Outputs (4)

Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption (2025)
Journal Article
Buchanan, W. J., & Ali, H. (2025). Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption. Cryptography, 9(2), Article 33. https://doi.org/10.3390/cryptography9020033

The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. To overcome the existing privacy issues, we can apply fully homomorphic encryption (FHE) to encrypt... Read More about Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption.

LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts (2025)
Journal Article
Kumar, G., Saha, R., Conti, M., & Buchanan, W. J. (online). LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts. IEEE Transactions on Services Computing, https://doi.org/10.1109/tsc.2025.3562323

Smart contracts are integral to decentralized systems like blockchains and enable the automation of processes through programmable conditions. However, their immutability, once deployed, poses challenges when addressing errors or bugs. Existing solut... Read More about LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts.

Post-Quantum Migration of the Tor Application (2025)
Journal Article
Berger, D., Lemoudden, M., & Buchanan, W. J. (2025). Post-Quantum Migration of the Tor Application. Journal of Cybersecurity and Privacy, 5(2), Article 13. https://doi.org/10.3390/jcp5020013

The efficiency of Shor's and Grover's algorithms and the advancement of quantum computers implies that the cryptography used until now to protect one's privacy is potentially vulnerable to retrospective decryption, also known as the harvest now, decr... Read More about Post-Quantum Migration of the Tor Application.

Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques (2025)
Journal Article
Konstantinou, A., Kasimatis, D., Buchanan, W. J., Ullah Jan, S., Ahmad, J., Politis, I., & Pitropakis, N. (2025). Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques. Machine Learning and Knowledge Extraction, 7(2), Article 31. https://doi.org/10.3390/make7020031

This paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend... Read More about Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques.