New approach for threat classification and security risk estimations based on security event management

被引:29
作者
Sancho, Jose Carlos [1 ]
Caro, Andres [1 ]
avila, Mar [1 ]
Bravo, Alberto [1 ]
机构
[1] Univ Extremadura, Dept Comp & Telemat Syst Engn, Av Univ S-N, ES-10003 Caceres, Spain
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2020年 / 113卷
关键词
SIEM; Cybersecurity; STRIDE; Knowledge extraction; Data processing; Bug bar; SYSTEMS;
D O I
10.1016/j.future.2020.07.015
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Security Information and Event Management (SIEM) systems are essential for identifying cyber attacks, being an extended practice in organizations to detect threats, vulnerabilities and to estimate security risks. The management of events and information related to security is done through systems that provide all the information, processing different data sources. The developing of alternative models that provide complementary information to commercial solutions, based on the same data sources, is presented as a novel and interesting challenge, not only for organizations, but also for the scientific community. This paper presents a new system to classify security threats, computing their criticality according to the Bug Bar technique, with the aim of addressing threats in order of priority. High correlations were achieved between severity risk values achieved from commercial systems and results computed by the new approach. Accordingly, the new proposal could complement the information of SIEM systems, and help in the prediction of criticalities of future threats. (c) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码:488 / 505
页数:18
相关论文
共 45 条
[1]  
A.E.S.M. (ESM), SEC INF EV MAN SIEM
[2]  
Abomhara M., 2015, NISK J, V8, P82
[3]  
Al-Duwairi B., 2020, Int. J. Electr. Comput. Eng, V10, DOI [10.11591/ijece.v10i2.pp2182-2191, DOI 10.11591/IJECE.V10I2.PP2182-2191]
[4]  
[Anonymous], Improving web application security: Threats and countermeasures
[5]  
[Anonymous], 2006, SEV ASS THREATS EV V
[6]  
Bouckaert, 2004, WORKING PAPER SERIES
[7]  
Breiman Leo., 2001, Breiman 2001 - Random Forests, P1
[8]   SMOTE: Synthetic minority over-sampling technique [J].
Chawla, Nitesh V. ;
Bowyer, Kevin W. ;
Hall, Lawrence O. ;
Kegelmeyer, W. Philip .
2002, American Association for Artificial Intelligence (16)
[9]  
Chopra M., 2019, INT J INNOV TECHNOL, V8
[10]   Automated root cause identification of security alerts: Evaluation in a SaaS Cloud [J].
Cotroneo, Domenico ;
Paudice, Andrea ;
Pecchia, Antonio .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2016, 56 :375-387