TY - GEN
T1 - Deep Learning Models for Intrusion Detection in Wi-Fi Networks
T2 - International Conference on Sustainable Design, Engineering, Management, and Sciences, ICSDEMS 2020
AU - Aminanto, Achmad Eriza
AU - Aminanto, Muhamad Erza
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Recently, the number of devices that are connected to the Internet are increasing exponentially due to the rise of the Internet of Things (IoT) era. Despite many advancements of the IoT era, we have been exposed to cyber security threats. Moreover, in this Covid-19 pandemic situation, the trend of cyber crimes is also increasing sharply. In this paper, we discuss one of possible countermeasures to combat cyber threats, namely Intrusion Detection Systems (IDS). IDS usually leverage many different types of machine learning models to detect the unknown attacks. In order to avoid confusion for future researchers in this field, we examine several states of the art papers which leverage deep learning for IDS in Wi-Fi networks. For this purpose, we choose one common Wi-Fi networks dataset, called AWID dataset. By examining the recent studies, we are able to understand current problems of IDS in Wi-Fi networks and able to prepare the best machine learning model for the corresponding problem to achieve a safe environment with minimal risk of cyber threats.
AB - Recently, the number of devices that are connected to the Internet are increasing exponentially due to the rise of the Internet of Things (IoT) era. Despite many advancements of the IoT era, we have been exposed to cyber security threats. Moreover, in this Covid-19 pandemic situation, the trend of cyber crimes is also increasing sharply. In this paper, we discuss one of possible countermeasures to combat cyber threats, namely Intrusion Detection Systems (IDS). IDS usually leverage many different types of machine learning models to detect the unknown attacks. In order to avoid confusion for future researchers in this field, we examine several states of the art papers which leverage deep learning for IDS in Wi-Fi networks. For this purpose, we choose one common Wi-Fi networks dataset, called AWID dataset. By examining the recent studies, we are able to understand current problems of IDS in Wi-Fi networks and able to prepare the best machine learning model for the corresponding problem to achieve a safe environment with minimal risk of cyber threats.
KW - Anomaly detection
KW - AWID dataset
KW - Deep learning
KW - Intrusion detection system
UR - http://www.scopus.com/inward/record.url?scp=85113727786&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-2329-5_14
DO - 10.1007/978-981-16-2329-5_14
M3 - Conference contribution
AN - SCOPUS:85113727786
SN - 9789811623288
T3 - Lecture Notes in Civil Engineering
SP - 115
EP - 121
BT - Sustainable Architecture and Building Environment - Proceedings of ICSDEMS 2020
A2 - Yola, Lin
A2 - Nangkula, Utaberta
A2 - Ayegbusi, Olutobi Gbenga
A2 - Awang, Mokhtar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 December 2020 through 9 December 2020
ER -
