A Statistical Signal Processing Framework for Secure Wireless and Sensor Networking

无线网络，尤其是传感器网络，很容易受到入侵和攻击。即使是最强的加密和身份验证技术也不足以提供安全性和信任。由于信号在共享媒体中传播，因此传输行为本身就揭示了网络的关键方面。无线传感器网络面临的更大挑战是，传感器在物理上并不安全；然后，对手可以完全控制一小部分传感器，并对其进行编程，从内部进行攻击。本研究探讨安全无线与感测器网路的理论与实务技术。统计信号处理和机器学习技术的发展，以提取网络信息。利用网络编码和随机混合技术开发了针对流量分析的对策。从理论上描述了稳定吞吐量和保密性（具有延迟约束）之间的权衡。对于执行信号处理任务的传感器网络，研究了使用拜占庭传感器的最优攻击策略。利用鲁棒和通用的检测和估计技术开发对策。协作传感器网络策略也被研究以对抗拜占庭攻击。

Wireless networks, sensor networks in particular, are vulnerable to intrusion and attack. Even the strongest encryption and authentication techniques are not adequate to provide security and trust. Because signals propagate in shared media, the very acts of transmission reveal crucial aspects of networking. More challenging for wireless sensor networks is that sensors are not physically secure; An adversary can then gain full control of a fraction of the sensors and program them to attack from within.This research investigates the theory and practical techniques of secure wireless and sensor networking. Statistical signal processing and machine learning techniques are developed to extract networking information. Countermeasures against traffic analysis are developed using network coding and randomized mixing techniques. Tradeoffs between stable throughput and secrecy (with delay constraints) are characterized theoretically. For sensor networks performing signal processing tasks, optimal attacking strategies using Byzantine sensors are investigated. Countermeasures are developed using robust and universal detection and estimation techniques. Collaborative sensor networking strategies are also investigated to counter Byzantine attacks.