scholarly journals Using Graph Representation in Host-Based Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhichao Hu ◽  
Likun Liu ◽  
Haining Yu ◽  
Xiangzhan Yu
Keyword(s):  
Intrusion Detection ◽  
Representation Learning ◽  
Graph Representation ◽  
Daily Lives ◽  
Embedding Method ◽  
System Calls ◽  
Average Improvement ◽  
Wide Range ◽  
Structural Association ◽  
N Gram

Cybersecurity has become an important part of our daily lives. As an important part, there are many researches on intrusion detection based on host system call in recent years. Compared to sentences, a sequence of system calls has unique characteristics. It contains implicit pattern relationships that are less sensitive to the order of occurrence and that have less impact on the classification results when the frequency of system calls varies slightly. There are also various properties such as resource consumption, execution time, predefined rules, and empirical weights of system calls. Commonly used word embedding methods, such as Bow, TI-IDF, N-Gram, and Word2Vec, do not fully exploit such relationships in sequences as well as conveniently support attribute expansion. To solve these problems, we introduce Graph Representation based Intrusion Detection (GRID), an intrusion detection framework based on graph representation learning. It captures the potential relationships between system calls to learn better features, and it is applicable to a wide range of back-end classifiers. GRID utilizes a new sequence embedding method Graph Random State Embedding (GRSE) that uses graph structures to model a finite number of sequence items and represent the structural association relationships between them. A more efficient representation of sequence embeddings is generated by random walks, word embeddings, and graph pooling. Moreover, it can be easily extended to sequences with attributes. Our experimental results on the AFDA-LD dataset show that GRID has an average improvement of 2% using the GRSE embedding method comparing to others.

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

Possession and Repetition

2012 ◽  
Vol 6 (1-3) ◽  
pp. 243-259 ◽  
Author(s):  
Yohan Yoo
Keyword(s):  
First Century ◽  
Daily Lives ◽  
Modern Times ◽  
Sacred Texts ◽  
New Methods ◽  
Wide Range ◽  
Lay Buddhists ◽  
Iconic Status ◽  
And Function ◽  
To Receive

This article demonstrates the need for the iconic status and function of Buddhist scripture to receive more attention by illuminating how lay Korean Buddhists try to appropriate the power of sutras. The oral and aural aspects of scripture, explained by Wilfred Cantwell Smith, provide only a limited understanding of the characteristics of scripture. It should be noted that, before modern times, most lay people, not only in Buddhist cultures but also in Christian and other traditions, neither had the chance to recite scriptures nor to listen to their recitations regularly. Several clear examples demonstrate contemporary Korean Buddhists’ acceptance of the iconic status of sutras and their attempt to appropriate the power and status of those sacred texts. In contemporary Korea, lay Buddhists try to claim the power of scriptures in their daily lives by repeating and possessing them. Twenty-first century lay believers who cannot read or recite in a traditional style have found new methods of repetition, such as internet programs for copying sacred texts and for playing recordings of their recitations. In addition, many Korean Buddhists consider the act of having sutras in one’s possession to be an effective way of accessing the sacred status and power of these texts. Hence, various ways of possessing them have been developed in a wide range of products, from fancy gilded sutras to sneakers embroidered with mantras.

Antineoplastic Activity, Structural Modification, Synthesis and Structure-activity Relationship of Dammarane-type Ginsenosides: An Overview

2019 ◽  
Vol 23 (5) ◽  
pp. 503-516 ◽  
Author(s):  
Qiang Zhang ◽  
Xude Wang ◽  
Liyan Lv ◽  
Guangyue Su ◽  
Yuqing Zhao
Keyword(s):  
Structural Modification ◽  
Gastrointestinal Disease ◽  
Structure Activity Relationship ◽  
Cerebrovascular Diseases ◽  
Activity Relationship ◽  
Cycle Arrest ◽  
Structure Activity ◽  
Wide Range ◽  
Structural Association ◽  
Relationship Of

Dammarane-type ginsenosides are a class of tetracyclic triterpenoids with the same dammarane skeleton. These compounds have a wide range of pharmaceutical applications for neoplasms, diabetes mellitus and other metabolic syndromes, hyperlipidemia, cardiovascular and cerebrovascular diseases, aging, neurodegenerative disease, bone disease, liver disease, kidney disease, gastrointestinal disease and other conditions. In order to develop new antineoplastic drugs, it is necessary to improve the bioactivity, solubility and bioavailability, and illuminate the mechanism of action of these compounds. A large number of ginsenosides and their derivatives have been separated from certain herbs or synthesized, and tested in various experiments, such as anti-proliferation, induction of apoptosis, cell cycle arrest and cancer-involved signaling pathways. In this review, we have summarized the progress in structural modification, shed light on the structure-activity relationship (SAR), and offered insights into biosynthesis-structural association. This review is expected to provide a preliminary guide for the modification and synthesis of ginsenosides.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals Graph Representation Learning for Single Cell Biology

2021 ◽  
Author(s):  
Leon Hetzel ◽  
David S. Fischer ◽  
Stephan Günnemann ◽  
Fabian J. Theis
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Representation Learning ◽  
Graph Representation

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

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