Intrusion Detection Systems (IDS) form a critical component of the security infrastructure in Software-Defined Networks (SDN). However, they often need to strike a balance between high detection accuracy, real-time responsiveness, and adaptability. Furthermore, traditional IDS that operate at the controller level suffer from centralized bottlenecks and high false alarms for evolving threats. Thus, edge-assisted approaches have been proposed to alleviate some of these drawbacks. While emerging quantum-enhanced strategies can be employed to accelerate this process, this would require specialized hardware at the edge devices, which is either impractical or too cost-prohibitive. In this work, we apply Classical Client Quantum Federated Learning (CC-QFL) to adaptive resource-constrained SDN nodes that use classical shadow tomography to offload quantum model training and aggregation to a centralized quantum controller. The overhead of periodic retraining is significantly reduced and inference requires $0.1 \mu$ s per instance. CC-QFL is evaluated on the Edge IIoTset dataset consisting of 1,048,576 instances, achieving $83.2 \%$ accuracy, $86 \%$ precision, $95 \%$ recall, and a $91 \%$ F1-score. It takes only 2.8 ms per round to train, which is several orders of magnitude faster than conventional deep learning IDS. These results reveal that in dynamic SDN environments, CC-QFL provides ultra-low latency, scalable, and robust intrusion detection, opening up possibilities for quantum-driven enhancements in the responsiveness of SDN controllers.

Sorting operations are considered to be a significant part of any computer system and are widely used in many applications. In applications where sorting has to be efficiently accomplished (i.e., inO(1) time) on small-sized entries, hardware accelerators, such as ASICs, FPGAs, or GPUs, are used to speed up the sorting operations. In the literature, the bitonic sort algorithm (or variants thereof) is still considered to be the most commonly used approach in many hardware sort implementations for decades. However, the time complexity of the bitonic sort isO((log(n))2) for sortingnelements, which does not satisfy the constant-time constraint we demand for our setting. In this paper, we proposecompetition-style sorting networks(CSNs), a framework for designing hardware-based competition-style class of sorting networks that captures all forms of two-stage sorting networks where the first stage (competition) consists of pairwise comparisons and the second stage (evaluation) ranks the entries and sorts them. To illustrate the utility of this framework, we develop and test one instance of this design, called the Competition Sort Algorithm (CSA), which has a time complexity ofO(1), and specifically, one clock cycle. We implemented and tested CSA on both an Intel Cyclone V FPGA and the NVIDIA Quadro T1000 GPU then measured itsgain, which combines the trade-offs between the relative speedup and the relative area increase, against the bitonic sort. Our results show that the CSA achieves a significant gain of up to 11.01× on the FPGA and a relative speedup of up to 3.32× on the GPU. We also compare the area, power, and latency of CSA with the bitonic sort algorithm on the FPGA.

Machine learning classifiers are vulnerable to adversarial examples, which are carefully crafted inputs designed to compromise their classification performance. Recently, a new machine learning classifier was proposed that is composed of forests of decision trees, inspired by the architecture of deep neural networks. However, deep neural networks are vulnerable to adversarial attacks. Therefore, in this work, we launch a series of adversarial attacks on deep forests, including black-box and white-box attacks, to assess its vulnerability to adversarial attacks for the first time. Prior work has shown that adversarial examples crafted on one model transfer across various models with different learning techniques. We demonstrate empirically that deep forest is vulnerable to cross-technique-based transferability attacks. On the other hand, to improve the performance of deep forest under adversarial settings, our work includes experiments that demonstrate that training non-differentiable models such as deep forests on randomly or adversarially perturbed inputs increases its adversarial robustness to such attacks. Furthermore, a heuristic white-box method to attack deep forests is proposed by implementing a faster and more efficient decision tree attack algorithm. By attacking both deep forest components, namely the cascade forest and multi-grained layer, we show that deep forests are susceptible to the proposed white-box adversarial attack.

In this article, we present DPTS, a data payment and transfer the scheme that uses bitcoin payments to reward users for detailed electricity measurements they submit to a utility provider (UP). DPTS emphasizes both privacy and fairness of transactions; not only it allows participants to earn bitcoins in a way that cannot be linked to their actions or identities but also ensures that data are delivered if and only if an appropriate payment is received. While DPTS is described in the smart grid setting, the protocol can also be applied in other areas where incentives are used to increase user participation. One such important area is participatory or crowdsensing, where individuals use their smartphones to report sensed data back to a campaign administrator and obtain a reward for it. DPTS allows users to enjoy the benefits of participation without compromising anonymity. The proposal is coupled with a security analysis showing the privacy-preserving character of the system along with an efficiency analysis demonstrating the feasibility of our approach.