Abstract The document examines cloud computing security challenges, emphasizing that misconfigurations, weak identity controls, insecure APIs, and DoS/DDoS attacks are among the most critical vulnerabilities in modern cloud environments. It highlights that human error not platform flaws is... The document examines cloud computing security challenges, emphasizing that misconfigurations, weak identity controls, insecure APIs, and DoS/DDoS attacks are among the most critical vulnerabilities in modern cloud environments. It highlights that human error not platform flaws is the primary cause of breaches, underscoring the need for robust policy based defenses. The study's main contribution involves analyzing various DoS attack types (volumetric, protocol based, and application layer), enhancing edge router security through ACLs, rate limiting, and deep packet inspection, and validating these measures in a GNS3 network simulation environment. Key experiments demonstrate that disabling Cisco Discovery Protocol (CDP), enabling DHCP snooping, and applying port security effectively mitigate ICMP floods, rogue DHCP servers, and reconnaissance threats. The paper validates established best practices such as those from Cisco rather than proposing novel cryptographic or architectural solutions. While results show 100% mitigation of specific attacks under controlled conditions, limitations include the lack of real world deployment, the absence of AI despite the title's implication, a narrow threat scope, and a simplified network topology. Future work recommends testing in live multi cloud infrastructures, integrating AI driven anomaly detection for adaptive policy enforcement, and developing context aware threat models for hybrid cloud ecosystems. Overall, the research provides practical, simulation backed evidence that foundational Layer 2/3 security configurations significantly improve resilience against common network layer threats in cloud infrastructures. Read More Read Less

Abstract The paper explores the integration of the Organizational Project Management Maturity Model (OPM3) into the internal control framework of enterprise accounting information systems. It emphasizes that weak internal controls exemplified by historical corporate scandals like Enron and... The paper explores the integration of the Organizational Project Management Maturity Model (OPM3) into the internal control framework of enterprise accounting information systems. It emphasizes that weak internal controls exemplified by historical corporate scandals like Enron and WorldCom can lead to financial misreporting, loss of investor confidence, and even bankruptcy. The study argues that robust internal controls are now essential for corporate governance, strategic decision making, and accessing capital markets, especially for Chinese firms seeking overseas listings. Using OPM3 a framework developed by the Project Management Institute the paper constructs a tailored internal control model for accounting information systems in a digital environment. It identifies critical control points across five COSO based components: control environment, risk assessment, control activities, information and communication, and monitoring. Through a case study involving 40 participants, the author applies a fuzzy evaluation method to assess maturity levels, finding the organization operates between basic and standardized levels (scoring 2.86 out of 5). The paper concludes with practical recommendations: clarifying role segregation, implementing password protocols, establishing reward punishment mechanisms, creating risk early warning systems, and strengthening corporate culture to enhance internal control effectiveness and transform risks into strategic opportunities. Read More Read Less

Abstract The paper proposed a model to assess and predict financial risks in Chinese foreign trade listed companies. Recognizing limitations in the traditional Altman Z-Score model such as low diagnostic accuracy the authors integrate the Stochastic Optimization Algorithm... The paper proposed a model to assess and predict financial risks in Chinese foreign trade listed companies. Recognizing limitations in the traditional Altman Z-Score model such as low diagnostic accuracy the authors integrate the Stochastic Optimization Algorithm (SOA) to fine tune the model's coefficients. Using financial data from 20 listed firms and a five year case study of Jiangsu Sainty Co., Ltd. (2012-2016), they demonstrate that the SOA optimized Z-Score model significantly improves prediction accuracy, achieving a 96.33% recognition rate outperforming SVM and AdaBoost algorithms. The analysis reveals key financial indicators (e.g., retained earnings, EBIT, and asset turnover) critically influence risk levels. Based on findings, the authors recommend enterprises establish robust risk control systems, optimize capital structures, improve working capital management, and enhance profitability. The study underscores the value of combining classical financial models with modern optimization techniques for more reliable, adaptive financial risk assessment in dynamic market environments. Read More Read Less