Insider threat detection based on operational attention and data augmentation

Guanyun FENG; Cai FU; Jianqiang LYU; Lansheng HAN

doi:10.11959/j.issn.2096-109x.2023042

您当前的位置：

首页 >

文章列表页 >

Insider threat detection based on operational attention and data augmentation

Papers | 更新时间：2024-06-03

- Insider threat detection based on operational attention and data augmentation
- Chinese Journal of Network and Information Security Vol. 9, Issue 3, Pages: 102-112(2023)
- 作者机构：
  
  1. 分布式系统安全湖北省重点实验室，湖北省大数据安全工程技术研究中心，湖北武汉 430074
  2. 华中科技大学网络空间安全学院，湖北武汉 430074
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023042
  CLC： TP309.2
- Online First：2023-06，
  
  Published：25 June 2023
- 稿件说明：
移动端阅览
Guanyun FENG, Cai FU, Jianqiang LYU, et al. Insider threat detection based on operational attention and data augmentation[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 102-112.
DOI：

Guanyun FENG, Cai FU, Jianqiang LYU, et al. Insider threat detection based on operational attention and data augmentation[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 102-112. DOI： 10.11959/j.issn.2096-109x.2023042.

摘要

内部威胁是组织中出现重大安全隐患的主要原因之一，也是一个长期的挑战。通过分析现有的内部威胁数据

指出内部威胁检测最大的挑战在于数据不平衡、有标注的威胁样本少。内部威胁检测的经典数据集CMU-C R4.2共有322万条日志数据，其中标记出的恶意操作日志仅7 423条；日志中的大多数操作类型与恶意行为无关，如泄露企业数据这一恶意行为仅与两种类型操作高度相关，而其余的 40 多种类型操作的日志则可能对检测造成干扰。针对这一挑战，设计了一种基于操作注意力和数据增强的数据处理框架。该框架首先对操作进行异常评估，对低异常评分的操作进行掩码操作，使模型更好地关注与恶意行为相关的操作，可以被认为是一种操作的硬注意力机制。通过分析内部威胁数据集的特点，设计了3种规则对恶意样本进行数据增强，以增加样本的多样性和缓解正负样本严重不平衡的问题。将有监督的内部威胁检测视作一个时序分类问题，在长短期记忆卷积神经网络（LSTM-FCN）模型中加入残差连接以实现多粒度的检测，并使用精确率、召回率等指标实施评估，要优于现有的基线模型；另外，在 ITD-Bert、TextCNN 等多种经典模型上实施基于操作注意力和数据增强的数据处理框架，结果表明所提方法能够有效提升内部威胁检测模型的性能。

Abstract

In recent years

there has been an increased focus on the issue of insider threats.Insider threats are a major cause security breaches in organizations and pose an ongoing challenge.By analyzing the existing insider threat data

it was identified that the biggest challenge in insider threat detection lies in data imbalance and the limited number of labeled threat samples.In the Cert R4.2 dataset

which is a classic dataset for insider threat detection

there are over 3.22 million log data

but only 7

423 are marked as malicious operation logs.Furthermore

most of the operation types in the logs are not related to malicious behavior

and only two types of operations are highly correlated with malicious behavior

such as leaking company data

creating interference in the detection process.To address this challenge

a data processing framework was designed based on operational attention and data augmentation.Anomaly evaluation was first performed on operations by the framework

and operations with low anomaly scores were then masked.This makes the model better focus on operations related to malicious behavior

which can be considered as a hard attention mechanism for operations.Next

the characteristics of the insider threat dataset were analyzed

and three rules were designed for data augmentation on malicious samples to increase the diversity of samples and alleviate the substantial imbalance between positive and negative samples.Supervised insider threat detection was regarded as a time-series classification problem.Residual connections were added to the LSTM-FCN model to achieve multi-granularity detection

and indicators such as precision rate and recall rate were used to evaluate the model.The results indicate superior performance over existing baseline models.Moreover

the data processing framework was implemented on various classic models

such as ITD-Bert and TextCNN

and the results show that the methods effectively improve the performance of insider threat detection models.

