标题： 基于机器学习和深度学习的GNSS/GPS欺骗和干扰识别 显示英文标题

标题： GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning

摘要： 随着对全球导航卫星系统（GNSS）的依赖不断增加，特别是全球定位系统（GPS），迫切需要保护这些技术免受欺骗和干扰等恶意威胁。 作为各种应用中定位、导航和定时（PNT）的支柱，包括交通、电信和应急服务，GNSS容易受到故意干扰，这会带来重大风险。 欺骗攻击涉及传输伪造的GNSS信号，使接收器计算出错误的位置，可能导致严重后果，从民用航空中的导航错误到军事行动中的安全漏洞。 此外，GNSS系统内部缺乏固有的安全措施，使其成为对手的诱人目标。 虽然GNSS/GPS干扰和欺骗系统由许多组件组成，但能够区分真实信号和恶意信号对于维护系统完整性至关重要。 机器学习和深度学习的最新进展为增强对这些威胁的检测和缓解策略提供了有希望的途径。 本文通过机器学习、深度学习和计算机视觉技术解决欺骗和干扰问题，应对现实世界中的挑战。 通过对两个与欺骗和干扰检测相关的现实数据集使用先进算法进行广泛实验，我们取得了最先进的结果。 在GNSS/GPS干扰检测任务中，我们达到了约99%的准确率，相比之前的研究性能提高了约5%。 此外，我们还解决了与欺骗检测相关的一个具有挑战性的任务，所得结果突显了机器学习和深度学习在这一领域的潜力。 显示英文摘要

摘要： The increasing reliance on Global Navigation Satellite Systems (GNSS), particularly the Global Positioning System (GPS), underscores the urgent need to safeguard these technologies against malicious threats such as spoofing and jamming. As the backbone for positioning, navigation, and timing (PNT) across various applications including transportation, telecommunications, and emergency services GNSS is vulnerable to deliberate interference that poses significant risks. Spoofing attacks, which involve transmitting counterfeit GNSS signals to mislead receivers into calculating incorrect positions, can result in serious consequences, from navigational errors in civilian aviation to security breaches in military operations. Furthermore, the lack of inherent security measures within GNSS systems makes them attractive targets for adversaries. While GNSS/GPS jamming and spoofing systems consist of numerous components, the ability to distinguish authentic signals from malicious ones is essential for maintaining system integrity. Recent advancements in machine learning and deep learning provide promising avenues for enhancing detection and mitigation strategies against these threats. This paper addresses both spoofing and jamming by tackling real-world challenges through machine learning, deep learning, and computer vision techniques. Through extensive experiments on two real-world datasets related to spoofing and jamming detection using advanced algorithms, we achieved state of the art results. In the GNSS/GPS jamming detection task, we attained approximately 99% accuracy, improving performance by around 5% compared to previous studies. Additionally, we addressed a challenging tasks related to spoofing detection, yielding results that underscore the potential of machine learning and deep learning in this domain.