3,026 research outputs found
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks
IoT application domains, device diversity and connectivity are rapidly
growing. IoT devices control various functions in smart homes and buildings,
smart cities, and smart factories, making these devices an attractive target
for attackers. On the other hand, the large variability of different
application scenarios and inherent heterogeneity of devices make it very
challenging to reliably detect abnormal IoT device behaviors and distinguish
these from benign behaviors. Existing approaches for detecting attacks are
mostly limited to attacks directly compromising individual IoT devices, or,
require predefined detection policies. They cannot detect attacks that utilize
the control plane of the IoT system to trigger actions in an
unintended/malicious context, e.g., opening a smart lock while the smart home
residents are absent.
In this paper, we tackle this problem and propose ARGUS, the first
self-learning intrusion detection system for detecting contextual attacks on
IoT environments, in which the attacker maliciously invokes IoT device actions
to reach its goals. ARGUS monitors the contextual setting based on the state
and actions of IoT devices in the environment. An unsupervised Deep Neural
Network (DNN) is used for modeling the typical contextual device behavior and
detecting actions taking place in abnormal contextual settings. This
unsupervised approach ensures that ARGUS is not restricted to detecting
previously known attacks but is also able to detect new attacks. We evaluated
ARGUS on heterogeneous real-world smart-home settings and achieve at least an
F1-Score of 99.64% for each setup, with a false positive rate (FPR) of at most
0.03%.Comment: To appear in the 32nd USENIX Security Symposium, August 2022, Anaheim
CA, US
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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