CNN-LSTM Algorithm-Based Insider Threat Detection Model

doi:10.3969/j.issn.1671-1122.2025.02.012

Abstract

Abstract:

The primary information security risks encountered by enterprises and organizations stem from internal threats, particularly malicious behaviors by internal personnel. These risks are inherently more covert and difficult to detect compared to external attacks. To improve the accuracy of detecting malicious behaviors by internal personnel, this study proposed an insider threat detection model based on the CNN-LSTM algorithm, utilizing user behavior log analysis. The model leveraged the publicly available CMU CERT R4.2 insider threat dataset to construct sequences of user behavior features. In this model, a CNN layer was first employed to extract key features from user behavior data, followed by an LSTM layer to capture temporal dependencies and predict behavior patterns. Finally, a fully connected layer is used to determine whether the behavior constitutes a threat. Comparative experiments with CNN, LSTM, and LSTM-CNN models validate the feasibility and superior performance of the proposed model in detecting insider threats, achieving an AUC score of 0.99. The experimental results further demonstrate that the CNN-LSTM algorithm significantly reduces the false positive rate and achieves a detection accuracy of 98%, effectively identifying potential internal threats within organizations.

Key words: insider threat detection, user behavior logs, CNN, LSTM

CLC Number:

TP309

YANG Menghua, YI Junkai, ZHU Hejun. CNN-LSTM Algorithm-Based Insider Threat Detection Model[J]. Netinfo Security, 2025, 25(2): 327-336.

Figures/Tables 11

References 22

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符号	定义
$S=\{{{s}_{1}},{{s}_{2}},\cdots,{{s}_{m}}\}$	内部人员集合，其中${{s}_{i}}$表示某一个内部用户，$m$为内部用户的总数
$X_{S}^{m}=\{{{x}_{1}},{{x}_{2}},\cdots,{{x}_{k}}\}$	用户行为特征序列集合，$k$为特征总数，每个特征${{x}_{i}}(i\in \{1,2,\cdots,k\})$ 表示某一个具体用户的行为属性
$N$	输入数据的批量大小，模型训练迭代中处理的样本数量，在本文实验中，批量大小设置为64
$T$	时间步长，表示输入数据中每个样本包含的时间序列长度，为了捕捉用户行为的时序依赖关系，本文将时间步长T设置为5，表示每个输入样本包含连续5天的用户行为数据

数据类型	内容描述	日志条数/条	备注
logon	用户登录/登出活动	845860	任何特定用户的平均习惯每天都保持一致。一些用户会在非工作时间登录，大多数不会
device	用户使用拇指驱动器活动	405381	用户每天使用拇指驱动器的次数是正常的/平均的
http	用户访问网页获得	28434424	URL中的单词通常与网页主题相关

特征类型	特征编号	特征
Logon	${{x}_{1}}\sim {{x}_{16}}$	每个时间段内用户登录/登出的次数，全天内用户登录/登出的总次数，全天内用户登录/登出的最早/最晚时间，工作时间内用户登录/登出的最早/最晚时间，非工作时间内用户登录/登出的最早/最晚时间
device	${{x}_{17}}\sim {{x}_{32}}$	每个时间段内用户使用可移动设备连接/断开的次数，全天内用户使用可移动设备连接/断开的总次数，全天内用户使用可移动设备连接/断开的最早/最晚时间，工作时间内用户使用可移动设备连接/断开的最早/最晚时间，非工作时间内用户使用可移动设备连接/断开的最早/最晚时间
http	${{x}_{32}}\sim {{x}_{41}}$	每个时间段内用户访问可疑网站的次数，全天内用户访问可疑网站的总次数，全天内用户访问可疑网站的最早/最晚时间，工作时间内用户访问可疑网站的最早/最晚时间，非工作时间内用户访问可疑网站的最早/最晚时间，非工作时间内用户上传文件到可疑网站的次数

模型类别	主要参数	取值
CNN	学习率	0.001
	卷积核个数	32
	卷积核大小	2、3、4
	池化核大小	2x2
	优化函数	Adam
	激活函数	ReLU
	Dropout	0.5
	Dense 单元数	1
	Dense 激活函数	Sigmoid
LSTM	学习率	0.001
	隐藏层神经元数量	128
	LSTM 隐藏层数量	1
	优化函数	Adam
	Dropout	0.5
	Dense 单元数	1
	Dense 激活函数	Sigmoid
CNN-LSTM	学习率	0.001
	卷积核个数	32
	卷积核大小	2、3、4
	池化核大小	2x2
	优化函数	Adam
	Dropout	0.5
	Dense 单元数	1
	Dense 激活函数	Sigmoid

算法	准确率	精确度	召回率
Autoencoder^[20]	92.00%	96.00%	96.00%
DNN^[21]	96.00%	—	—
Random forest^[22]	98.00%	99.32%	93.00%
LSTM-RNN^[11]	94.70%	—	95.90%
CNN-LSTM（本文）	98.00%	99.00%	95.00%