Dataset for Machine Learning Based Cache Timing Attacks and Mitigation

Cache side-channel attacks have evolved alongside increasingly complex microprocessor architectural designs. The attacks and their prevention mechanisms, such as cache partitioning, OS kernel isolation, and various hardware/operating system enhancements, have similarly progressed. Nonetheless, side-channel attacks necessitate effective and efficient prevention mechanisms or alterations to hardware architecture. Recently, machine learning (ML) is an emerging method for detecting and defending such attacks. However, The effectiveness of machine learning relies on the dataset it is trained on. The datasets for training these ML models today are not vast enough to enhance the robustness and consistency of the model performance. This thesis aims to enhance the ML method for exploring various cache side-channel attacks and defenses by offering a more reasonable and potentially realistic dataset to distinguish between the attacker and the victim process. The dataset is gathered through a computer system simulation model, which is subsequently utilized to train both the attacker and detector agents of the model. Different ways to collect datasets using the system simulation are explored. A New Dataset for training and detecting cache side-channel attacks is also explored and methodized. Lastly, the effectiveness of the dataset is studied by training a Flush+Reload attacker and detector model performance.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Kalidasan, Vishnu Kumar

Chair dc:contributor.committeechair

• Xiong, Wenjie

Committee members dc:contributor.committeemember

• Nazhandali, Leyla
• Min, Chang Woo

• Cache timing-attack
• Cache side-channel
• gem5
• Autocat
• Macta

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40765

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119312