Survey and Systematization of Secure Device Pairing

Secure Device Pairing (SDP) schemes have been developed to facilitate secure communications among smart devices, both personal mobile devices and Internet of Things (IoT) devices. Comparison and assessment of SDP schemes is troublesome, because each scheme makes different assumptions about out-of-band channels and adversary models, and are driven by their particular use-cases. A conceptual model that facilitates meaningful comparison among SDP schemes is missing. We provide such a model. In this article, we survey and analyze a wide range of SDP schemes that are described in the literature, including a number that have been adopted as standards. A system model and consistent terminology for SDP schemes are built on the foundation of this survey, which are then used to classify existing SDP schemes into a taxonomy that, for the first time, enables their meaningful comparison and analysis.The existing SDP schemes are analyzed using this model, revealing common systemic security weaknesses among the surveyed SDP schemes that should become priority areas for future SDP research, such as improving the integration of privacy requirements into the design of SDP schemes. Our results allow SDP scheme designers to create schemes that are more easily comparable with one another, and to assist the prevention of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications Surveys & Tutorials 2017 (Volume: PP, Issue: 99

Extracting and Harnessing Interpretation in Data Mining

Machine learning, especially the recent deep learning technique, has aroused significant development to various data mining applications, including recommender systems, misinformation detection, outlier detection, and health informatics. Unfortunately, while complex models have achieved unprecedented prediction capability, they are often criticized as ``black boxes'' due to multiple layers of non-linear transformation and the hardly understandable working mechanism. To tackle the opacity issue, interpretable machine learning has attracted increasing attentions. Traditional interpretation methods mainly focus on explaining predictions of classification models with gradient based methods or local approximation methods. However, the natural characteristics of data mining applications are not considered, and the internal mechanisms of models are not fully explored. Meanwhile, it is unknown how to utilize interpretation to improve models. To bridge the gap, I developed a series of interpretation methods that gradually increase the transparency of data mining models. First, a fundamental goal of interpretation is providing the attribution of input features to model outputs. To adapt feature attribution to explaining outlier detection, I propose Contextual Outlier Interpretation (COIN). Second, to overcome the limitation of attribution methods that do not explain internal information inside models, I further propose representation interpretation methods to extract knowledge as a taxonomy. However, these post-hoc methods may suffer from interpretation accuracy and the inability to directly control model training process. Therefore, I propose an interpretable network embedding framework to explicitly control the meaning of latent dimensions. Finally, besides obtaining explanation, I propose to use interpretation to discover the vulnerability of models in adversarial circumstances, and then actively prepare models using adversarial training to improve their robustness against potential threats. My research of interpretable machine learning enables data scientists to better understand their models and discover defects for further improvement, as well as improves the experiences of customers who benefit from data mining systems. It broadly impacts fields such as Information Retrieval, Information Security, Social Computing, and Health Informatics

Context and communication profiling for IoT security and privacy: techniques and applications

During the last decade, two major technological changes have profoundly changed the way in which users consume and interact with on-line services and applications. The first of these has been the success of mobile computing, in particular that of smartphones, the primary end device used by many users for access to the Internet and various applications. The other change is the emergence of the so-called Internet-of-Things (IoT), denoting a technological transition in which everyday objects like household appliances that traditionally have been seen as stand-alone devices, are given network connectivity by introducing digital communication capabilities to those devices. The topic of this dissertation is related to a core challenge that the emergence of these technologies is introducing: how to effectively manage the security and privacy settings of users and devices in a user-friendly manner in an environment in which an ever-growing number of heterogeneous devices live and co-exist with each other? In particular we study approaches for utilising profiling of contextual parameters and device communications in order to make autonomous security decisions with the goal of striking a better balance between a system's security on one hand, and, its usability on the other. We introduce four distinct novel approaches utilising profiling for this end. First, we introduce ConXsense, a system demonstrating the use of user-specific longitudinal profiling of contextual information for modelling the usage context of mobile computing devices. Based on this ConXsense can probabilistically automate security policy decisions affecting security settings of the device. Further we develop an approach utilising the similarity of contextual parameters observed with on-board sensors of co-located devices to construct proofs of presence that are resilient to context-guessing attacks by adversaries that seek to fool a device into believing the adversary is co-located with it, even though it is in reality not. We then extend this approach to a context-based key evolution approach that allows IoT devices that are co-present in the same physical environment like the same room to use passively observed context measurements to iteratively authenticate their co-presence and thus gradually establish confidence in the other device being part of the same trust domain, e.g., the set of IoT devices in a user's home. We further analyse the relevant constraints that need to be taken into account to ensure security and usability of context-based authentication. In the final part of this dissertation we extend the profiling approach to network communications of IoT devices and utilise it to realise the design of the IoTSentinel system for autonomous security policy adaptation in IoT device networks. We show that by monitoring the inherent network traffic of IoT devices during their initial set-up, we can automatically identify the type of device newly added to the network. The device-type information is then used by IoTSentinel to adapt traffic filtering rules automatically to provide isolation of devices that are potentially vulnerable to known attacks, thereby protecting the device itself and the rest of the network from threats arising from possible compromise of vulnerable devices

STASH : Securing transparent authentication schemes using prover-side proximity verification

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