SaTC: CORE: Small: Towards Secure and Reliable Network Tomography in Wireline and Wireless Networks

SaTC：核心：小型：在有线和无线网络中实现安全可靠的网络层析成像

• 批准号: 1717969
• 负责人: Zhuo Lu
• 金额: $ 40万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717969&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Towards; Secure

Today's networks, such as the Internet, cellular networks, and the Internet of Things, provide ubiquitous wired or wireless connections over large areas. Secure and reliable operations are among the most important objectives in these networks. Network tomography has become a promising framework for accurate monitoring of network operation status, which is vital to ensure an efficient and reliable network environment. However, this measurement process can be exploited by malicious attackers to generate falsified, misleading measurement or monitoring results, which significantly affects follow-on network operations based on these results, and accordingly degrades the operational reliability and health of today's networks. The goal of this project is to analyze security vulnerabilities, understand potential security attack strategies and their impact, and design effective defense mechanisms against such attacks. The research in this project will extend the technical knowledge of security vulnerabilities, attacks and defense during network measurement. The project will also offer undergraduate students opportunities to gain research experiences in network measurement and security. The project focuses on investigating a new class of security attacks, called measurement integrity attacks, during the measurement process in network tomography. Unlike conventional data integrity problems that can be usually protected by standard security methods (e.g., encryption and authentication), a key challenge in measurement integrity attacks is that the network event data (e.g., packet transmission/delivery timings and loss) during measurement is not protected by standard security methods, but can be easily manipulated by an inside attacker. This project aims at (i) creating the theoretical foundation to analyze the feasibility and impact of measurement integrity attacks with different attack strategies in wireline and wireless scenarios, (ii) designing robust attack detection and localization methods, and analyzing the effectiveness of such countermeasures, (iii) investigating efficient management methods for network measurement to increase the attack resilience in a network, and (iv) conducting comprehensive evaluation of the efficiency and effectiveness of attack countermeasures in both wireline and wireless networks.

今天的网络，如互联网、蜂窝网络和物联网，在大范围内提供无处不在的有线或无线连接。安全可靠的运行是这些网络中最重要的目标之一。网络层析成像已成为一种很有前景的网络运行状态准确监测框架，对确保高效可靠的网络环境至关重要。然而，恶意攻击者可能会利用此测量过程生成伪造的、误导性的测量或监控结果，从而严重影响基于这些结果的后续网络操作，并相应地降低当今网络的运行可靠性和健康状况。该项目的目标是分析安全漏洞，了解潜在的安全攻击策略及其影响，并针对此类攻击设计有效的防御机制。本项目的研究将扩展网络测量过程中安全漏洞、攻击与防御的技术知识。该项目还将为本科生提供获得网络测量和安全研究经验的机会。该项目重点研究在网络断层扫描测量过程中的一种新型安全攻击，称为测量完整性攻击。与通常可以通过标准安全方法（例如，加密和身份验证）保护的传统数据完整性问题不同，测量完整性攻击的一个关键挑战是，测量期间的网络事件数据（例如，数据包传输/交付时间和丢失）不受标准安全方法的保护，但很容易被内部攻击者操纵。本项目旨在(i)建立理论基础，分析有线和无线场景下不同攻击策略下测量完整性攻击的可行性和影响；（ii）设计稳健的攻击检测和定位方法，并分析这些对策的有效性；（iii）研究有效的网络测量管理方法，以提高网络的攻击弹性。（iv）全面评估有线及无线网络攻击对抗措施的效率和成效。

项目成果

ProTO: Proactive Topology Obfuscation Against Adversarial Network Topology Inference

• DOI: 10.1109/infocom41043.2020.9155255
• 发表时间: 2020-07
• 期刊: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
• 作者: Tao Hou;Zhe Qu;Tao Wang;Zhuo Lu;Yao Liu

Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic

• DOI: 10.1109/globalsip45357.2019.8969428
• 发表时间: 2019-11
• 期刊: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
• 作者: Tao Hou;Tao Wang;Zhuo Lu;Yao Liu

Orthogonality-Sabotaging Attacks against OFDMA-based Wireless Networks

• DOI: 10.1109/infocom.2019.8737389
• 发表时间: 2019-04
• 期刊: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
• 作者: Shangqing Zhao;Zhuo Lu;Z. Luo;Yao Liu

No Training Hurdles: Fast Training-Agnostic Attacks to Infer Your Typing

• DOI: 10.1145/3243734.3243755
• 发表时间: 2018-10
• 期刊: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Song Fang;Ian D. Markwood;Yao Liu;Shangqing Zhao;Zhuo Lu;Haojin Zhu

Measurement Integrity Attacks Against Network Tomography: Feasibility and Defense

• DOI: 10.1109/tdsc.2019.2958934
• 发表时间: 2021-11
• 期刊: IEEE Transactions on Dependable and Secure Computing
• 影响因子: 7.3
• 作者: Shangqing Zhao;Zhuo Lu;Cliff X. Wang