IoT Network Security from the Perspective of Adversarial Deep Learning
IoT Network Security from the Perspective of Adversarial Deep Learning
复制标题
对抗性深度学习视角下的物联网网络安全
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Erpek
中科院分区：
文献类型：
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作者：
Y. Sagduyu;Yi Shi;T. Erpek
机器学习在物联网（IoT）网络中有着丰富的应用，如信息检索、流量管理、频谱感知和信号认证。虽然人们对理解机器学习的安全问题的兴趣激增，但它们对无线应用的影响尚未被理解，例如物联网系统中的无线应用，由于无线通信的开放和广播性质，这些应用容易受到各种攻击。为了支持具有不同优先级的异构设备的物联网系统，我们提出了基于对抗机器学习的新技术，并将其应用于三种类型的空中（OTA）无线攻击，即干扰方面的拒绝服务（DoS）攻击，频谱中毒攻击和优先级违规攻击。通过观察频谱，攻击者首先进行探索性攻击，通过构建预测传输结果的深度神经网络分类器来推断物联网发射机的信道接入算法。基于这些预测结果，无线攻击继续通过空中干扰数据传输或操纵感测结果（通过在感测阶段期间进行发送），以欺骗发送器在测试阶段做出错误的发送决策（对应于规避攻击）。当物联网发射机收集感测结果作为训练数据以重新训练其信道接入算法时，对手会发起因果攻击，以通过空中操纵发射机的输入数据。我们发现，这些具有不同能量消耗和隐蔽性的攻击会导致物联网系统无线通信的吞吐量和成功率显著下降。然后，我们引入了一个防御机制，系统地增加了对手的不确定性在推理阶段，提高了性能。研究结果为如何使用深度学习攻击和防御物联网网络提供了新的见解。
Machine learning finds rich applications in Internet of Things (IoT) networks such as information retrieval, traffic management, spectrum sensing, and signal authentication. While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT systems that are susceptible to various attacks due the open and broadcast nature of wireless communications. To support IoT systems with heterogeneous devices of different priorities, we present new techniques built upon adversarial machine learning and apply them to three types of over-the-air (OTA) wireless attacks, namely denial of service (DoS) attack in terms of jamming, spectrum poisoning attack, and priority violation attack. By observing the spectrum, the adversary starts with an exploratory attack to infer the channel access algorithm of an IoT transmitter by building a deep neural network classifier that predicts the transmission outcomes. Based on these prediction results, the wireless attack continues to either jam data transmissions or manipulate sensing results over the air (by transmitting during the sensing phase) to fool the transmitter into making wrong transmit decisions in the test phase (corresponding to an evasion attack). When the IoT transmitter collects sensing results as training data to retrain its channel access algorithm, the adversary launches a causative attack to manipulate the input data to the transmitter over the air. We show that these attacks with different levels of energy consumption and stealthiness lead to significant loss in throughput and success ratio in wireless communications for IoT systems. Then we introduce a defense mechanism that systematically increases the uncertainty of the adversary at the inference stage and improves the performance. Results provide new insights on how to attack and defend IoT networks using deep learning.