TY - GEN
T1 - Deep learning in intrusion detection perspective
T2 - 2017 International Workshop on Big Data and Information Security, WBIS 2017
AU - Kim, Kwangjo
AU - Aminanto, Muhamad Erza
N1 - Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2013-0-00396, Research on Communication Technology using Bio-Inspired Algorithm and 2017-0-00555, Towards Provable-secure Multi-party Authenticated Key Exchange Protocol based on Lattices in a Quantum World).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/29
Y1 - 2018/1/29
N2 - Deep learning techniques are famous due to Its capability to cope with large-scale data these days. They have been investigated within various of applications e.g., language, graphical modeling, speech, audio, image recognition, video, natural language and signal processing areas. In addition, extensive researches applying machine-learning methods in Intrusion Detection System (IDS) have been done in both academia and industry. However, huge data and difficulties to obtain data instances are hot challenges to machine-learning-based IDS. We show some limitations of previous IDSs which uses classic machine learners and introduce feature learning including feature construction, extraction and selection to overcome the challenges. We discuss some distinguished deep learning techniques and its application for IDS purposes. Future research directions using deep learning techniques for IDS purposes are briefly summarized.
AB - Deep learning techniques are famous due to Its capability to cope with large-scale data these days. They have been investigated within various of applications e.g., language, graphical modeling, speech, audio, image recognition, video, natural language and signal processing areas. In addition, extensive researches applying machine-learning methods in Intrusion Detection System (IDS) have been done in both academia and industry. However, huge data and difficulties to obtain data instances are hot challenges to machine-learning-based IDS. We show some limitations of previous IDSs which uses classic machine learners and introduce feature learning including feature construction, extraction and selection to overcome the challenges. We discuss some distinguished deep learning techniques and its application for IDS purposes. Future research directions using deep learning techniques for IDS purposes are briefly summarized.
KW - Artificial Neural Network
KW - Decision Tree
KW - Feature Selection
KW - Intrusion Detection System
KW - Wi-Fi Network
UR - http://www.scopus.com/inward/record.url?scp=85050699839&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2017.8275095
DO - 10.1109/IWBIS.2017.8275095
M3 - Conference contribution
AN - SCOPUS:85050699839
T3 - Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
SP - 5
EP - 10
BT - Proceedings - WBIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 September 2017 through 24 September 2017
ER -