Insider Threat Detection Based on Deep Belief Network Feature Representation

2017 ◽  
Author(s):  
Lingli Lin ◽  
Shangping Zhong ◽  
Cunmin Jia ◽  
Kaizhi Chen
Keyword(s):  
Deep Belief Network ◽  
Feature Representation ◽  
Insider Threat ◽  
Threat Detection ◽  
Belief Network ◽  
Network Feature

scholarly journals Deep Belief Network Approach for Recognition of Cow using Cow Nose Image Pattern

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Rotimi-Williams BELLO ◽  
Abdullah Zawawi Hj TALIB ◽  
Ahmad Sufril Azlan Bin MOHAMED
Keyword(s):  
Texture Features ◽  
Deep Belief Network ◽  
Feature Representation ◽  
Image Texture ◽  
Belief Network ◽  
Animal Biometrics ◽  
Network Application ◽  
Identification Techniques ◽  
Deep Learning Model ◽  
Image Pattern

A deep belief network is proposed to learn the discriminatory cow nose image texture features for a robust representation of cows' features and recognition using a cow nose image pattern. Deep belief network is a deep learning model that is graphically based, and it is applied to learn the extracted feature sets of cow nose image pattern for hierarchical representation by using the training details of the training phase of the system proposed. Deep belief network application is useful in animal biometrics to monitor the animals through its recognition and identification techniques. Biometrics application emanated from computer vision and pattern recognition. Its application plays an important role in registering and monitoring animals through its recognition and identification techniques. Because the existing physical-based feature representation methods and manual visual feature extractions cannot handle animal recognition, the deep belief network technique is proposed using the animal's visual attributes. An experiment performed under a controlled condition of identification indicated that the proposed method outshines the existing methods with approximately 98.99 % accuracy. Four thousand cow nose images from an existing database of 400 individual cows contribute to the community of research, especially in the animal biometrics for identification of individual cow.

scholarly journals Image-Based Insider Threat Detection via Geometric Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Dongyang Li ◽  
Lin Yang ◽  
Hongguang Zhang ◽  
Xiaolei Wang ◽  
Linru Ma ◽  
...  
Keyword(s):  
User Behavior ◽  
Characteristic Curve ◽  
Feature Representation ◽  
Geometric Transformation ◽  
Insider Threat ◽  
Threat Detection ◽  
Geometric Transformations ◽  
Latent Features ◽  
Supervised Image Classification ◽  
Grayscale Images

Insider threat detection has been a challenging task over decades; existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as anomalies. However, such approaches are insufficient in precision and computational complexity. In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised image classification task, and therefore the performance can be boosted via computer vision techniques. To illustrate, our IGT uses a novel image-based feature representation of user behavior by transforming audit logs into grayscale images. By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner. The motivation behind our proposed method is that images converted from normal behavior data may contain unique latent features which remain unchanged after geometric transformation, while malicious ones cannot. Experimental results on CERT dataset show that IGT outperforms the classical autoencoder-based unsupervised insider threat detection approaches, and improves the instance and user based Area under the Receiver Operating Characteristic Curve (AUROC) by 4% and 2%, respectively.

Software Defect Prediction via Deep Belief Network

2019 ◽  
Vol 28 (5) ◽  
pp. 925-932
Author(s):  
Hua WEI ◽  
Chun SHAN ◽  
Changzhen HU ◽  
Yu ZHANG ◽  
Xiao YU
Keyword(s):  
Deep Belief Network ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Belief Network ◽  
Software Defect
Sign in / Sign up

Export Citation Format

Share Document