Hardware Trojans (HTs), which are malicious modifications injected into an integrated circuit (IC) by untrusted vendors, pose a significant threat to circuit design due to their highly destructive nature. The covert characteristics of HTs present challenges for detection methods, such as the requirement for transferable unknown circuit detection, the extensive manual effort involved, and the difficulty in fine-grained localization. To address these issues, we present B-HTRecognizer, a novel learning-based classification methodology that leverages HT similarities to automatically localize HTs in unknown designs at the bit level. In this study, we convert Verilog hardware description language (HDL) design into bit-level edge-featured data flow graphs (DFGs) using graph attention network (GAT) for multidimensional feature extraction of HTs. The bit-level feature extraction can achieve better performance when dealing with Trigger-hidden HTs, which are highly likely to bypass existing GNN solutions. Furthermore, we construct an open-source HT dataset named TrustHub IMEex The HTs dataset is publicly available athttps://www.scidb.cn/en/anonymous/QjNFdmUywhich extends the TrustHub dataset to facilitate effective training and precise localization. Through rigorous experimentation across different designs, our proposed method achieves 84% precision and 93% recall in noncross-design settings, and a recall rate of 77% on a 32-bit RISC-V design in cross-design testing.

Noise present in real gravity data can lead to inaccurate inversion results. Multi-task strategy in deep learning provides a promising method to solve this problem. In this study, a multi-task framework is proposed for simultaneous inversion and denoising of noisy gravity data, in which the denoising task can constrain the inversion task. To extract multi-scale field information for high precision inversion, a novel backbone network, known as the cross-dimensional UNet (CDUNet), is proposed. CDUNet employs cross-dimensional skip-connections to transfer different scale field features, wherein transformation modules are used to convert two dimensional (2D) features extracted from gravity data to 3D features for density model reconstruction. A noisy dataset was synthesized to train the network, which comprised diverse density models having highly random geometric and physical characteristics. The test set evaluations showed that CDUNet and the multi-task framework could integrate well and the inversion accuracy of the network could reach 70.8% over the density perturbation area and 97% over the entire area. The synthetic examples showed that the inverted models characterized by distinct boundaries and relatively accurate values. Finally, the method was validated using real data from the Vinton salt dome in Texas and Louisiana, USA.

Three-dimensional (3-D) gravity inversion obtains the density distribution of subsurface geological bodies through observed gravity anomalies. Recently, data-driven methods based on deep neural networks (DNNs) have received considerable attention for geophysical inverse problems. They proved to be superior to physics-driven methods that suffer from nonuniqueness issues and high computational costs. Several deep-learning inversion strategies have been developed for geophysical modeling and are mainly applicable for two-dimensional (2-D) subsurface imaging. Despite their effectiveness, deep-learning inversions suffer from appropriate generalization to new case scenarios. In this study, a novel 3-D gravity inversion method based on encoder–decoder neural networks is proposed. The network has a gravity field encoder for 2-D gravity field feature extraction, a dimension transformation module for feature space transformation, and a density structure decoder for 3-D density structure reconstruction. A highly random dataset is constructed to enlarge the feature distribution space of the samples and perform hyper-parameter experiments to improve the accuracy and generalisability of the network. Numerical examples using synthetic gravity data show that the accuracy of the network can reach 97% in the entire area and 81.5% in the area of density structures while reducing the computational time by 20 times. In the experiments on real data of the Vinton salt dome, the results are in good agreement with the known geological information. Our method significantly improves the inversion accuracy and generalisability of 3-D gravity inversion and can be applied in real case scenarios with less computational time.

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

In edge computing scenarios, the computing service provider needs to be selected according to the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases system response latency. Compared with TCP/IP, Information-Centric Networking(ICN) has more potential to provide efficient network support for edge computing, so we consider leveraging ICN facilitate edge computing. However, traditional ICN cannot be used directly in edge computing because the computing results are dynamically generated in edge computing. Therefore, we propose an edge computing service model based on ICN, which contains a new naming scheme, service session models and service request forwarding strategies. Our edge computing service model can forward computing requests to an appropriate edge server in a distributed way, which can provide real-time, dynamic and efficient edge computing services.

Multicast rate adaptation in WLAN has become a hot topic from the research community. However, wireless IP multicast lacks in feedback mechanism from the receiver and also no retransmission mechanism from packet loss. To achieve multicast reliability, multicast data is always transmitted at the basic data rate (e.g., 1Mbps for 802.11b). Moreover, wireless NDN multicast is essentially different from traditional wireless IP multicast. NDN multicast composition is based on Pending Interest Table (PIT) state information, which significantly increases the dynamics of the multicast groups. In this paper, we propose a multicast rate adaptation scheme to dynamically select the multicast transmission rate for NDN multicast communication. We present a mapping mechanism between the PIT entry and the MAC address of the station. To reduce the multicast transmission time, we propose a multi-rate multicast transmission scheme for the dynamic NDN multicast groups. In addition, we use the NDN caching mechanism to improve the reliability and reduce the transmission delay when the multicast data loss. Simulation results show that the proposed multicast rate adaptation scheme can effectively reduce transmission time while achieving lower delay and packet loss rate.