J4 ›› 2016, Vol. 33 ›› Issue (2): 182-187.

• 量子光学 • 上一篇    下一篇

基于量子计算的僵尸网络周期性通信行为检测算法

王新良,杨茜惠,靳翔   

  1. 河南理工大学电气工程与自动化学院,河南 焦作,454000
  • 收稿日期:2015-01-21 修回日期:2015-02-24 出版日期:2016-03-28 发布日期:2016-03-28
  • 通讯作者: 王新良 E-mail:junci158@163.com
  • 作者简介:National Natural Science Foundation of China (Grant Nos. 41074090), the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 61405055)

Periodic communication detection of botnet based on quantum computing

Wang Xin-Liang, Yang Qian-Hui, Jin Xiang   

  1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiao Zuo, 454000, China
  • Received:2015-01-21 Revised:2015-02-24 Published:2016-03-28 Online:2016-03-28
  • Contact: 王新良(1980-),河南禹州人,博士,副教授,主要研究方向为量子通信、网络安全。 E-mail:junci158@163.com

摘要: 僵尸网络需要在控制者和受控主机之间维持周期性通信,如果能够有效识别僵尸网络的周期性通信行为,就能够以此为基础实现僵尸网络检测。尽管一些算法提出了基于周期性通信行为的僵尸网络检测方法,但是如何在海量数据中实现僵尸网络的快速检测仍然是一个问题。基于量子计算的僵尸网络周期性通信行为检测算法,是在已有算法的基础上引入量子计算来提高周期性通信检测算法的速度。实验结果表明,改进后的算法与已有算法相比,拥有相同的检测精度,与此同时,能够使用较少的查询次数完成僵尸网络检测,能够有效提高僵尸网络检测的速度。

关键词: 周期性通信,僵尸网络,量子计算,Grover算法

Abstract: The botnet needs to maintain periodic communication between the controller and compromised hosts, so it becomes possible to utilize the periodic communication to achieve the botnet detection. Although some algorithms have been proposed to achieve the botnet detection based on the periodic communication behavior, it is still a problem how to achieve the faster detection of botnet in the vast amounts of data. The improved detection algorithm will introduce quantum computing into accelerating the periodic communication detection based on the existing algorithm. The experimental results show that the improved algorithm owns the same accuracy, and all abnormal IP will be correctly decided. Meanwhile, it can utilize the less query count to complete the detection of botnet, and can effectively achieve the algorithm acceleration.

Key words: Periodic communication, Botnet, Quantum computing, Grover

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