, Vol. 7, No. 12, pp. 9-18, 2018. 미소장
9 K. Xu, Y. Li, R.H. Deng, and K. Chen, “Deep Refiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks,”Proceeding of IEEE European Symposium on Security and Privacy, pp. 473-487, 2018. 미소장
10 N. McLaughlin, J.M.d. Rincon, B.J. Kang, S. Yerima, P. Miller, and S. Sezer, “Deep Android Malware Detection,” Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, pp. 301-308, 2017. 미소장
11 R. Lu, Malware Detection with LSTM Using Opcode Language, University of Chinese Academy of Sciences, Beijing, 2019. 미소장
12 J. Yan, Y. Qi, and Q. Rao, “Detecting Malware with an Ensemble Method Based on Deep Neural Network,” Security and Communication Networks, Vol. 2018, No. 7247095, pp. 1-16, 2018. 미소장
13 B. Kang, S.Y. Yerima, S. Sezer, and K. McLaughlin, “N-gram Opcode Analysis for Android Malware Detection,” International Journal on Cyber Situational Awareness, Vol. 1, No. 1, pp. 231-255, 2016. 미소장
14 A.I. Elkhawas and N. Abdelbaki, “Malware Detection Using Opcode Trigram Sequence with SVM,” Proceeding of International Conference on Software, Telecommunications and Computer Networks, pp 1-6, 2018. 미소장
15 R. Vinayakumar and K.P. Soman, “Detecting Android Malware Using Long Short-term Memory (LSTM),” Journal of Intelligent and Fuzzy Systems, Vol. 34, No. 3, pp. 1277-1288, 2018. 미소장
16 H. Alimardani and M. Nazeh, “Permissionbased Analysis of Android Applications Using Categorization and Deep Learning Scheme,”Proceeding of MATEC Web of Conferences 2018, pp. 1-7, 2019. 미소장
17 R. Vinayakumar and K. P. Soman, “Deep Android Malware Detection and Classification,” Proceeding of 2017 International Conference on Advances in Computing, Communications and Informatics, pp. 1677-1683, 2017. 미소장
18 J. Yan, Y. Oi, and Q. Rao, “LSTM-based Hierarchical Denoising Network for Android Malware Detection,” Security and Communication Networks, Vol. 2018, No. 5249190, pp. 1-18, 2018. 미소장
19 A. Hota and P. Irolla, “Deep Neural Networks for Android Malware Detection,” Proceedings of the 5th International Conference on Information Systems Security and Privacy -Volume 1: ForSE, pp. 657-663, 2019. 미소장
20 G. Canfora, F. Mercaldo, and C.A. Visaggio, “An HMM and Structural Entropy Based Detector for Android Malware: An Empirical Study,” Computers and Security, Vol. 61, pp. 1-18, 2016. 미소장
21 M.B. Erdene, H. Park, H. Li, H. Lee, and M. S. Cho, “Entropy Analysis to Classify Unknown Packing Algorithms for Malware Detection,” International Journal of Information Security, Vol. 16, No. 3, pp. 227-248, 2017. 미소장
22 A. Bhattacharya and R.T. Goswami, “DMDAM:Data Mining Based Detection of Android Malware,” Proceedings of the First International Conference on Intelligent Computing and Communication, pp. 187-194, 2016. 미소장
23 L. Singh and M. Hofmann, “Dynamic Behavior Analysis of Android Applications for Malware Detection,” Proceeding of International Conference on Intelligent Communication and Computational Techniques, pp. 1-7, 2017. 미소장