Name
Abstract | ||
|---|---|---|
The growing targeted incidents constitute a permanent menace to internal security with more frequent data breach and service interruption events nowadays. Attackers move laterally and reside in the internal systems for accessing valuable information continuously. This paper proposed a novel approach based on network embedding synthesizing the information on hosts, traffics and correlations to forestall further loss under a deliberate attack. The approach begins with constructing a host communication graph with features extracted from the original data recorded in the internal network and the topology structures of the constructed network. Inspired by previous works, the approach uses a features aggregation learning method, that is composing the features on vertices and edges with neighbors' features to aggregate new composite features. Then the features are selected and learned iteratively. The ultimately selected features were reduced to lower dimension for training and used for the malicious host classification task. Besides, the approach can reemployment the classification results to optimize the dimensionality reduction. Compared with the state-of-the-art models and methods, the proposed approach is (i) flexible with multiple types of data and exchangeable methods, (ii) accurate in feature learning, selecting and extracting with the remarkably average accuracy of 99.9% and the average precision of 91.3%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BDCloud.2018.00107 | 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) |
Keywords | Field | DocType |
lateral movement,internal threat,network embedding,semi supervised | Data mining,Graph,Dimensionality reduction,Lateral movement,Vertex (geometry),Computer science,Human–computer interaction,Data type,Network embedding,Data breach,Feature learning | Conference |
ISSN | ISBN | Citations |
2158-9178 | 978-1-7281-1141-4 | 3 |
PageRank | References | Authors |
0.42 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyi Chen | 1 | 3 | 2.45 |
| Yepeng Yao | 2 | 5 | 2.82 |
| Junrong Liu | 3 | 10 | 2.35 |
| Bo Jiang | 4 | 18 | 11.25 |
| Liya Su | 5 | 4 | 2.80 |
| Zhigang Lu | 6 | 10 | 6.68 |