UMBC ebiquity
PhD proposal: Sandeep Nair Narayanan, Cognitive Analytics Framework to Secure Internet of Things

PhD proposal: Sandeep Nair Narayanan, Cognitive Analytics Framework to Secure Internet of Things

Tim Finin, 12:47pm 26 November 2016

cognitive car

Dissertation Proposal

Cognitive Analytics Framework to Secure Internet of Things

Sandeep Nair Narayanan

1:00-3:30pm, Monday, 28 November 2016, ITE 325b

Recent years have seen the rapid growth and widespread adoption of Internet of Things in a wide range of domains including smart homes, healthcare, automotive, smart farming and smart grids. The IoT ecosystem consists of devices like sensors, actuators and control systems connected over heterogeneous networks. The connected devices can be from different vendors with different capabilities in terms of power requirements, processing capabilities, etc. As such, many security features aren’t implemented on devices with lesser processing capabilities. The level of security practices followed during their development can also be different. Lack of over the air update for firmware also pose a very big security threat considering their long-term deployment requirements. Device malfunctioning is yet another threat which should be considered. Hence, it is imperative to have an external entity which monitors the ecosystem and detect attacks and anomalies.

In this thesis, we propose a security framework for IoTs using cognitive techniques. While anomaly detection has been employed in various domains, some challenges like online approach, resource constraints, heterogeneity, distributed data collection etc. are unique to IoTs and their predecessors like wireless sensor networks. Our framework will have an underlying knowledge base which has the domain-specific information, a hybrid context generation module which generates complex contexts and a fast reasoning engine which does logical reasoning to detect anomalous activities. When raw sensor data arrives, the hybrid context generation module queries the knowledge base and generates different simple local contexts using various statistical and machine learning models. The inferencing engine will then infer global complex contexts and detects anomalous activities using knowledge from streaming facts and and domain specific rules encoded in the Ontology we will create. We will evaluate our techniques by realizing and validating them in the vehicular domain.

Committee: Drs. Dr. Anupam Joshi (Chair), Dr. Tim Finin, Dr. Nilanjan Banerjee, Dr. Yelena Yesha, Dr. Wenjia Li, NYIT, Dr. Filip Perich, Google

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