A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
AES, ElGamal are two examples of algorithms that have been developed in cryptography to protect data in a variety of domains including native and cloud systems, and mobile applications. There has been a good deal of research into the use of side channel attacks on these algorithms. This work has conducted an experiment to detect malicious loops inside Flush+Reload and Prime+Prob attack programs against AES through the exploitation of Hardware Performance Counters (HPC). This paper examines the accuracy and eciency of three machine learning algorithms: Neural Network (NN); Decision Tree C4.5; and K Nearest Neighbours (KNN). The study also shows how Standard Performance Evaluation Corporation (SPEC) CPU2006 benchmarks impact predictions.
| Original language | English |
|---|---|
| Title of host publication | Advances in Intelligent Systems and Computing |
| Editors | Fei Chao, Steven Schockaert, Qingfu Zhang |
| Publisher | Springer |
| Pages | 203-213 |
| Number of pages | 11 |
| ISBN (Electronic) | 978-319669397 |
| ISBN (Print) | 978-3319669380 |
| DOIs | |
| Publication status | Published - Sep 2017 |
| Event | The 17th UK Workshop on Computational Intelligence - Cardiff University, Cardiff, United Kingdom Duration: 6 Sep 2017 → 8 Sep 2017 |
Publication series
| Name | Advances in Intelligent Systems and Computing (AISC) |
|---|---|
| Publisher | Springer, Cham |
| Volume | 650 |
| ISSN (Print) | 2194-5357 |
| ISSN (Electronic) | 2194-5365 |
Conference
| Conference | The 17th UK Workshop on Computational Intelligence |
|---|---|
| Abbreviated title | UKCI 2017 |
| Country | United Kingdom |
| City | Cardiff |
| Period | 6/09/17 → 8/09/17 |
Documents
- UKCI_2017_PAPER
Rights statement: This is a pre-copyedited version of a contribution published in Advances in Computational Intelligence Systems. UKCI 2017. Fei Chao, Steven Schockaert, Qingfu Zhang (eds). Published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-66939-7_17.
Accepted author manuscript (Post-print), 379 KB, PDF document
Related information
ID: 7574487
