AI 中文总结
本研究针对恶意软件检测难题，提出基于机器学习的检测框架，评估对比多种算法，发现XGBoost准确率达98.62%最优，还基于Flask开发了实时检测系统，为网络安全方案提供支撑。
AI 中文摘要
恶意软件对现代计算系统构成持续且不断演变的威胁，使得准确及时的检测成为关键的网络安全挑战。传统基于特征码的杀毒解决方案往往无法识别新出现的恶意软件，在更新的特征码可用前，系统易受攻击。为解决这一局限，本研究提出一种基于机器学习的恶意软件检测框架，可高精度区分恶意软件与良性应用程序。采用基准恶意软件数据集，对包括人工神经网络（ANN）、支持向量机（SVM）、XGBoost、极端随机树分类器在内的多种先进分类算法进行评估与对比。实验结果显示，XGBoost表现最佳，准确率达98.62%，优于其他被评估模型。为验证所提方法的实际适用性，还采用Flask框架开发了实时客户端-服务器恶意软件检测系统，可高效将可执行文件分类为恶意或良性。研究结果凸显了先进机器学习技术在提升恶意软件检测方面的有效性，并为开发智能且可扩展的网络安全解决方案作出贡献。
英文摘要
Malware poses a persistent and evolving threat to modern computing systems, making accurate and timely detection a critical cybersecurity challenge. Traditional signature-based antivirus solutions often fail to identify newly emerging malware, leaving systems vulnerable until updated signatures become available. To address this limitation, this study proposes a machine learning-based malware detection framework capable of distinguishing malicious software from benign applications with high accuracy. Several state-of-the-art classification algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), XGBoost, and Extra Trees Classifier, were evaluated and compared using a benchmark malware dataset. Experimental results demonstrate that XGBoost achieved the best performance, attaining an accuracy of 98.62%, outperforming the other evaluated models. To demonstrate the practical applicability of the proposed approach, a real-time client-server malware detection system was also developed using the Flask framework, enabling efficient classification of executable files as malicious or benign. The findings highlight the effectiveness of advanced machine learning techniques for enhancing malware detection and contribute toward the development of intelligent and scalable cybersecurity solutions.
DOI：10.14569/IJACSA.2020.0110163
