TY - GEN
T1 - Detection of threats to IoT devices using scalable VPN-forwarded honeypots
AU - Tambe, Amit
AU - Aung, Yan Lin
AU - Sridharan, Ragav
AU - Ochoa, Martín
AU - Tippenhauer, Nils Ole
AU - Shabtai, Asaf
AU - Elovici, Yuval
N1 - Publisher Copyright: © 2019 Copyright held by the owner/author(s).
PY - 2019/3/13
Y1 - 2019/3/13
N2 - Attacks on Internet of Things (IoT) devices, exploiting inherent vulnerabilities, have intensified over the last few years. Recent large-scale attacks, such as Persirai, Hakai, etc. corroborate concerns about the security of IoT devices. In this work, we propose an approach that allows easy integration of commercial off-the-shelf IoT devices into a general honeypot architecture. Our approach projects a small number of heterogeneous IoT devices (that are physically at one location) as many (geographically distributed) devices on the Internet, using connections to commercial and private VPN services. The goal is for those devices to be discovered and exploited by attacks on the Internet, thereby revealing unknown vulnerabilities. For detection and examination of potentially malicious traffic, we devise two analysis strategies: (1) given an outbound connection from honeypot, backtrack into network traffic to detect the corresponding attack command that caused the malicious connection and use it to download malware, (2) perform live detection of unseen URLs from HTTP requests using adaptive clustering. We show that our implementation and analysis strategies are able to detect recent large-scale attacks targeting IoT devices (IoT Reaper, Hakai, etc.) with overall low cost and maintenance effort.
AB - Attacks on Internet of Things (IoT) devices, exploiting inherent vulnerabilities, have intensified over the last few years. Recent large-scale attacks, such as Persirai, Hakai, etc. corroborate concerns about the security of IoT devices. In this work, we propose an approach that allows easy integration of commercial off-the-shelf IoT devices into a general honeypot architecture. Our approach projects a small number of heterogeneous IoT devices (that are physically at one location) as many (geographically distributed) devices on the Internet, using connections to commercial and private VPN services. The goal is for those devices to be discovered and exploited by attacks on the Internet, thereby revealing unknown vulnerabilities. For detection and examination of potentially malicious traffic, we devise two analysis strategies: (1) given an outbound connection from honeypot, backtrack into network traffic to detect the corresponding attack command that caused the malicious connection and use it to download malware, (2) perform live detection of unseen URLs from HTTP requests using adaptive clustering. We show that our implementation and analysis strategies are able to detect recent large-scale attacks targeting IoT devices (IoT Reaper, Hakai, etc.) with overall low cost and maintenance effort.
KW - Adaptive clustering
KW - Attack attribution
KW - High-interaction IoT honeypot
KW - Intrusion detection
KW - Network traffic analysis
UR - http://www.scopus.com/inward/record.url?scp=85063863449&partnerID=8YFLogxK
U2 - 10.1145/3292006.3300024
DO - 10.1145/3292006.3300024
M3 - Conference contribution
T3 - CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy
SP - 85
EP - 96
BT - CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy
T2 - 9th ACM Conference on Data and Application Security and Privacy, CODASPY 2019
Y2 - 25 March 2019 through 27 March 2019
ER -