Real-Time Threat Intelligence Integration for Cybersecurity in Autonomous Vehicles - A Deep Learning Framework: Integrates real-time threat intelligence into cybersecurity systems for AVs using a deep learning framework
Keywords:
Indicators of Compromise, Convolutional Neural Networks, Threat Indicators,Abstract
The increasing dependence of Autonomous Vehicles (AVs) on complex software and network connectivity makes them vulnerable to cyberattacks. These attacks can potentially compromise control systems, leading to safety hazards and disruption of critical infrastructure. Real-time Threat Intelligence (RTTI) plays a crucial role in mitigating these risks by providing up-to-date information about emerging threats and vulnerabilities. This research paper proposes a deep learning framework for integrating RTTI into the cybersecurity systems of AVs.
The paper begins by outlining the cybersecurity challenges faced by AVs. The interconnected nature of AVs, with various sensors, communication modules, and control systems, creates a vast attack surface for malicious actors. Traditional signature-based intrusion detection systems struggle to keep pace with the evolving threat landscape.
This paper then explores the concept of RTTI and its benefits for AV cybersecurity. RTTI provides continuous insights into ongoing cyber threats, including attack vectors, vulnerabilities, and indicators of compromise (IOCs). By integrating RTTI with AV systems, we can proactively identify and respond to potential attacks, minimizing the risk of successful exploits.
The core of the paper presents a deep learning framework designed for real-time threat detection in AVs. The framework leverages the strengths of deep learning architectures, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to analyze data streams from various AV sensors and network traffic.
The RTTI integration is achieved by incorporating threat indicators and attack vectors from external feeds into the training process of both anomaly and intrusion detection models. This allows the framework to adapt to new threats in real-time, enhancing its effectiveness.
The paper then discusses the evaluation methodology for the proposed framework. This includes defining performance metrics for anomaly and intrusion detection, followed by training and testing the model on realistic datasets that simulate AV sensor data and network traffic.
Finally, the paper presents the results of the evaluation, analyzing the framework's accuracy, precision, and recall in detecting various attack scenarios. The paper also discusses the limitations of the proposed framework and potential areas for future research.
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