A Binary Classifier-Based Wire Resistance Attack on the KLJN Secure Key Exchanger

The statistical fluctuations of the mean-square noise voltages measured at Alice’s and Bob’s ends in the KLJN scheme are used to implement a binary classifier for a new type of wire-resistance-based attack. The data are plotted on a two-dimensional graph, where the x- and y-axes represent the mean-square voltages at Alice’s and Bob’s ends, respectively. When the wire resistance is non-zero, the data form distinct lines for the LH and HL cases, allowing Eve to extract the secure bits with nearly 100% success. Further analysis shows that swapping the x and y axes for the LH data reproduces the curve for the HL case, effectively reducing the number of independent measurements by half. These results suggest that machine learning tools could exploit this property for enhanced detection performance, although such methods are unnecessary here since the LH and HL cases are completely separable. The only effective defense against this attack remains the traditional approach: properly increase the noise temperature on the side with lower resistance, or equivalently, scale down the noise temperature on the higher-resistance side. All claims are confirmed through computer simulations.