Cryptosystem Identification Scheme Combining Feature Selection and Ensemble Learning
Abstrak
In cyphertext identification,the encryption algorithm is the prerequisite for further analysis of ciphertext.The existing identification schemes are constructed in a single form,and thus often fail to cope with the differences between different cryptosystems when identifying multiple cryptosystems.To address the problem,this paper studies how different ciphertext features influence the performance of identification schemes,then combines the Relief feature selection algorithm and heterogeneous ensemble learning to propose a dynamic feature identification scheme that can adapt to the scenario of multiple cryptosystem identification.Experiments are carried out on ciphertext data sets generated by thirty-six encryption algorithms,and results show that,compared with the existing hierarchical cryptosystem identification schemes based on random forest,the proposed scheme increases the identification accuracy by 6.41%,10.03% and 11.40% respectively in three different cryptosystem identification scenarios.
Topik & Kata Kunci
Penulis (1)
WANG Xu, CHEN Yongle, WANG Qingsheng, CHEN Junjie
Akses Cepat
- Tahun Terbit
- 2021
- Sumber Database
- DOAJ
- DOI
- 10.19678/j.issn.1000-3428.0056918
- Akses
- Open Access ✓