The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pembelajaran mendalam mendapat lebih banyak tarikan dan prestasi yang lebih baik dalam melaksanakan Sistem Pengesanan Pencerobohan (IDS), terutamanya untuk pembelajaran ciri. Kertas kerja ini membentangkan kemajuan dan cabaran terkini dalam IDS menggunakan model pembelajaran mendalam, yang telah mencapai peningkatan prestasi besar dalam bidang penglihatan komputer, pemprosesan bahasa semula jadi dan pemprosesan imej/audio berbanding kaedah tradisional. Selepas memberikan penerangan yang sistematik dan berkaedah tentang perkembangan terkini dalam pembelajaran mendalam dari sudut seni bina dan teknik yang digunakan, kami mencadangkan kebaikan dan keburukan semua IDS berasaskan pembelajaran mendalam, dan membincangkan kepentingan model pembelajaran mendalam sebagai pendekatan pembelajaran ciri. Untuk ini, penulis telah mencadangkan konsep Deep-Feature Extraction and Selection (D-FES). Dengan menggabungkan pengekstrakan ciri bertindan dan pemilihan ciri berwajaran untuk D-FES, percubaan kami telah disahkan untuk mendapatkan prestasi terbaik bagi kadar pengesanan, 99.918% dan kadar penggera palsu, 0.012% untuk mengesan serangan penyamaran dalam rangkaian Wi-Fi yang boleh dicapai dengan lebih baik daripada penerbitan sebelumnya. Ringkasan dan cabaran selanjutnya dicadangkan sebagai kata penutup.
Kwangjo KIM
Korea Advanced Institute of Science and Technology (KAIST)
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Salinan
Kwangjo KIM, "Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 7, pp. 1433-1447, July 2020, doi: 10.1587/transinf.2019ICI0001.
Abstract: Deep learning is gaining more and more lots of attractions and better performance in implementing the Intrusion Detection System (IDS), especially for feature learning. This paper presents the state-of-the-art advances and challenges in IDS using deep learning models, which have been achieved the big performance enhancements in the field of computer vision, natural language processing, and image/audio processing than the traditional methods. After providing a systematic and methodical description of the latest developments in deep learning from the points of the deployed architectures and techniques, we suggest the pros-and-cons of all the deep learning-based IDS, and discuss the importance of deep learning models as feature learning approach. For this, the author has suggested the concept of the Deep-Feature Extraction and Selection (D-FES). By combining the stacked feature extraction and the weighted feature selection for D-FES, our experiment was verified to get the best performance of detection rate, 99.918% and false alarm rate, 0.012% to detect the impersonation attacks in Wi-Fi network which can be achieved better than the previous publications. Summary and further challenges are suggested as a concluding remark.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019ICI0001/_p
Salinan
@ARTICLE{e103-d_7_1433,
author={Kwangjo KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network},
year={2020},
volume={E103-D},
number={7},
pages={1433-1447},
abstract={Deep learning is gaining more and more lots of attractions and better performance in implementing the Intrusion Detection System (IDS), especially for feature learning. This paper presents the state-of-the-art advances and challenges in IDS using deep learning models, which have been achieved the big performance enhancements in the field of computer vision, natural language processing, and image/audio processing than the traditional methods. After providing a systematic and methodical description of the latest developments in deep learning from the points of the deployed architectures and techniques, we suggest the pros-and-cons of all the deep learning-based IDS, and discuss the importance of deep learning models as feature learning approach. For this, the author has suggested the concept of the Deep-Feature Extraction and Selection (D-FES). By combining the stacked feature extraction and the weighted feature selection for D-FES, our experiment was verified to get the best performance of detection rate, 99.918% and false alarm rate, 0.012% to detect the impersonation attacks in Wi-Fi network which can be achieved better than the previous publications. Summary and further challenges are suggested as a concluding remark.},
keywords={},
doi={10.1587/transinf.2019ICI0001},
ISSN={1745-1361},
month={July},}
Salinan
TY - JOUR
TI - Intrusion Detection System Using Deep Learning and Its Application to Wi-Fi Network
T2 - IEICE TRANSACTIONS on Information
SP - 1433
EP - 1447
AU - Kwangjo KIM
PY - 2020
DO - 10.1587/transinf.2019ICI0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2020
AB - Deep learning is gaining more and more lots of attractions and better performance in implementing the Intrusion Detection System (IDS), especially for feature learning. This paper presents the state-of-the-art advances and challenges in IDS using deep learning models, which have been achieved the big performance enhancements in the field of computer vision, natural language processing, and image/audio processing than the traditional methods. After providing a systematic and methodical description of the latest developments in deep learning from the points of the deployed architectures and techniques, we suggest the pros-and-cons of all the deep learning-based IDS, and discuss the importance of deep learning models as feature learning approach. For this, the author has suggested the concept of the Deep-Feature Extraction and Selection (D-FES). By combining the stacked feature extraction and the weighted feature selection for D-FES, our experiment was verified to get the best performance of detection rate, 99.918% and false alarm rate, 0.012% to detect the impersonation attacks in Wi-Fi network which can be achieved better than the previous publications. Summary and further challenges are suggested as a concluding remark.
ER -