In recent years, PowerShell has increasingly been reported as appearing in a variety of cyber attacks. However, because the PowerShell language is dynamic

With the rapid expansion of the Internet, the surge in data traffic, propelled by the exponential growth of network services and users, has heightened the risk of network congestion, security breaches, and system instability. Addressing these challenges presents stringent demands and novel complexities in queue management. However, prevailing solutions often rely heavily on average queue size thresholds while ignoring traffic variations. In this paper, CCD-AQM, an Adaptive Queue Management approach based on the Consecutive Change trend Detection in the queue size is proposed. Considering that today's programmable data plane offers promising ways for finer analysis of the queue in the hardware switches, we implement CCD-AQM on an RMT switch and analyze its resource usage. Large-scale simulations are conducted to evaluate CCD-AQM, showcasing its superior performance in queue management. The results demonstrate its ability to maintain low queue occupancy and high throughput while ensuring fairness among competing flows.

The widespread integration of third-party components (TPCs) in Internet of Things (IoT) firmware significantly increases the risk of software vulnerabilities, especially in resource-constrained devices deployed in sensitive environments. Binary Code Similarity Detection (BCSD) techniques, particularly those based on deep neural networks, have emerged as powerful tools for identifying vulnerable functions without access to source code. However, individual models, such as Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), and Transformer-based methods, often exhibit limitations due to their differing focus on structural, spatial, or semantic features. To address this, we propose VoteSim, a novel ensemble framework that integrates multiple BCSD models using an inverse average rank voting mechanism. VoteSim combines the strengths of individual models while reducing the impact of model-specific false positives, leading to more stable and accurate vulnerability detection. We evaluate VoteSim on a large-scale real-world IoT firmware dataset comprising over 800,000 binary functions and 10 high-risk CVEs. Experimental results show that VoteSim consistently outperforms state-of-the-art BCSD models in both Recall@10 and Mean Reciprocal Rank (MRR), achieving improvements of up to 14.7% in recall. Our findings highlight the importance of model diversity and rank-aware aggregation for robust binary-level vulnerability detection in heterogeneous IoT firmware.

We present FractionalDiffEq.jl, a comprehensive solver suite for solving fractional differential equations, featuring high-performance numerical algorithms in the Julia programming language. FractionalDiffEq.jl is designed to be user-friendly and scalable, tackling different types of fractional differential equations, encompassing powerful numerical algorithms including predictor-corrector methods, product-integral methods, and linear multistep methods, etc, and providing a unifying API to accommodate diverse solver features. This paper illustrates the convenient usage of FractionalDiffEq.jl in modeling various scientific problems, accompanied by detailed examples and applications. FractionalDiffEq.jl leverages best practices in Julia to ensure the high performance of numerical solvers. To validate the efficiency of FractionalDiffEq.jl , we conducted extensive benchmarks that prove the superiority of FractionalDiffEq.jl against other implementations on both stiff and non-stiff problems. We further demonstrate its capability on several challenging real-life scenarios including parameter estimation in fractional-order tequila fermentation processes, and harmonic oscillator problems, etc, emphasizing the robustness and flexibility of FractionalDiffEq.jl.