The Algorithm of Malicious Code Detection Based on Data Mining

被引：1
|
作者：
Yang, Yubo [1 ]
Zhao, Yang [1 ]
Liu, Xiabi [2 ]
机构：
[1] Beijing E Hualu Informat Technol CO LTD, Beijing 100043, Peoples R China
[2] Beijing Inst Technol, Beijing 100081, Peoples R China
来源：
GREEN ENERGY AND SUSTAINABLE DEVELOPMENT I | 2017年 / 1864卷
关键词：
Malicious Code; Data Mining; Information Gain; Decision Tree;
D O I：
10.1063/1.4992960
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Traditional technology of malicious code detection has low accuracy and it has insufficient detection capability for new variants. In terms of malicious code detection technology which is based on the data mining, its indicators are not accurate enough, and its classification detection efficiency is relatively low. This paper proposed the information gain ratio indicator based on the N-gram to choose signature, this indicator can accurately reflect the detection weight of the signature, and helped by C4.5 decision tree to elevate the algorithm of classification detection.
引用
收藏
页数：5
相关论文
共 50 条
  • [21] Algorithm Optimization of Anomaly Detection Based on Data Mining
    Zhang, Lei
    Chen, Yong
    Liao, Shaowen
    2018 10TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA), 2018, : 402 - 404
  • [22] Power Based Malicious Code Detection Techniques for Smartphones
    Dixon, Bryan
    Mishra, Shivakant
    2013 12TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2013), 2013, : 142 - 149
  • [23] Detection of Malicious Code Variants Based on Deep Learning
    Cui, Zhihua
    Xue, Fei
    Cai, Xingjuan
    Cao, Yang
    Wang, Gai-ge
    Chen, Jinjun
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (07) : 3187 - 3196
  • [24] Dynamic Malicious Code Detection Based on Binary Translator
    Fang, Zhe
    Li, Minglu
    Weng, Chuliang
    Luo, Yuan
    CLOUD COMPUTING, PROCEEDINGS, 2009, 5931 : 80 - 89
  • [25] Malicious Code Detection Model Based on Behavior Association
    Han, Lansheng
    Qian, Mengxiao
    Xu, Xingbo
    Fu, Cai
    Kwisaba, Hamza
    TSINGHUA SCIENCE AND TECHNOLOGY, 2014, 19 (05) : 508 - 515
  • [26] Malicious code detection based on heterogeneous information network
    Liu Y.
    Hou Y.
    Yan H.
    Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (02): : 258 - 265
  • [27] A detection algorithm of customer outlier data based on data mining technology
    Ren, Jia
    PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON ECONOMIC AND BUSINESS MANAGEMENT (FEBM 2017), 2017, 33 : 272 - 278
  • [28] Massive data mining for polymorphic code detection
    Payer, U
    Teufl, P
    Kraxberger, S
    Lamberger, M
    COMPUTER NETWORK SECURITY, PROCEEDINGS, 2005, 3685 : 448 - 453
  • [29] Investigating Clustering Algorithm DBSCAN to Self Select Locations for Power Based Malicious Code Detection on Smartphones
    Dixon, Bryan
    PROCEEDINGS OF THE 2017 THIRD INTERNATIONAL CONFERENCE ON MOBILE AND SECURE SERVICES (MOBISECSERV), 2017,
  • [30] Malicious code detection in compound documents based on spectral analyses
    Liu, L. (liul@itsec.gov.cn), 1713, Tsinghua University (53):
12345