鬼屋:存在受损传感器的物理智能家居事件验证

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2022-04-11 DOI:10.1145/3506859
S. Birnbach, Simon Eberz, I. Martinovic
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引用次数: 6

摘要

在本文中,我们使用来自智能家居传感器集合的数据验证物理事件。这种方法既可以防止事件传感器故障,也可以防止复杂的攻击者。为了验证系统的性能,我们在办公环境中设置了一个“智能家居”。在两周的时间里,我们使用48个传感器识别了22种事件类型。使用来自物理传感器的数据,我们验证由事件传感器提供的事件流,以检测屏蔽和欺骗攻击。我们考虑了三种威胁模型:零努力攻击者、机会攻击者和可以任意修改实时传感器数据的传感器妥协攻击者。对于欺骗事件,我们对22个事件中的9个事件实现了完美的分类,并且在15个事件的检测率超过99.9%的情况下实现了0%的虚警率。对于11个事件,大多数屏蔽攻击可以被检测到而不会引起任何假警报。我们还表明,即使是强大的机会主义攻击者也天生局限于欺骗少数选定的事件,并且这样做需要很长的等待时间。最后,我们展示了单一分类器系统对受损传感器数据的脆弱性,并介绍了一种基于传感器融合的更安全的方法。
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Haunted House: Physical Smart Home Event Verification in the Presence of Compromised Sensors
In this article, we verify physical events using data from an ensemble of smart home sensors. This approach both protects against event sensor faults and sophisticated attackers. To validate our system’s performance, we set up a “smart home” in an office environment. We recognize 22 event types using 48 sensors over the course of two weeks. Using data from the physical sensors, we verify the event stream supplied by the event sensors to detect both masking and spoofing attacks. We consider three threat models: a zero-effort attacker, an opportunistic attacker, and a sensor-compromise attacker who can arbitrarily modify live sensor data. For spoofed events, we achieve perfect classification for 9 out of 22 events and achieve a 0% false alarm rate at a detection rate exceeding 99.9% for 15 events. For 11 events the majority of masking attacks can be detected without causing any false alarms. We also show that even a strong opportunistic attacker is inherently limited to spoofing few select events and that doing so involves lengthy waiting periods. Finally, we demonstrate the vulnerability of a single-classifier system to compromised sensor data and introduce a more secure approach based on sensor fusion.
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来源期刊
CiteScore
5.20
自引率
3.70%
发文量
0
期刊最新文献
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