关键词

Keywords

references

HOMOLIAK I , TOFFALINI F , GUARNIZO J , et al . Insight into insiders and IT:a survey of insider threat taxonomies,analysis,modeling,and countermeasures [J ] . ACM Computing Surveys , 2020 , 52 ( 2 ): 1 - 40 .

Insider threat 2022 report [EB ] .

Gurucul - insider threat survey report [EB ] .

Insider threat report 2023 [EB ] .

YUAN S H , WU X T . Deep learning for insider threat detection:review,challenges and opportunities [J ] . Computers ＆ Security , 2021 ,104:102221.

LIU L , DE VEL O , HAN Q L , et al . Detecting and preventing cyber insider threats:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2018 , 20 ( 2 ): 1397 - 1417 .

MONTANO I H , ARANDA J J G , DIAZ J R , et al . Survey of techniques on data leakage protection and methods to address the insider threat [J ] . Cluster Computing , 2022 , 25 ( 6 ): 4289 - 4302 .

XU J H , WU H X , WANG J M , et al . Anomaly transformer:time series anomaly detection with association discrepancy [EB/OL ] . 2021 :arXiv:2110.02642.

Cmu-cert insider threat test dataset [EB ] .

TUOR A , KAPLAN S , HUTCHINSON B , et al . Deep learning for unsupervised insider threat detection in structured cybersecurity data streams [C ] // Proceeding of Workshops of the Thirty-First AAAI Conference on Artificial Intelligence . 2017 .

LIU L , DE VEL O , CHEN C , et al . Anomaly-based insider threat detection using deep autoencoders [C ] // Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW) . 2019 : 39 - 48 .

SHARMA B , POKHAREL P , JOSHI B . User behavior analytics for anomaly detection using LSTM autoencoder-insider threat detection [C ] // Proceedings of the 11th International Conference on Advances in Information Technology . 2020 : 1 - 9 .

LIU L , CHEN C , ZHANG J , et al . Insider threat identification using the simultaneous neural learning of multi-source logs [J ] . IEEE Access , 2019 , 7 : 183162 - 183176 .

SINGH M , MEHTRE B , SANGEETHA S . User behavior based insider threat detection using a multi fuzzy classifier [J ] . Multimedia Tools and Applications , 2022 , 81 ( 16 ): 22953 - 22983 .

JIANG J G , CHEN J M , GU T B , et al . Warder:online insider threat detection system using multi-feature modeling and graph-based correlation [C ] // Proceedings of MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) . 2020 : 1 - 6 .

LIU C C , ZHONG Y , WANG Y L . Improved detection of user malicious behavior through log mining based on IHMM [C ] // Proceedings of 2018 5th International Conference on Systems and Informatics (ICSAI) . 2019 : 1193 - 1198 .

LYU B , WANG D , WANG Y , et al . A hybrid model based on multi-dimensional features for insider threat detection [C ] // Proceedings of International Conference on Wireless Algorithms,Systems,and Applications . 2018 : 333 - 344 .

NASSER M , AL-MHIQANI . A new intelligent multilayer framework for insider threat detection [J ] . Computers ＆ Electrical Engineering , 2022 ,97:107597.

GAYATHRI R G , SAJJANHAR A , XIANG Y . Image-based feature representation for insider threat classification [J ] . Applied Sciences , 2020 , 10 ( 14 ): 4945 .

CHATTOPADHYAY P , WANG L P , TAN Y P . Scenario-based insider threat detection from cyber activities [J ] . IEEE Transactions on Computational Social Systems , 2018 , 5 ( 3 ): 660 - 675 .

NASIR R , AFZAL M , LATIF R , et al . Behavioral based insider threat detection using deep learning [J ] . IEEE Access , 2021 , 9 : 143266 - 143274 .

MENG F Z , LOU F , FU Y S , et al . Deep learning based attribute classification insider threat detection for data security [C ] // Proceedings of 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) . 2018 : 576 - 581 .

JIANG W , TIAN Y , LIU W X , et al . An insider threat detection method based on user behavior analysis [C ] // International Conference on Intelligent Information Processing . 2018 : 421 - 429 .

