3 research outputs found
An efficient modeling and simulation of differential phase shift-quantum key distribution (DPS-QKD) system using optisystem
No full textDifferential phase-shift (DPS) quantum key distribution (QKD) is a unique QKD protocol that is different from traditional ones, featuring simplicity and practicality. In this work, we simulated the DPS-QKD experiment conducted by (Liu et al., 2013), using OptiSystem 7. To the best of our knowledge, this is the first simulation work on DPS-QKD using a single photon source.We used a random number generator to get the phase modulation pattern of N=5, 7,9,11 and 13, while for the 3 and 15 pulse cases, the pattern adopted in the experiment was used. When the number of pulse (N) was 3, a quantum bit error rate (QBER) of 3.0%, which is lower than the minimum QBER of 4.12% required for unconditional security, was obtained. The key creation efficiency increases with the increase in the number of pulse up to 15, as it reaches 93.4% but at the expense of the increment in QBER. The result of our simulation is, on some aspect, in agreement with the experimental result. However, we were able to extend the transmission distance from 3 meter, as in the experiment, to 10 meter. The coincidence count obtained was also in total agreement with the one obtained from the experiment. The result of the average QBER indicated that increase in the pulse number N causes the QBER to raise up due to longer rise and fall time of phase modulation step which affect the MZ inference. Therefore, we suggest using a faster waveform generator with shorter rise and fall times will remarkably lower the QBER. Extending the transmission coverage to a longer distance while, at the same time reducing the QBER with full unconditional security will part of the future research
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems
No full textThe internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models are still vulnerable to evasion, poisoning and exploratory attacks by adversarial examples. The BFF-IDS offers partial defence against poisoning but has no measure for evasion attacks, the most common attack/threat faced by ML models. Besides, the impact of adversarial examples transferability in CAN IDS has largely remained untested. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We also investigated the statistical adversarial detector's effectiveness and resilience in detecting the attacks and subsequent countermeasures by augmenting the model with detected samples. Our investigation results established that BFF-IDS is very vulnerable to adversarial examples attacks. The statistical adversarial detector and the subsequent BFF-IDS augmentation (BFF-IDS(AUG)) provide an effective mechanism against the adversarial examples. Consequently, integrating the statistical adversarial detector and the subsequent BFF-IDS augmentation with the detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.Information and Communication Technolog
