论文标题
对抗机器学习的基于对车辆到微网络服务的威胁的期望
Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services
论文作者
论文摘要
在本文中，我们研究了对抗机器学习（AML）的扩大攻击表面以及对车辆到感官（V2M）服务的潜在攻击。我们提出了一项对多阶段灰色盒子攻击的预期研究，该研究可以与白盒攻击达到可比的结果。对手旨在欺骗网络边缘的目标机器学习（ML）分类器，以错误地分类来自微电网的传入能量请求。通过推理攻击，对手可以从智能微电网和5G GNODEB之间的通信中收集实时数据，以训练边缘目标分类器的代理（即阴影）模型。为了预测对手收集实时数据实例能力的相关影响，我们研究了五种不同的情况，每个案例代表了对手收集的不同数量的实时数据实例。在完整数据集中训练的六个ML模型中，K-Nearest邻居（K-NN）被选为替代模型，通过模拟，我们证明了多阶段的灰色盒攻击能够误导ML分类器，并导致逃避率（EIR）最高73.2％，最多使用40％的数据来实现较低的数据攻击，以实现相似的eir。
In this paper, we study the expanding attack surface of Adversarial Machine Learning (AML) and the potential attacks against Vehicle-to-Microgrid (V2M) services. We present an anticipatory study of a multi-stage gray-box attack that can achieve a comparable result to a white-box attack. Adversaries aim to deceive the targeted Machine Learning (ML) classifier at the network edge to misclassify the incoming energy requests from microgrids. With an inference attack, an adversary can collect real-time data from the communication between smart microgrids and a 5G gNodeB to train a surrogate (i.e., shadow) model of the targeted classifier at the edge. To anticipate the associated impact of an adversary's capability to collect real-time data instances, we study five different cases, each representing different amounts of real-time data instances collected by an adversary. Out of six ML models trained on the complete dataset, K-Nearest Neighbour (K-NN) is selected as the surrogate model, and through simulations, we demonstrate that the multi-stage gray-box attack is able to mislead the ML classifier and cause an Evasion Increase Rate (EIR) up to 73.2% using 40% less data than what a white-box attack needs to achieve a similar EIR.
