5 research outputs found
A Novel Blockchain-based Trust Model for Cloud Identity Management
Secure and reliable management of identities has become one of the greatest
challenges facing cloud computing today, mainly due to the huge number of new
cloud-based applications generated by this model, which means more user
accounts, passwords, and personal information to provision, monitor, and
secure. Currently, identity federation is the most useful solution to overcome
the aforementioned issues and simplify the user experience by allowing
efficient authentication mechanisms and use of identity information from data
distributed across multiple domains. However, this approach creates
considerable complexity in managing trust relationships for both the cloud
service providers and their clients. Poor management of trust in federated
identity management systems brings with it many security, privacy and
interoperability issues, which contributes to the reluctance of organizations
to move their critical identity data to the cloud. In this paper, we aim to
address these issues by introducing a novel trust and identity management model
based on the Blockchain for cloud identity management with security and privacy
improvements
A machine-learning approach to Detect users' suspicious behaviour through the Facebook wall
Facebook represents the current de-facto choice for social media, changing
the nature of social relationships. The increasing amount of personal
information that runs through this platform publicly exposes user behaviour and
social trends, allowing aggregation of data through conventional intelligence
collection techniques such as OSINT (Open Source Intelligence). In this paper,
we propose a new method to detect and diagnose variations in overall Facebook
user psychology through Open Source Intelligence (OSINT) and machine learning
techniques. We are aggregating the spectrum of user sentiments and views by
using N-Games charts, which exhibit noticeable variations over time, validated
through long term collection. We postulate that the proposed approach can be
used by security organisations to understand and evaluate the user psychology,
then use the information to predict insider threats or prevent insider attacks.Comment: 8 page