Chen Zhiguo, Xing Shuangshuang, Ren Xuanyu
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China.
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China.
Front Plant Sci. 2023 Feb 15;14:1123696. doi: 10.3389/fpls.2023.1123696. eCollection 2023.
Due to developments in science and technology, the field of plant protection and the information industry have become increasingly integrated, which has resulted in the creation of plant protection information systems. Plant protection information systems have modernized how pest levels are monitored and improved overall control capabilities. They also provide data to support crop pest monitoring and early warnings and promote the sustainable development of plant protection networks, visualization, and digitization. However, cybercriminals use technologies such as code reuse and automation to generate malware variants, resulting in continuous attacks on plant protection information terminals. Therefore, effective identification of rapidly growing malware and its variants has become critical. Recent studies have shown that malware and its variants can be effectively identified and classified using convolutional neural networks (CNNs) to analyze the similarity between malware binary images. However, the malware images generated by such schemes have the problem of image size imbalance, which affects the accuracy of malware classification. In order to solve the above problems, this paper proposes a malware identification and classification scheme based on bicubic interpolation to improve the security of a plant protection information terminal system. We used the bicubic interpolation algorithm to reconstruct the generated malware images to solve the problem of image size imbalance. We used the Cycle-GAN model for data augmentation to balance the number of samples among malware families and build an efficient malware classification model based on CNNs to improve the malware identification and classification performance of the system. Experimental results show that the system can significantly improve malware classification efficiency. The accuracy of RGB and gray images generated by the Microsoft Malware Classification Challenge Dataset (BIG2015) can reach 99.76% and 99.62%, respectively.
由于科学技术的发展，植物保护领域与信息产业的融合日益加深，催生了植物保护信息系统。植物保护信息系统使害虫监测方式实现了现代化，并提升了整体防控能力。它们还提供数据以支持农作物害虫监测和早期预警，并推动植物保护网络的可视化和数字化可持续发展。然而，网络犯罪分子利用代码复用和自动化等技术生成恶意软件变种，导致对植物保护信息终端的持续攻击。因此，有效识别快速增长的恶意软件及其变种变得至关重要。最近的研究表明，使用卷积神经网络（CNN）分析恶意软件二进制图像之间的相似度，可以有效地识别和分类恶意软件及其变种。然而，此类方案生成的恶意软件图像存在图像大小不平衡的问题，这影响了恶意软件分类的准确性。为了解决上述问题，本文提出了一种基于双立方插值的恶意软件识别和分类方案，以提高植物保护信息终端系统的安全性。我们使用双立方插值算法对生成的恶意软件图像进行重构，以解决图像大小不平衡的问题。我们使用循环生成对抗网络（Cycle-GAN）模型进行数据增强，以平衡恶意软件家族之间的样本数量，并基于卷积神经网络构建高效的恶意软件分类模型，以提高系统的恶意软件识别和分类性能。实验结果表明，该系统可以显著提高恶意软件分类效率。微软恶意软件分类挑战赛数据集（BIG2015）生成的RGB图像和灰度图像的准确率分别可达99.76%和99.62%。
