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 nonzero, 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.
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 nonzero, 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.
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.
It is shown that narrowing the difference between the high and low resistor values in the Kirchhoff Law-Johnson Noise (KLJN) key exchange strongly affects security against a recently introduced binary classifier-based wire resistance attack. Using time domain simulations of a non-ideal KLJN loop with finite cable resistance, we generate large ensembles of secure (HL/LH) bits and evaluate the mean-square noise voltages at Alice's and Bob's ends. For each bit, these mean-square values form a point in a two-dimensional classifier plane, where the separation between the HL and LH point clouds characterizes the information available to an eavesdropper (Eve). We quantify Eve's success probability p by a simple decision rule based on the sign of the difference between the measured mean-square voltages. For strongly asymmetric resistors (for example RL = 4 kOhm and RH = 10 kOhm) and realistic wire resistances, the HL and LH clouds are fully separable and Eve's p approaches 1, which confirms that the classifier attack can practically recover all secure bits. As the low resistor value approaches the high one (for example RL = 9 kOhm and RH = 10 kOhm) at the same cable resistance, the HL and LH clouds increasingly overlap, and the measured p drops close to 0.7, approaching the ideal limit p = 0.5 as RL approaches RH. A surprising phenomenon is that, in this classifier-based scenario, increasing the wire resistance can decrease the information leak. This counterintuitive effect is strikingly the opposite of the behavior in the classical Bergou-Scheuer-Yariv wire resistance attack, where the mean-square voltages at the two ends of the wire are simply compared.
It is shown that narrowing the difference between the high and low resistor values in the Kirchhoff Law–Johnson Noise (KLJN) key exchange strongly affects security against a recently introduced binary classifier-based wire resistance attack. Using time domain simulations of a non-ideal KLJN loop with finite cable resistance, we generate large ensembles of secure (HL/LH) bits and evaluate the mean-square noise voltages at Alice’s and Bob’s ends. For each bit, these mean-square values form a point in a two-dimensional classifier plane, where the separation between the HL and LH point clouds characterizes the information available to an eavesdropper (Eve). We quantify Eve’s success probability p by a simple decision rule based on the sign of the difference between the measured mean-square voltages. For strongly asymmetric resistors (for example [Formula: see text] kOhm and [Formula: see text] kOhm) and realistic wire resistances, the HL and LH clouds are fully separable and Eve’s p approaches 1, which confirms that the classifier attack can practically recover all secure bits. As the low resistor value approaches the high one (for example [Formula: see text] kOhm and [Formula: see text] kOhm) at the same cable resistance, the HL and LH clouds increasingly overlap and the measured p drops close to 0.7, approaching the ideal limit [Formula: see text] as [Formula: see text] approaches [Formula: see text]. A surprising phenomenon is that, in this classifier-based scenario, increasing the wire resistance can decrease the information leak. This counterintuitive effect is strikingly the opposite of the behavior in the classical Bergou–Scheuer–Yariv wire resistance attack, where the mean-square voltages at the two ends of the wire are simply compared.
A new attack against the Kirchhoff-Law-Johnson-Noise (KLJN) key distribution system is introduced. The attack is based on 1) Utilizing the dc-voltage-source - which we put at Alice’s end in our mathematical modeling of the attack-that could exist due to errors, imbalances, Electromagnetic interference and etc.2) On studying the number of samples per bit in the security key that the measured Alice/Bob voltages exceeds or falls below a threshold voltage, respectively. The threshold voltage is the average between dc voltages across low and high resistors- generated by a dc-voltage source. We count the number of samples the voltage at Bob’s end (containing both the noise and dc components) exceeds the threshold voltage and how many times it falls below the threshold to judge whether the resistor can be guessed as low or high for every cycle. Also for a pre-specified key-length we count the number of high resistance estimations per bit –at Bob’s end-according to the previous criterion and the non-successful estimations per bit to judge the final guessed resistor value at Alice’s end and Bob’s end, where if we have more bits with most of its measured samples are above the threshold voltage then we will predict Bob’s resistance as high resistance, otherwise we predict Bob’s resistance to be low resistance. The Simulation was conducted and the attack proved that it is successful unless the temperature increased dramatically to ranges more than a threshold temperature ~ Kelvin that increases when the number of samples per bit increases.
This dissertation demonstrates three new types of attacks against the KLJN scheme. The first attack type is based on compromised RNGs. The first RNG attacks are deterministic. First, Eve knows both noises. She can crack the bit via Ohm's Law and one-bit powers within a fraction of the bit exchange period. Second, Eve knows only Bob's noise, so she can learn Bob's resistance value via Ohm's Law and Alice's resistance at the end of the bit exchange period. She can also use a process of elimination. The second RNG attacks are statistical. First, Eve has partial knowledge of Alice's and Bob's noises. She can crack the bit by taking the highest cross-correlation between her noises and the measured noise in the wire, and by taking the highest cross-correlation between her noises and Alice's/Bob's noises. Second, Eve has partial knowledge of only Alice's noise. She can still crack the bit, but after the bit exchange period. The second attack type is based on thermodynamics. Previously, the KLJN scheme required thermal equilibrium. However, Vadai, et al, in (Nature) Science Reports shows a modified scheme, where there is a non-zero thermal noise, yet the system resists all the known attacks. We utilize coincidence events between the line current and voltage and show that there is non-zero information leak. As soon as thermal equilibrium is restored, the system is perfectly secure again. The final attack type is based on the nonlinearity of the noise generators. We explore the effect of distortion at the second and third orders. It is demonstrated that 1% distortion results in a significant information leak. We also show that decreasing the effective temperature results in the KLJN scheme approaching perfect security.
The information-theoretically (unconditionally) secure Kirchhoff-law-Johnson-noise (KLJN) bit exchange protocol uses two identical resistor pairs with high (H) and low (L) resistance values, driven by Gaussian noise generators emulating Johnson noise with a high common temperature. The resulting mean-square noise voltage on the wire connecting Alice and Bob has three levels: low (L/L), intermediate (H/LorL/H), and high (H/H), and secure key sharing is achieved at the intermediate level (L/HorH/L). This paper introduces the Flip-KLJN scheme, where a pre-agreed intermediate level, such asH/L, triggers a flip of the bit map value during the bit exchange period. For Eve, the bit map flips appear random. Thus, the formerly discardedH/HandL/Lsituations can also have a pre-agreed bit value mapping, which flips together with the original bit mapping. Thus, Flip-KLJN doubles the key rate and ensures that all three levels on the wire are indistinguishable for Eve. Bit error probabilities are addressed through analytic calculations and computer simulations.