@article{chen_su_yu_he_wang_ma_guo_2023, title={Cross-Domain Industrial Intrusion Detection Deep Model Trained With Imbalanced Data}, volume={10}, ISSN={["2327-4662"]}, DOI={10.1109/JIOT.2022.3201888}, abstractNote={Constrained by the high acquisition and labeling cost, traffic data in industrial control systems (ICSs) are usually extremely imbalanced. Deep-learning-based (DL) industrial control intrusion detection systems (IDSs) are not applicable to dynamic networks and show a limited detection performance. In this article, we enhanced the information transmission link in adversarial domain adaptation (DA) and proposed an information-enhanced adversarial DA (IADA) method. Our method could train a cross-domain industrial intrusion detection deep model with imbalanced data and maintained high detection accuracy. The experimental results based on SCADA network layer data showed that the detection accuracy of the gated recurrent unit model trained in IADA reached 93.7% and 91.3% in the two transfer tasks with a significant cross-domain discrepancy.}, number={1}, journal={IEEE INTERNET OF THINGS JOURNAL}, author={Chen, Yongle and Su, Sida and Yu, Dan and He, Hao and Wang, Xiaojian and Ma, Yao and Guo, Hao}, year={2023}, month={Jan}, pages={584–596} }
 @article{wang_yu_yang_xue_gu_li_zhou_2023, title={Fence: Fee-Based Online Balance-Aware Routing in Payment Channel Networks}, volume={10}, ISSN={["1558-2566"]}, DOI={10.1109/TNET.2023.3324136}, abstractNote={Scalability is a critical challenge for blockchain-based cryptocurrencies. Payment channel networks (PCNs) have emerged as a promising solution for this challenge. However, channel balance depletion can significantly limit the capacity and usability of a PCN. Specifically, frequent transactions that result in unbalanced payment flows from two ends of a channel can quickly deplete the balance on one end, thus blocking future payments from that direction. In this paper, we propose Fence, an online balance-aware fee setting algorithm to prevent channel depletion and improve PCN sustainability and long-term throughput. In our algorithm, PCN routers set transaction fees based on the current balance and level of congestion on each channel, in order to incentivize payment senders to utilize paths with more balance and less congestion. Our algorithm is guided by online competitive algorithm design, and achieves an asymptotically tight competitive ratio with constant violation in a unidirectional PCN. We further prove that no online algorithm can achieve a finite competitive ratio in a general PCN. Extensive simulations under a real-world PCN topology show that Fence achieves high throughput and keeps network channels balanced, compared to state-of-the-art PCN routing algorithms.}, journal={IEEE-ACM TRANSACTIONS ON NETWORKING}, author={Wang, Xiaojian and Yu, Ruozhou and Yang, Dejun and Xue, Guoliang and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong}, year={2023}, month={Oct} }
 @article{zhou_yu_li_gu_wang_2022, title={FedAegis: Edge-Based Byzantine-Robust Federated Learning for Heterogeneous Data}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM48099.2022.10000981}, abstractNote={This paper studies how an edge-based federated learning algorithm called FedAegis can be designed to be ro-bust under both heterogeneous data distributions and Byzantine adversaries. The divergence of local data distributions leads to suboptimal results for the training process of federated learning, and the Byzantine adversaries aim to prevent the training process from converging in a distributed learning system. In this paper, we show that an edge-based hierarchical federated learning architecture can help tackle this dilemma by utilizing edge nodes geographically close to clusters of local devices. By combining a distributionally robust global loss function with a local Byzantine-robust aggregation rule, FedAegis can defend against remote Byzantine adversaries who cannot manipulate local devices' connections to edge nodes, meanwhile accounting for global data heterogeneity across benign local devices. Experiments with the MNIST, FMNIST and CIFAR-IO datasets show that our proposed algorithm can achieve convergence and high accuracy under heterogeneous data and various attack scenarios, while state-of-the-art defenses and robustness mechanisms are non-converging or have reduced average and/or worst-case accuracy.}, journal={2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022)}, author={Zhou, Fangtong and Yu, Ruozhou and Li, Zhouyu and Gu, Huayue and Wang, Xiaojian}, year={2022}, pages={3005–3010} }
 @article{wang_gu_li_zhou_yu_yang_2022, title={Why Riding the Lightning? Equilibrium Analysis for Payment Hub Pricing}, ISSN={["1550-3607"]}, DOI={10.1109/ICC45855.2022.9839171}, abstractNote={Payment Channel Network (PCN) is an auspicious solution to the scalability issue of the blockchain, improving transaction throughput without relying on on-chain transactions. In a PCN, nodes can set prices for forwarding payments on behalf of other nodes, which motivates participation and improves network stability. Analyzing the price setting behaviors of PCN nodes plays a key role in understanding the economic properties of PCNs, but has been under-studied in the literature. In this paper, we apply equilibrium analysis to the price-setting game between two payment hubs in the PCN with limited channel capacities and partial overlap demand. We analyze existence of pure Nash Equilibriums (NEs) and bounds on the equilibrium revenue under various cases, and propose an algorithm to find all pure NEs. Using real data, we show bounds on the price of anarchy/stability and average transaction fee under realistic network conditions, and draw conclusions on the economic advantage of the PCN for making payment transfers by cryptocurrency users.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)}, author={Wang, Xiaojian and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong and Yu, Ruozhou and Yang, Dejun}, year={2022}, pages={5409–5414} }