IoT can be viewed as an ideal opportunity to bridge the worlds of information technology (IT) and operational technology (OT). IoT projects over the last several years have focused on device onboarding and getting data aggregated into the cloud…usually an IT-managed cloud. Data access and analytical insight from that data has been parsed out from IT as a service. The “IT as a service” model is not well positioned to take advantage of the new opportunities presented by edge compute devices and the analytical capabilities in those devices transacted at origination of the data.
Historically, IT and OT data did not intersect. The departmental silos were hardened by “division-of-responsibility” charters that ratified barriers to working together. But today’s ability to deploy analytics at the edge raises the need for the integration of the IT and OT worlds. Read More
Daily Archives: October 4, 2019
Information Exposure From Consumer IoT Devices
Internet of Things (IoT) devices are increasingly found in every-day homes, providing useful functionality for devices such as TVs,smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However,understanding these risks in depth and at scale is difficult due to heterogeneity in devices’ user interfaces, protocols, and functionality.
In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual con-trolled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an insitu user study comprising 36 participants. Read More
MIT Deep Learning Basics: Introduction and Overview with TensorFlow
As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond.
This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094. Read More
How Fog Computing is changing the BigData paradigm for IoT device?
The new era of BigData and advances in technology have made significant transitions towards the high functionality of IoT devices. The popularity of IoT devices has led to more easier methods for BigData collection, analysis, and distribution at a rapid rate. According to a report by Statista, by 2020, there will be 30 billion IoT devices worldwide, with this number set to exceed over 75 billion by 2025, Statistically, also, BigData accumulation over IoT devices and networks is clearly visible and to solve this problem, various computing methods are already popular. There are methods like quantum computing, cloud computing, edge/fog computing.
Though Quantum computing has a bright prospect, it has a long way to go, meanwhile, cloud computing is already a popular analytic method among developers and data scientists. In 2014, a new method, ‘fogging’ was first termed at Cisco. Fogging is better known as edge computing/fog computing. Big data analytics tools like Hadoop helps in reducing the cost of storage. This further increases the efficiency of the business. Read More
Blind Spots in AI Just Might Help Protect Your Privacy
Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what’s visible and hidden. It can, for instance, enable highly accurate facial recognition, see through the pixelation in photos, and even—as Facebook’s Cambridge Analytica scandal showed—use public social media data to predict more sensitive traits like someone’s political orientation.
Those same machine-learning applications, however, also suffer from a strange sort of blind spot that humans don’t—an inherent bug that can make an image classifier mistake a rifle for a helicopter, or make an autonomous vehicle blow through a stop sign. Those misclassifications, known as adversarial examples, have long been seen as a nagging weakness in machine-learning models. Read More
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