Biblio

The Internet of Things (IoT) market is growing rapidly, allowing continuous evolution of new technologies. Alongside this development, most IoT devices are easy to compromise, as security is often not a prioritized characteristic. This paper proposes a novel IoT Security Model (IoTSM) that can be used by organizations to formulate and implement a strategy for developing end-to-end IoT security. IoTSM is grounded by the Software Assurance Maturity Model (SAMM) framework, however it expands it with new security practices and empirical data gathered from IoT practitioners. Moreover, we generalize the model into a conceptual framework. This approach allows the formal analysis for security in general and evaluates an organization's security practices. Overall, our proposed approach can help researchers, practitioners, and IoT organizations, to discourse about IoT security from an end-to-end perspective.

Preventing unauthorized access to sensitive data is an exceedingly complex access control problem. In this keynote, I will break down the data breach problem and give insights into how organizations could and should do to reduce their risks. The talk will start with discussing the technical reasons behind some of the recent high-profile data breach incidents (e.g., in Equifax, Target), as well as pointing out the threats of inadvertent or accidental data leaks. Then, I will show that there are usually multiple points to stop data breach and give an overview of the relevant state-of-the-art solutions. I will focus on some of the recent algorithmic advances in preventing inadvertent data loss, including set-based and alignment-based screening techniques, outsourced screening, and GPU-based performance acceleration. I will also briefly discuss the role of non-technical factors (e.g., organizational culture on security) in data protection. Because of the cat-and-mouse-game nature of cybersecurity, achieving absolute data security is impossible. However, proactively securing critical data paths through strategic planning and placement of security tools will help reduce the risks. I will also point out a few exciting future research directions, e.g., on data leak detection as a cloud security service and deep learning for reducing false alarms in continuous authentication and the prickly insider-threat detection.