TY - GEN
T1 - Signature-based and behavior-based attack detection with machine learning for home IoT devices
AU - Visoottiviseth, Vasaka
AU - Sakarin, Pranpariya
AU - Thongwilai, Jetnipat
AU - Choobanjong, Thanakrit
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Currently, Internet of Things (IoT) becomes pervasive and widely deployed. However, the lack of developer and user cyber security awareness leaves IoT devices become the new target of cyber attacks. Therefore, we design and develop "A System for Preventing IoT Device Attacks on Home Wi-Fi Router"(SPIDAR) in order to protect home Wi-Fi networks. This system consists of SPIDAR home Wi-Fi router, SPIDAR Raspberry Pi, and SPIDAR web application to prevent attacks and display the attack statistics to home users. It also helps saving costs from purchasing expensive intrusion prevention software and hardware to install at home. For the prevention method, we provide both the signature-based method using Snort software and the behavior-based method which learns and analyzes IoT devices' behavior by using either the baseline or the machine learning in order to increase the system performance. SPIDAR can prevent five major attack types specified in the OWASP IoT Top 10 vulnerabilities 2018.
AB - Currently, Internet of Things (IoT) becomes pervasive and widely deployed. However, the lack of developer and user cyber security awareness leaves IoT devices become the new target of cyber attacks. Therefore, we design and develop "A System for Preventing IoT Device Attacks on Home Wi-Fi Router"(SPIDAR) in order to protect home Wi-Fi networks. This system consists of SPIDAR home Wi-Fi router, SPIDAR Raspberry Pi, and SPIDAR web application to prevent attacks and display the attack statistics to home users. It also helps saving costs from purchasing expensive intrusion prevention software and hardware to install at home. For the prevention method, we provide both the signature-based method using Snort software and the behavior-based method which learns and analyzes IoT devices' behavior by using either the baseline or the machine learning in order to increase the system performance. SPIDAR can prevent five major attack types specified in the OWASP IoT Top 10 vulnerabilities 2018.
KW - Behavior-based detection
KW - Cyber security
KW - Internet of things
KW - Intrusion prevention system
KW - Smart home
UR - https://www.scopus.com/pages/publications/85098969757
U2 - 10.1109/TENCON50793.2020.9293811
DO - 10.1109/TENCON50793.2020.9293811
M3 - Conference contribution
AN - SCOPUS:85098969757
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 829
EP - 834
BT - 2020 IEEE Region 10 Conference, TENCON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Region 10 Conference, TENCON 2020
Y2 - 16 November 2020 through 19 November 2020
ER -