论文标题
通过交换性神经网络在物理层中欺骗攻击检测
Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks
论文作者
论文摘要
在欺骗攻击中，攻击者冒充合法用户访问或篡改合法用户的数据。在无线通信系统中，可以通过依靠通道和发射器收音机的功能来检测这些攻击。在这种情况下，一种流行的方法是利用收到的信号强度（RSS）在多个接收机或访问点相对于发射机的空间位置的依赖。现有方案依赖于长期估计，这使得很难区分欺骗与合法用户的运动。这里通过一个深层神经网络来解决此限制，该网络隐含地了解了短期RSS矢量估计对的分布。所采用的网络体系结构将决策问题表现出的输入（交换性）的排列施加了不变性。所提出的算法的优点在我们收集的数据集上得到了证实。
In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user. In wireless communication systems, these attacks may be detected by relying on features of the channel and transmitter radios. In this context, a popular approach is to exploit the dependence of the received signal strength (RSS) at multiple receivers or access points with respect to the spatial location of the transmitter. Existing schemes rely on long-term estimates, which makes it difficult to distinguish spoofing from movement of a legitimate user. This limitation is here addressed by means of a deep neural network that implicitly learns the distribution of pairs of short-term RSS vector estimates. The adopted network architecture imposes the invariance to permutations of the input (commutativity) that the decision problem exhibits. The merits of the proposed algorithm are corroborated on a data set that we collected.
