Reasoning Crypto Ransomware Infection Vectors with Bayesian Networks
| Title | Reasoning Crypto Ransomware Infection Vectors with Bayesian Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Zimba, A., Wang, Z., Chen, H. |
| Conference Name | 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) |
| ISBN Number | 978-1-5090-6727-5 |
| Keywords | Bayes methods, Bayesian Network, Bayesian network statistics, belief networks, composability, conditional probability, crypto ransomware, crypto ransomware infection vectors, cryptography, Encryption, Infection Vector, invasive software, Malware, Metrics, Nickel, Payloads, pubcrawl, ransomware, resilience, Resiliency, Uncertainty |
| Abstract | Ransomware techniques have evolved over time with the most resilient attacks making data recovery practically impossible. This has driven countermeasures to shift towards recovery against prevention but in this paper, we model ransomware attacks from an infection vector point of view. We follow the basic infection chain of crypto ransomware and use Bayesian network statistics to infer some of the most common ransomware infection vectors. We also employ the use of attack and sensor nodes to capture uncertainty in the Bayesian network. |
| URL | http://ieeexplore.ieee.org/document/8004894/ |
| DOI | 10.1109/ISI.2017.8004894 |
| Citation Key | zimba_reasoning_2017 |
- Infection Vector
- uncertainty
- Resiliency
- resilience
- Ransomware
- pubcrawl
- Payloads
- Nickel
- Metrics
- malware
- invasive software
- Bayes methods
- encryption
- Cryptography
- crypto ransomware infection vectors
- crypto ransomware
- conditional probability
- composability
- belief networks
- Bayesian network statistics
- Bayesian network