LI D Y , YANG L , ZHANG H G , et al . Image-based insider threat detection via geometric transformation [J ] . Security and Communication Networks , 2021 : 1 - 18 .

HUANG W Q , ZHU H , LI C , et al . ITDBERT:temporal-semantic representation for insider threat detection [C ] // Proceedings of 2021 IEEE Symposium on Computers and Communications (ISCC) . 2021 : 1 - 7 .

LEGG P A , BUCKLEY O , GOLDSMITH M , et al . Automated insider threat detection system using user and role-based profile assessment [J ] . IEEE Systems Journal , 2017 , 11 ( 2 ): 503 - 512 .

JIANG J G , CHEN J M , GU T B , et al . Anomaly detection with graph convolutional networks for insider threat and fraud detection [C ] // Proceedings of MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) . 2020 : 109 - 114 .

ABEDINIA O , AMJADY N , ZAREIPOUR H . A new feature selection technique for load and price forecast of electrical power systems [J ] . IEEE Transactions on Power Systems , 2017 , 32 ( 1 ): 62 - 74 .

XU K , BA J L , KIROS R , et al . Show,attend and tell:neural image caption generation with visual attention [C ] // Proceedings of the 32nd International Conference on International Conference on Machine Learning . 2015 : 2048 - 2057 .

JADERBERG M , SIMONYAN K , ZISSERMAN A , et al . Spatial transformer networks [J ] . arXiv Preprint arXiv:1506.02025 , 2015 .

CHENG X , LI X , YANG J , et al . SESR:single image super resolution with recursive squeeze and excitation networks [C ] // Proceedings of 2018 24th International Conference on Pattern Recognition (ICPR) . 2018 : 147 - 152 .

DOSOVITSKIY A , BEYER L , KOLESNIKOV A , et al . An image is worth 16x16 words:transformers for image recognition at scale [K ] . arXiv Preprint arXiv:2010.11929 , 2020 .

LIU Z , LIN Y T , CAO Y , et al . Swin transformer:hierarchical vision transformer using shifted windows [C ] // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . 2022 : 9992 - 10002 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . 2017 : 6000 - 6010 .

SUN F , LIU J , WU J , et al . BERT4Rec:sequential recommendation with bidirectional encoder representations from transformer [C ] // Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 2019 : 1441 - 1450 .

MNIH V , HEESS N , GRAVES A , et al . Recurrent models of visual attention [J ] . arXiv Preprint arXiv:1406.6247 , 2014 .

ZHAO B , WU X , FENG J S , et al . Diversified visual attention networks for fine-grained object classification [J ] . IEEE Transactions on Multimedia , 2017 , 19 ( 6 ): 1245 - 1256 .

STOLLENGA M F , MASCI J , GOMEZ F , et al . Deep networks with internal selective attention through feedback connections [C ] // Proceedings of the 27th International Conference on Neural Information Processing Systems . 2014 : 3545 - 3553 .

ELSAYED G F , KORNBLITH S , LE Q V . Saccader:improving accuracy of hard attention models for vision [J ] . arXiv Preprint arXiv:1908.07644 , 2019 .

ANDERSON P , HE X D , BUEHLER C , et al . Bottom-up and top-down attention for image captioning and visual question answering [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2018 : 6077 - 6086 .

Views

320

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

DynaSynth: a large language model fine-tuning data dynamic synthesis agent tool for unstructured cybersecurity corpus

Data augmentation based multi-view contrastive learning graph anomaly detection

Pseudo-siamese network image tampering localization model based on reinforced samples

Malware detection approach based on improved SOINN

Neural network-based link prediction algorithm

Related Author

Zhao Zhengyang

Li Siqi

Zhao Bofu

Wang Buhong

Piao Chengkai

Guan Yongjian

DAI Yuanfei

LI Yifan

Related Institution

Information and Navigation College, Air Force Engineering University

College of Computer and Cyber Security, Fujian Normal University

Fujian Provincial Key Laboratory of Network Security and Cryptology

College of Computer and Information Engineering, Nanjing University of Technology

Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology

⁰