Over the years, honeypots emerged as an important security tool to understand attacker intent and deceive attackers to spend time and resources. Recently, honeypots are being deployed for Internet of things (IoT) devices to lure attackers, and learn their behavior. However, most of the existing IoT honeypots, even the high interaction ones, are easily detected by an attacker who can observe honeypot traffic due to lack of real network traffic originating from the honeypot. This implies that, to build better honeypots and enhance cyber deception capabilities, IoT honeypots need to generate realistic network traffic flows. To achieve this goal, we propose a novel deep learning based approach for generating traffic flows that mimic real network traffic due to user and IoT device interactions.A key technical challenge that our approach overcomes is scarcity of device-specific IoT traffic data to effectively train a generator.We address this challenge by leveraging a core generative adversarial learning algorithm for sequences along with domain specific knowledge common to IoT devices.Through an extensive experimental evaluation with 18 IoT devices, we demonstrate that the proposed synthetic IoT traffic generation tool significantly outperforms state of the art sequence and packet generators in remaining indistinguishable from real traffic even to an adaptive attacker.
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Generating Stateful Policies for IoT Device Security with Cross-Device Sensors
The security of Internet-of-Things (IoT) devices in the residential environment is important due to their widespread presence in homes and their sensing and actuation capabilities. However, securing IoT devices is challenging due to their varied designs, deployment longevity, multiple manufacturers, and potentially limited availability of long-term firmware updates. Attackers have exploited this complexity by specifically targeting IoT devices, with some recent high-profile cases affecting millions of devices.
In this work, we explore access control mechanisms that tightly constrain access to devices at the residential router, with the goal of precluding access that is inconsistent with legitimate users' goals. Since many residential IoT devices are controlled via applications on smartphones, we combine application sensors on phones with sensors at residential routers to analyze workflows. We construct stateful filters at residential routers that can require user actions within a registered smartphone to enable network access to an IoT device. In doing so, we constrain network packets only to those that are consistent with the user's actions. In our experiments, we successfully identified 100% of malicious traffic while correctly allowing more than 98% of legitimate network traffic. The approach works across device types and manufacturers with straightforward API and state machine construction for each new device workflow.
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- Award ID(s):
- 1651540
- NSF-PAR ID:
- 10431058
- Date Published:
- Journal Name:
- International Conference on Network of the Future (NoF)
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NoF55974.2022.9942547
