Selecting System Specific Cybersecurity Attack Patterns Using Topic Modeling
| Title | Selecting System Specific Cybersecurity Attack Patterns Using Topic Modeling |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Adams, S., Carter, B., Fleming, C., Beling, P. A. |
| Conference Name | 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) |
| Date Published | aug |
| ISBN Number | 978-1-5386-4388-4 |
| Keywords | attack topic distribution, capec, CAPEC database, common attack pattern enumeration, common attack pattern enumeration and classification database, computer security, Cyber-physical systems, cybersecurity expert, Data models, Databases, expert systems, human factors, KL divergence, Mathematical model, natural language processing, pattern classification, posterior distribution, privacy, pubcrawl, ranking method, Scalability, security, security of data, statistical distributions, system, system specific cybersecurity attack patterns, topic modeling |
| Abstract | One challenge for cybersecurity experts is deciding which type of attack would be successful against the system they wish to protect. Often, this challenge is addressed in an ad hoc fashion and is highly dependent upon the skill and knowledge base of the expert. In this study, we present a method for automatically ranking attack patterns in the Common Attack Pattern Enumeration and Classification (CAPEC) database for a given system. This ranking method is intended to produce suggested attacks to be evaluated by a cybersecurity expert and not a definitive ranking of the "best" attacks. The proposed method uses topic modeling to extract hidden topics from the textual description of each attack pattern and learn the parameters of a topic model. The posterior distribution of topics for the system is estimated using the model and any provided text. Attack patterns are ranked by measuring the distance between each attack topic distribution and the topic distribution of the system using KL divergence. |
| URL | https://ieeexplore.ieee.org/document/8455944 |
| DOI | 10.1109/TrustCom/BigDataSE.2018.00076 |
| Citation Key | adams_selecting_2018 |
- Mathematical model
- topic modeling
- system specific cybersecurity attack patterns
- system
- statistical distributions
- security of data
- security
- Scalability
- ranking method
- pubcrawl
- privacy
- posterior distribution
- pattern classification
- natural language processing
- attack topic distribution
- KL divergence
- Human Factors
- expert systems
- Databases
- Data models
- cybersecurity expert
- cyber-physical systems
- computer security
- common attack pattern enumeration and classification database
- common attack pattern enumeration
- CAPEC database
- capec