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Gaofei Huang

Publications and source records attributed to Gaofei Huang.

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A Binary and System Integrated Analysis Approach for Securing the QUIC Protocol

The Quick UDP Internet Connections (QUIC) protocol is increasingly used to provide secure transport for Internet of Things (IoT) firmware and applications. Existing security analyses of QUIC focus on the captured network traffic, while binary-level analyses of QUIC implementations remain unexplored, leaving open the question of whether a defence specified by the QUIC standard is both present in the compiled binary and active when the server is under attack. This paper evaluates the Binary and System Integrated Security Analysis (BSISA) approach, in which a binary-level analysis of the compiled QUIC server is combined with a system-level analysis of the captured network traffic, on four production QUIC server implementations under six attack scenarios. Across 24 cells, the combined classifier configuration is the only configuration that correctly classifies at least one cell on every attack scenario, achieving 45.8% overall accuracy compared with 37.5% for the binary-level configuration and 25.0% for the system-level configuration. BSISA also identifies the specific defence function in the compiled binary that absorbed each attack, and flags declared-but-silent defences, routines that are present in the compiled binary (Retry-token validation in three of four stacks, anti-amplification in quiche) but never execute during attack, a class of finding that network capture alone cannot produce. In terms of efficiency, picoquic loses legitimate-client availability under slowloris and connection- ID exhaustion with failure rates of 72.4% and 73.3% respectively, while the other three implementations hold the failure rate at or below 0.5%. We hope these insights will be informative for QUIC security evaluations in IoT firmware deployments.

cs.NI

Joint mode switching and resource allocation in wireless-powered RIS-aided multiuser communication systems

This paper investigates a wireless-powered hybrid reflecting intelligent surface (hybrid RIS)-assisted multiple access system, where the RIS can harvest energy from energy station (ES) transmitted radio frequency signal (RF), and each reflecting element can flexibly switch between active mode, passive mode, and idle mode. The objective is to minimize the maximum energy consumption of the users by jointly optimizing the operating modes of each reflecting element, the amplification factor of active elements, the transmit power, and transmission time allocation, subject to quality-of-service (QoS) of each user and the available energy constraint of RIS. In the formulated optimization problem, the operating modes of each reflecting element are highly coupled with the amplification coefficient of the active reflecting elements, making it a challenging mixed-integer programming problem. To solve this problem, a hierarchical optimization method based on deep reinforcement learning is proposed, where the operating modes of each reflecting element and the amplification coefficient of active elements are obtained by solving the outer sub-problem using proximal policy optimization (PPO), and the transmit power and transmission time allocation are obtained by solving the inner sub-problem using convex optimization methods. Simulation results show that compared to the baseline scheme, the proposed scheme can reduce user energy consumption by $70 \%$.

eess.SP