Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi, He Yan, Lu Xiangju, Junmin Zhu, Wei Zhang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Extensions complement web browsers with additional functionalities and also bring new vulnerability venues, allowing privilege escalations from adversarial web pages to use extension APIs. Prior works on extension vulnerability detection adopt classic static analysis, which is unable to handle dynamic JavaScript features such as those function calls as part of array lookups. At the same time, prior abstract interpretation focuses on lightweight server-side JavaScript, which often cannot scale to client-side extension code due to object explosions in the abstract domain.

Mobile edge computing (MEC) holds great promise as an effective solution that empowers resource-constrained intelligent applications to transfer computation-intensive operations to neighboring edge servers. To realize its potential, reconfigurable intelligent surface (RIS) provides high spectral and energy efficiency, can effectively reduce the computation costs. This paper discusses a joint computation offloading and power allocation strategy for non-orthogonal multiple access (NOMA) based MEC scenario assisted by RIS. First, we formulate a cost minimization problem which considers task buffering delay, power consumption, and task queue length constraints. Then, we use Lyapunov optimization method to transform the task queue length constraint into a queue stability problem. Finally, a Markov decision process (MDP) model and a double deep Q-network (DDQN) based computation offloading and power allocation (DCOPA) algorithm are proposed to obtain optimal computation offloading strategy and achieve the system’s cost objective. Through simulation results, it is demonstrated that the proposed method can effectively reduce the computation offloading delay and has a lower computation cost than other methods. The algorithm is effective in both convergence and long-term performance.

With the increasing prevalence of web servers, protecting them from cyber attacks has become a crucial task for online service providers.Webshells, which are backdoors to websites, are commonly used by hackers to gain unauthorized access to web servers.However, traditional methods for detecting webshells often fail to produce satisfactory results due to the use of obfuscation or encryption to conceal their characteristics.In recent years, webshell detection methods based on deep learning (DL) have received significant attention, but they struggle to preserve the syntax and semantic information contained in the source code.In this paper, we propose a structuralaware webshell detection system to address these problems, denoted as SAWD.Specifically, we first generate the control flow graph (CFG) with syntax and semantic information from the PHP source code.Then, we leverage CFG to build our graph representation, which consists of the adjacency matrix and keywords-based basic block features.Finally, based on our graph representation, we adopt convolutional neural networks (GCN) combined with graph pooling to detect webshells more efficiently.Experimental results demonstrate that our method outperforms state-of-the-art webshell detection systems on the collected dataset.