Malaria Analysis

Li, Qigui; Weina, Peter J.; Miller, Fidsa R. Scott

doi:10.1002/9781444398595.ch48

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…For instance, let's consider a simple example where P (x) checks for a potential buffer overflow vulnerability in an IoT device. The logical predicate might take the form: P(x) = MemoryUsage > Threshold (10) "MemoryUsage ¿ Threshold" P(x)="MemoryUsage ¿ Threshold" Here, x represents the telemetry data collected from the device, and "Memo-ryUsage" is one of the variables. The predicate checks if the "MemoryUsage" variable exceeds a predefined "Threshold," which could indicate a possible buffer overflow vulnerability.…”

Section: Logical Predicate Formulationmentioning

confidence: 99%

Reinforcement Learning for Zero-Day Vulnerability Detection in IoT Devices: A Proactive Approach

Reddy,

Shaik,

Gaddam

et al. 2024

Preprint

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Zero - Day vulnerabilities pose a significant threat to the security of IoT devices, as they remain undetected and unpatched by vendors. In this research paper, we propose a novel approach to Zero-Day Vulnerability Detection in IoT devices using reinforcement learning for conjecture generation. Our model leverages real-time telemetry data from IoT devices and metadata about the network to generate potential conjectures about Zero-Day vulnerabilities. The agent is trained with a deep reinforcement learning architecture and benefits from a human-in-the-loop to incorporate domain expertise. We evaluate the performance of the model on a real-world IoT testbed and compare it with existing approaches, demonstrating its proactive and efficient nature in detecting Zero-Day vulnerabilities. Keywords: Zero-Day Vulnerabilities, IoT Devices, Reinforcement Learning, Conjecture Generation Vulnerability Detection

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Section: Logical Predicate Formulationmentioning

confidence: 99%