High latency is a critical limitation within the Tor network that has a negative impact on web application responsiveness. A key factor exacerbating Tor latency is the creation of lengthy circuits that span across geographically distant regions, causing significant transmission delays. A common solution involves modifying Tor's circuit-building process to reduce the likelihood of selecting lengthy circuits. However, this strategy compromises Tor's routing randomness, increasing the risk of deanonymization. Reducing Tor's latency while minimizing security degradation presents a challenge. This paper proposes and investigates SaTor, a satellite-assisted routing scheme to reduce Tor latency. By equipping Tor relays with satellite network access, SaTor could accelerate slow circuits via satellite transmission, without biasing the existing path selection process. Our performance evaluation, using a simulator we developed along with real-world measurements, shows that over the long term, SaTor provides an expected speed-up of 21.8 ms for over 40% of circuits, with only 100 top relays equipped with satellite service. Our research uncovers a viable way to overcome Tor's latency bottleneck, serving as a practical reference for its future enhancement.

Network covert timing channels can be maliciously used to exfiltrate secrets, coordinate attacks and propagate malwares, posing serious threats to cybersecurity. Current covert timing channels normally conduct small-volume transmission under the covers of various disguising techniques, making them hard to detect especially when a detector has little priori knowledge of their traffic features. In this article, we propose a generic and sensitive detection approach, which can simultaneously (i) identify various types of channels without their traffic knowledge and (ii) maintain reasonable performance on small traffic samples. The basis of our approach is the finding that the short-term timing behavior of covert and legitimate traffic is significantly different from the perspective of inter-packet delays’ variation. This phenomenon can be a generic reference to detect various channels because it is resistant to major channel disguising techniques which only mimic long-term traffic features, while it is also a sensitive reference to spot small-volume covert transmission since it can capture traffic anomalies in a fine-grained manner. To obtain the inner patterns of inter-packet delays’ variation, we design a context-sensitive feature-extraction technique. This technique transforms each raw inter-packet delay into a discrete counterpart based on its contextual properties, thus extracting its variation features and reducing traffic data complexity. Then we learn legitimate variation patterns using a neural network model, and identify samples showing anomalous variation as covert. The experimental results show that our approach effectively detects all currently representative channels in the absence of their knowledge, presenting once to twice higher sensitivity than the state-of-the-art solutions.

Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce annotated examples. Existing methods solve this problem by performing subtasks of classification and localization utilizing a shared component in the detector, yet few of them take the distinct preferences towards feature embedding of two subtasks into consideration. In this paper, we carefully analyze the characteristics of FSOD, and present that a few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations. To the end, we propose a simple yet effective Adaptive Fully-Dual Network (AFD-Net). Specifically, we extend Faster R-CNN by introducing Dual Query Encoder and Dual Attention Generator for separate feature extraction, and Dual Aggregator for separate model reweighting. In this way, separate state estimation is achieved by the R-CNN detector. Furthermore, we introduce Adaptive Fusion Mechanism to guide the design of encoders for efficient feature fusion in the specific subtask. Extensive experiments on PASCAL VOC and MS COCO show that our approach achieves state-of-the-art performance by a large margin, demonstrating its effectiveness and generalization ability.

An emerging subsynchronous oscillation (SSO) associated with wind generators and voltage-sourced converter (VSC) based HVDC has been observed around the world. It is revealed that this new type of SSO is caused by the subsynchronous control interaction among power electronic converters and the grid. In this paper, the stability criterion of SSO is examined using frequency-dependent impedance models. Then a shunt VSC based subsynchronous damping control (SVSDC) has been proposed to reshape the impedance characteristic of the whole system and thus mitigate the SSO. The stability criterion and the control has been applied to a typical system with DFIG-based wind farms connected to a modular multilevel converter (MMC) based HVDC transmission. Impedance model based analyses as well as electromagnetic simulations have verified the effectiveness of the criterion and the control scheme. For its flexibility of capacity and deployment, the proposed SVSDC offers a promising option to address the SSO issues associated with wind generators and/or VSC-HVDCs.