Network Security Situation Awareness Based on Phishing Detection

Jian Yi Zhang; Cheng Gen Song; Xin Jin

doi:10.4028/www.scientific.net/AMM.644-650.2784

Paper Titles

An Improved Scheme of One-Time Password Identity Authentication Based on the S/KEY System
p.2763

The New Key-Stream Generator Based on the OFB Mode of AES
p.2768

Collaboration Research on Web 2.0
p.2772

An Online E-Payment System Applying to Auto Insurance Based on Proxy Blind Signature
p.2776

Network Security Situation Awareness Based on Phishing Detection
p.2784

Supermarket Trolley Positioning System Based on ZigBee
p.2788

Study of Web Service Discovery Algorithm Based on Semantic
p.2793

Research on Usage Intention of Community Information System
p.2797

Discussion on the Application of Networking Technology in Intelligent Campus Construction
p.2804

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Network Security Situation Awareness Based on...

Network Security Situation Awareness Based on Phishing Detection

Abstract:

In this paper, we introduce a statistical machine learning classifier and a LSH page similarity detector as the network security situation awareness mechanism to detect the spear phishing that has been widely used in the Advanced Persistent Threats. Then, a number of comprehensive experiments show that our proposed method achieves high accuracy over a balanced dataset. The accuracy is no less than 92% while the recall is more than 97%.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

2784-2787

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.2784

Citation:

Cite this paper

Online since:

September 2014

Authors:

Jian Yi Zhang*, Cheng Gen Song, Xin Jin

Keywords:

Advanced Persistent Threats, Network Security Situation Awareness, Phishing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Chen XZ etc., Quantitative hierarchical threat evaluation model for network security, Journal of Software, 2006, 17(4): pp.885-897.

Google Scholar

[2] Wei Hu etc., A Novel Approach to Cyberspace Security Situation Based on the Vulnerabilities Analysis, in Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, (2006).

DOI: 10.1109/wcica.2006.1713284

Google Scholar

[3] Lau S. The spinning cube of potential doom. Communications of the ACM, 2004, 47(6): 25-26.

DOI: 10.1145/990680.990699

Google Scholar

[4] Kiran Lakkaraju, W. Yurcik, Adam J Lee. NVisionIP: netflow visualizations of system state for security situational awareness, in Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, 2004: pp.65-72.

DOI: 10.1145/1029208.1029219

Google Scholar

[5] T. Bass, Intrusion Detection Systems and Multisensor Data Fusion: Creating Cyberspace Situational Awareness. Communications of the ACM, 1999. 43(4): pp.99-105.

DOI: 10.1145/332051.332079

Google Scholar

[6] Jianyi Zhang, etc., A Real-time Automatic Detection of Phishing URLs. in Proceedings of Computer Science and Network Technology, 2012. pp.1212-1216.

Google Scholar

[7] Rasmussen, R. and G. Aaron (Nov 2011) Global Phishing Survey 1H2011. Anti-Phishing Working Group.

Google Scholar

[8] Garera, S., N. Provos, M. Chew, and A.D. Rubin. A Framework for Detection and Measurement of Phishing Attacks, in Proceedings of the 2007 ACM Workshop On Recurring Malcode, Alexandria, Virginia, USA, ACM, pp.1-8, (2007).

DOI: 10.1145/1314389.1314391

Google Scholar

[9] Yuan, G.X., C.H. Ho, and C.J. Lin. An Improved GLMNET for L1-regularized Logistic Regression, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2011).

DOI: 10.1145/2020408.2020421

Google Scholar