@article{li_chen_liu_zhang_liu_mao_2023, title={Graph Neural Networks for Joint Communication and Sensing Optimization in Vehicular Networks}, volume={41}, ISSN={["1558-0008"]}, DOI={10.1109/JSAC.2023.3322761}, abstractNote={In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs.}, number={12}, journal={IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS}, author={Li, Xuefei and Chen, Mingzhe and Liu, Yuchen and Zhang, Zhilong and Liu, Danpu and Mao, Shiwen}, year={2023}, month={Dec}, pages={3893–3907} }
 @article{ding_li_yi_liu_2023, title={IVSign: Interpretable Vulnerability Signature via Code Embedding and Static Analysis}, ISSN={["2325-6664"]}, DOI={10.1109/DSN-W58399.2023.00025}, abstractNote={Software vulnerability detection is evolving from pattern-driven methods to data-driven methods to be more automatic and intelligent due to the emergence of deep learning. However, false positives and coarse-grained detection can not tackle changeable and unknown threats (e.g., Oday vulnerabilities), and also low model interpretability leads to incontinent detection results or maliciously being manipulated. To enhance model confidence and interpretability, in this paper, we propose an Interpretable vulnerability signature (IVSign) scheme to combine pattern-driven and data-driven methods. In IVSign, pattern-driven methods utilize static analysis to provide rich vulnerability trace information extracted from data dependencies and data-driven methods respectively train a transformer-based model to encode these vulnerability traces and a recurrent neural network to generate vulnerability signatures. In the testing stage of IVSign, if a program sample with vulnerabilities is fed into the pipeline, its hit rate in the signature database will be very high. Experiments have shown that when the matching threshold is set appropriately, the evaluation metrics on the testing samples exceed 90.00%.}, journal={2023 53RD ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS, DSN-W}, author={Ding, Ao and Li, Gaolei and Yi, Xiaoyu and Liu, Yuchen}, year={2023}, pages={25–31} }
 @article{li_chen_zhang_liu_liu_mao_2023, title={Joint Optimization of Sensing and Communications in Vehicular Networks: A Graph Neural Network-based Approach}, DOI={10.1109/ICC45041.2023.10278588}, abstractNote={In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.}, journal={ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS}, author={Li, Xuefei and Chen, Mingzhe and Zhang, Zhilong and Liu, Danpu and Liu, Yuchen and Mao, Shiwen}, year={2023}, pages={5719–5724} }