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Enabling Auditing and Intrusion Detection for Proprietary Controller Area Networks.
| 题名： | Enabling Auditing and Intrusion Detection for Proprietary Controller Area Networks. |
| 作者： | Stone, B. J. |
| 关键词： | Cybersecurity, Reverse engineering, Machine learning, Artificial intelligence, Intrusion detection, Cyberattacks, Application protocols, Predictive modeling, Automata theory, Cyber-physical systems, Controller area network, Industrial control systems |
| 摘要： | The goal of this dissertation is to provide automated methods for security researchers to overcome `security through obscurity' used by manufacturers of proprietary Industrial Control Systems (ICS). `White hat' security analysts waste significant time reverse engineering these systems' opaque network configurations instead of performing meaningful security auditing tasks. Automating the process of documenting proprietary protocol configurations is intended to improve independent security auditing of ICS networks. The major contributions of this dissertation are a novel approach for unsupervised lexical analysis of binary network data flows and analysis of the time series data extracted as a result. We demonstrate the utility of these methods using Controller Area Network (CAN) data sampled from passenger vehicles. |
| 报告类型： | 科技报告 |
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