HARMer: Cyber-attacks Automation and Evaluation

Autor: Myung Kil Ahn, Donghwan Lee, Dong Seong Kim, Simon Yusuf Enoch, Zhibin Huang, Chun Yong Moon
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Attack automation
Computer Science - Cryptography and Security
cybersecurity
General Computer Science
Computer science
media_common.quotation_subject
0211 other engineering and technologies
Vulnerability
blue team
02 engineering and technology
Computer security
computer.software_genre
offensive security
Data modeling
penetration testing
attack planning
0202 electrical engineering
electronic engineering
information engineering
Enterprise private network
General Materials Science
Quality (business)
Representation (mathematics)
media_common
021110 strategic
defence & security studies
business.industry
General Engineering
020206 networking & telecommunications
Computer security model
Automation
Scalability
Key (cryptography)
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
computer
Cryptography and Security (cs.CR)
Zdroj: IEEE Access, Vol 8, Pp 129397-129414 (2020)
Popis: With the increasing growth of cyber-attack incidences, it is important to develop innovative and effective techniques to assess and defend networked systems against cyber attacks. One of the well-known techniques for this is performing penetration testing which is carried by a group of security professionals (i.e, red team). Penetration testing is also known to be effective to find existing and new vulnerabilities, however, the quality of security assessment can be depending on the quality of the red team members and their time and devotion to the penetration testing. In this paper, we propose a novel automation framework for cyber-attacks generation named `HARMer' to address the challenges with respect to manual attack execution by the red team. Our novel proposed framework, design, and implementation is based on a scalable graphical security model called Hierarchical Attack Representation Model (HARM). (1) We propose the requirements and the key phases for the automation framework. (2) We propose security metrics-based attack planning strategies along with their algorithms. (3) We conduct experiments in a real enterprise network and Amazon Web Services. The results show how the different phases of the framework interact to model the attackers' operations. This framework will allow security administrators to automatically assess the impact of various threats and attacks in an automated manner.
19 pages, journal
Databáze: OpenAIRE
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