期刊文献+

基于YARA的Java内存马检测方案设计

Design of Java memory-resident malware detection scheme based on YARA
下载PDF
导出
摘要 随着互联网的发展,恶意软件逐渐成为威胁网络安全的重要因素。而Java内存马作为一种内存驻留的恶意软件,不仅具有隐蔽性高、易于传播等特点,还能够利用一些Java的高级特性实现更复杂的攻击行为,给网络安全带来更大的威胁。文章提出了一种基于YARA的Java内存马检测方案,通过向JVM中注入Agent将高风险类导出并通过YARA实现对Java内存中的恶意代码的检测和定位,再对该方法进行了实验验证。实验结果表明,该方案能够有效地检测Java内存马,具有较高的检测准确率和较低的误报率。 With the development of the Internet,malware has gradually become an important factor threatening network security.As a type of memory-resident malware,Java memory-resident malware not only has high concealment and ease of propagation,but also can use some advanced features of Java to implement more complex attack behaviors,posing greater threats to network security.This paper proposes a YARA-based method for detecting Java memory-resident malware,which defines some feature strings and regular expression rules to detect and locate malicious code in Java memory,and verifies the method through experiments.The experimental results show that the method can effectively detect Java memory-resident malware with high detection accuracy and low false positive rate.
作者 刘向伟 张晓娇 宋金金 Liu Xiangwei;Zhang Xiaojiao;Song Jinjin(Jiangsu Golden Shield Detection Technology Co.,Ltd.,Nanjing 210042)
出处 《无线互联科技》 2023年第6期41-44,48,共5页 Wireless Internet Technology
关键词 Java内存马 YARA 恶意软件 检测方法 Java memory-resident malware YARA malware detection method
  • 相关文献

参考文献2

二级参考文献29

  • 1刘冰.多类SVM分类算法的研究和改进.电脑知识与技术,2007,(6):1590-1593.
  • 2Xiao Yao. Large and Medium-sized Network Intrusions Cases Research[J]. Publishing House Of Electronics Industry, 2010,(10):301-310.
  • 3J. Ross Quinlan. C4. 5: programs for machine learning[M]. San Francisco: Morgan Kaufmann, 1993.
  • 4Yung-Tsung Hou, Yimeng Chang, Tsuhan Chen.Malicious web content detection by machine learning[J]. Expert Systems with Applications,2010,37(1):55-60.
  • 5Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines[C]//Proceedings of IEEE Workshop on Neural Networks for Signal Processing. Amelia Island, USA: IEEE Press, 1997: 276-285.
  • 6Lin H T, Lin C J, Weng R C. A note on Plat tps probabilistic outputs for support vector machines[J]. Machine Learning, 2007, 68 (3): 267-276.
  • 7Brinker K. On multiclass active learning with support vector machines[C]//Proceedings of European Conference on Artificial Intelligence. 2004: 969-970.
  • 8Yuan X, Lai W, Mei T , et al. Automatic video genre categorization using hierarchical SVM[C]//IEEE International Conference on Image Processing. Atlanta: IEEE Press, 2006: 2905-2908.
  • 9Tong S , Chang. E Support vector machine active learning for image ret rieval[C]//Proceedings of the 9th ACM International Conference on Multimedia. Ottawa, Canada: ACM Press, 2001, 9: 107-118.
  • 10CORTES C, VAPNIK V. Support vector network[J]. Machine Learning, 1995, (20):273-297.

共引文献25

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部