[1]喻波,王志海,孙亚东,等.非结构化文档敏感数据识别与异常行为分析[J].智能系统学报,2021,16(5):932-939.[doi:10.11992/tis.202104028]
YU Bo,WANG Zhihai,SUN Yadong,et al.Unstructured document sensitive data identification and abnormal behavior analysis[J].CAAI Transactions on Intelligent Systems,2021,16(5):932-939.[doi:10.11992/tis.202104028]
点击复制
YU Bo,WANG Zhihai,SUN Yadong,et al.Unstructured document sensitive data identification and abnormal behavior analysis[J].CAAI Transactions on Intelligent Systems,2021,16(5):932-939.[doi:10.11992/tis.202104028]
非结构化文档敏感数据识别与异常行为分析
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第5期
页码:
932-939
栏目:
吴文俊人工智能科技进步奖一等奖
出版日期:
2021-09-05
- Title:
- Unstructured document sensitive data identification and abnormal behavior analysis
- Keywords:
- data security; artificial intelligence; classification; language model; user’s behavior analysis; sample; nlp; supervised learning
- 分类号:
- TP18;TP319;TP309
- 摘要:
- 在海量数据中快速、准确地对数据进行分类分级，快速识别用户异常行为是目前数据安全领域的重要研究内容。在数据分类分级研究领域，自然语言处理技术提升了分类分级的准确率，但是中文语体混杂、无监督学习准确率低、有监督学习样本标注工作量大等问题亟待取得关键突破。本文提出多元中文语言模型和基于无监督算法构建样本，突破数据分类分级领域面临的关键问题。在用户异常行为分析研究领域，由于样本依赖度过高，导致异常行为识别准确率较低，本文提出利用离群点检测方法构建异常行为样本库，解决样本依赖过高问题。为验证方法可行性，进一步构建实验系统开展实验分析，通过实验验证所提出方法可以显著提高数据分类分级和异常行为分析的准确率。
- Abstract:
- It is an important research content in the field of data security to classify data quickly and accurately in mass data, and to quickly identify user abnormal behavior. In the field of data classification research, natural language processing technology improves the accuracy of classification, but the problems of mixed Chinese language, low accuracy of unsupervised learning, and large workload of supervised learning sample labeling need to be Chinese made urgently. In the field of user anomaly analysis, due to high sample dependence, which leads to low accuracy of abnormal behavior recognition, this paper proposes to use outlier detection to build an abnormal behavior sample library to solve the problem of excessive sample dependence. In order to verify feasibility of the method, the experimental system is further constructed to carry out experimental analysis, and the proposed method can significantly improve the accuracy of data classification and abnormal behavior analysis.
- 参考文献/References:
-
[1] 新华社. 中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见[EB/OL]. 新华社,2020(2020-03-20). http://www.gov.cn/zhengce/2020-04/09/content_5500622.htm.
[2] ERIC OUELLET, JEFFREY WHEATMAN. Typical elements of an enterprise data security program[EB/OL].Gartner,2015(2020-10-16).https://www.gartner.com/en/documents/1153112.
[3] 罗军舟, 韩志耕, 王良民. 一种可信可控的网络体系及协议结构[J]. 计算机学报, 2009, 32(3): 391-404.
LUO Junzhou, HAN Zhigeng, WANG Liangmin. Trustworthy and controllable network architecture and protocol framework[J]. Chinese journal of computers, 2009, 32(3): 391-404.
[4] 王琨, 陆艳军. 数据文件安全管控技术的研究与实现[J]. 信息安全研究, 2018, 4(1): 84-90.
WANG Kun, LU Yanjun. Research and implementation of security management of data files[J]. Journal of information security research, 2018, 4(1): 84-90.
[5] MENEZES A J, VAN OORSCHOT P C, VANSTONE S A. Handbook of applied cryptography[M]. Boca Raton: CRC Press, 1997.
[6] NOVAK R. SPA-based adaptive chosen-ciphertext attack on RSA implementation[C]//5th International Workshop on Practice and Theory in Public Key Cryptosystems. Paris, France, 2002: 252-262.
