  In many large scale distributed systems and on the web, agents need to
interact with other unknown agents to carry out some tasks or transactions. The
ability to reason about and assess the potential risks in carrying out such
transactions is essential for providing a safe and reliable environment. A
traditional approach to reason about the trustworthiness of a transaction is to
determine the trustworthiness of the specific agent involved, derived from the
history of its behavior. As a departure from such traditional trust models, we
propose a generic, machine learning approach based trust framework where an
agent uses its own previous transactions (with other agents) to build a
knowledge base, and utilize this to assess the trustworthiness of a transaction
based on associated features, which are capable of distinguishing successful
transactions from unsuccessful ones. These features are harnessed using
appropriate machine learning algorithms to extract relationships between the
potential transaction and previous transactions. The trace driven experiments
using real auction dataset show that this approach provides good accuracy and
is highly efficient compared to other trust mechanisms, especially when
historical information of the specific agent is rare, incomplete or inaccurate.

Distributed, Parallel, and Cluster Computing;Cryptography and Security;Machine Learning