[7] FOUQUE P A, MARTINET G, POUPARD G. Attacking unbalanced RSA-CRT using SPA[C]//5th International Workshop on Cryptographic Hardware and Embedded Systems. Cologne, Germany, 2003: 254-268.
[8] 李增局, 彭乾, 史汝辉, 等. CRT-RSA算法的选择明文攻击[J]. 密码学报, 2016, 3(5): 447-461.
LI Zengju, PENG Qian, SHI Ruhui, et al. Chosen plaintext attacks on CRT-RSA[J]. Journal of cryptologic research, 2016, 3(5): 447-461.
[9] 赵冬斌, 邵坤, 朱圆恒, 等. 深度强化学习综述: 兼论计算机围棋的发展[J]. 控制理论与应用, 2016, 33(6): 701–717
ZHAO Dongbin, SHAO Kun, ZHU Yuanheng, et al. Review of deep reinforcement learning and discussions on the development of computer[J]. Control theory and applications, 2016, 33(6): 701–717
[10] 刘朝阳, 穆朝絮, 孙长银. 深度强化学习算法与应用研究现状综述[J]. 智能科学与技术学报, 2020, 2(4): 314–326
LIU Zhaoyang, MU Chaoxu, SUN Changyin. An overview on algorithms and applications of deep reinforcement learning[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 314–326
[11] 刘全, 翟建伟, 章宗长, 等. 深度强化学习综述[J]. 计算机学报, 2018, 41(1): 1–27
LIU Quan, ZHAI Jianwei, ZHANG Zongchang, et al. A survey on deep reinforcement learning[J]. Chinese journal of computers, 2018, 41(1): 1–27
[12] 万里鹏, 兰旭光, 张翰博, 等. 深度强化学习理论及其应用综述[J]. 模式识别与人工智能, 2019, 32(1): 67–81
WAN Lipeng, LAN Xuguang, ZHANG Hanbo, et al. A review of deep reinforcement learning theory and application[J]. Pattern recognition and artificial intelligence, 2019, 32(1): 67–81
[13] 窦宇宸, 胡勇. 基于BERT的安全事件命名实体识别研究[J]. 信息安全研究, 2021, 7(3): 242–249
DOU Yuchen, HUYong. Research on name entity recognition of security events based on BERT[J]. Journal of information security research, 2021, 7(3): 242–249
[14] 刘思琴, 冯胥睿瑞. 基于BERT的文本情感分析[J]. 信息安全研究, 2020, 6(3): 220–227
Liu Siqin, FengXuruirui. Text Sentiment Analysis Based on BERT[J]. Journal of information security reserach, 2020, 6(3): 220–227
[15] 陈红松, 王钢, 宋建林. 基于云计算入侵检测数据集的内网用户异常行为分类算法研究[J]. 信息网络安全, 2018, 18(3): 1–7
CHEN Hongsong, WANG Gang, SONG Jianlin. Research on anomaly behavior classification algorithm of internal network user based on cloud computing intrusion detection data set[J]. Netinfo Security, 2018, 18(3): 1–7
[16] 张建平, 李洪敏, 贾军, 等. 一种基于流量与日志的专网用户行为分析方法[J]. 信息安全研究, 2020, 6(9): 783-790.
ZHANG Jianping, LI Hongming, JIA Jun, et al. A method of user behavior analysis based on network flow and log in private network[J]. Journal of information securyity research, 2020, 6(9): 783-790.
[17] 蹇诗婕 卢志刚 姜波, 等. 基于层次聚类方法的流量异常检测[J]. 信息安全研究, 2020, 6(6): 474–481
SAI Shiyi, LU Zhigang, JING Bo. Flow anomaly detection based on hierarchical clustering method[J]. Journal of information security reserach, 2020, 6(6): 474–481
[18] SUTTON R S. Learning to predict by the methods of temporal differences[J]. Machine learning, 1988, 3(1): 9–44.
- 相似文献/References:
-
[1]李德毅.网络时代人工智能研究与发展[J].智能系统学报,2009,4(1):1.
LI De-yi.AI research and development in the network age[J].CAAI Transactions on Intelligent Systems,2009,4():1.
[2]赵克勤.二元联系数A+Bi的理论基础与基本算法及在人工智能中的应用[J].智能系统学报,2008,3(6):476.
ZHAO Ke-qin.The theoretical basis and basic algorithm of binary connection A+Bi and its application in AI[J].CAAI Transactions on Intelligent Systems,2008,3():476.
[3]徐玉如,庞永杰,甘?? 永,等.智能水下机器人技术展望[J].智能系统学报,2006,1(1):9.
XU Yu-ru,PANG Yong-jie,GAN Yong,et al.AUV—state-of-the-art and prospect[J].CAAI Transactions on Intelligent Systems,2006,1():9.
[4]王志良.人工心理与人工情感[J].智能系统学报,2006,1(1):38.
WANG Zhi-liang.Artificial psychology and artificial emotion[J].CAAI Transactions on Intelligent Systems,2006,1():38.
[5]赵克勤.集对分析的不确定性系统理论在AI中的应用[J].智能系统学报,2006,1(2):16.
ZHAO Ke-qin.The application of uncertainty systems theory of set pair analysis (SPU)in the artificial intelligence[J].CAAI Transactions on Intelligent Systems,2006,1():16.
[6]秦裕林,朱新民,朱? 丹.Herbert Simon在最后几年里的两个研究方向[J].智能系统学报,2006,1(2):11.
QIN Yu-lin,ZHU Xin-min,ZHU Dan.Herbert Simons two research directions in his lost years[J].CAAI Transactions on Intelligent Systems,2006,1():11.
[7]谷文祥,李 丽,李丹丹.规划识别的研究及其应用[J].智能系统学报,2007,2(1):1.
GU Wen-xiang,LI Li,LI Dan-dan.Research and application of plan recognition[J].CAAI Transactions on Intelligent Systems,2007,2():1.
[8]杨春燕,蔡 文.可拓信息-知识-智能形式化体系研究[J].智能系统学报,2007,2(3):8.
YANG Chun-yan,CAI Wen.A formalized system of extension information-knowledge-intelligence[J].CAAI Transactions on Intelligent Systems,2007,2():8.
[9]赵克勤.SPA的同异反系统理论在人工智能研究中的应用[J].智能系统学报,2007,2(5):20.
ZHAO Ke-qin.The application of SPAbased identicaldiscrepancycontrary system theory in artificial intelligence research[J].CAAI Transactions on Intelligent Systems,2007,2():20.
[10]王志良,杨?? 溢,杨?? 扬,等.一种周期时变马尔可夫室内位置预测模型[J].智能系统学报,2009,4(6):521.[doi:10.3969/j.issn.1673-4785.2009.06.009]
WANG Zhi-liang,YANG Yi,YANG Yang,et al.A periodic time-varying Markov model for indoor location prediction[J].CAAI Transactions on Intelligent Systems,2009,4():521.[doi:10.3969/j.issn.1673-4785.2009.06.009]
备注/Memo
收稿日期:2021-04-16。
基金项目:国家电子发展基金项目(工信部财[2014]425 号)
作者简介:喻波，北京明朝万达科技股份有限公司首席科学家、高级副总裁，兼任公安部通信标准化技术委员会委员，主要研究方向为数据安全。主持国家重点研发计划、国家自然科学基金重点项目6项。获吴文俊人工智能科技进步奖一等奖、授权发明专利 120余项;王志海，北京明朝万达科技有限公司董事长、总裁，计算机安全专业委员会常务委员、《信息安全技术》编委会委员，中国大数据生态产业联盟专家委员，国家移动信息产业技术创新战略联盟理事长，主要研究方向为数据安全。获吴文俊人工智能科技进步奖一等奖、授权发明专利120余项，出版专著1部;孙亚东，数据安全解决方案专家，主要研究方向为数据安全。获吴文俊人工智能科技进步奖一等奖、申请国家发明专利13项，授权3项，参与编写数据安全国家标准2项
通讯作者:喻波.E-mail:yubo@wondersoft.cn
更新日期/Last Update:
1900-01-01