The global pandemic has put a spotlight on personal safety and security, so it's unsurprising that the video surveillance market is surging as well. Globally, it hit $45.5 billion this year, while AI technology, which is being integrated into video surveillance products at every price point, will hit $100 billion by the year 2025. Both consumers and small- and medium-size businesses are increasingly looking for solutions to manage the safety and security of homes, businesses and assets. More importantly, they're in search of solutions that incorporate sophisticated new video analytics, AI and cloud-based storage technology. Manufacturers are racing to meet the demand, according to a report by IFSEC Global.
Artificial intelligence is reframing the cybersecurity debate in a major way. Countless cybersecurity experts have started investing more in AI technology. They are focusing more on using AI to improve the security of cloud-based platforms. Eric Broda wrote an article on this topic in Medium, stating that AI is the future of cloud security. He made some very salient points that should be taken to heart. Cloud computing is the modern way of accessing all the technical services your London-based business requires – from servers, storage and database systems to networking, software and analytics – and all through the Internet.
Wyebot, the leader in AI-driven WiFi automation, announced exciting new features to its award-winning, patented Wireless Intelligence Platform (WIP). With Version 3.0, WIP expands its security features and enhances user experience with its first-to-market support for WiFi 6 (802.11ax) and more detailed AP Classification. The Wyebot WIP combines on-premise sensor hardware and cloud-based, vendor agnostic software that integrates seamlessly with any existing network infrastructure. Its advanced wireless optimization algorithms work alongside next-generation predictive analytics to identify potential threats or problems in order to keep the WiFi network up and running reliably and efficiently, all while providing actionable steps to optimize performance. "As the leaders in AI-driven WiFi automation, Wyebot is committed to continued innovation and ensuring our WIP always defines the industry standard," said Roger Sands, CEO and Co-Founder of Wyebot.
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The concept of the fourth industrial revolution was first introduced in Hannover earlier in this decade. This followed several decades of industrial automation, albeit at lower levels of functionality and complexity. Many developments have since shaped several industry 4.0 technologies that were previously under the purview of researchers. This is possible today, mainly due to innovations in technology, software, and hardware. Already, the increasing human-machine, machine-machine, and human-human connectivity influence production systems and processes across the world. Industry 4.0 trends and technologies are fundamental in achieving connected manufacturing geared towards smart and autonomous factories.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to its exposure to external adversaries, making it a lucrative attack vector for malicious actors. A datacenter infected with malware can cause data loss and/or major disruptions to service for its users. This paper analyzes and compares various Convolutional Neural Networks (CNNs) for online detection of malware in cloud IaaS. The detection is performed based on behavioural data using process level performance metrics including cpu usage, memory usage, disk usage etc. We have used the state of the art DenseNets and ResNets in effectively detecting malware in online cloud system. CNN are designed to extract features from data gathered from a live malware running on a real cloud environment. Experiments are performed on OpenStack (a cloud IaaS software) testbed designed to replicate a typical 3-tier web architecture. Comparative analysis is performed for different metrics for different CNN models used in this research.
Products/Services Visa agreed to acquire the token and electronic ticketing business of Rambus for $75 million in cash. The business involved is part of the Smart Card Software subsidiary of Rambus. It includes the former Bell ID mobile-payment businesses and the Ecebs smart-ticketing systems for transit providers. Meanwhile, Rambus expanded its CryptoManager Root of Trust product line. "Security is a mission-critical imperative for SoC designs serving virtually every application space," Neeraj Paliwal, vice president of products, cryptography at Rambus, said in a statement.
Looking back, we can see the value these emerging innovations offered, but in the moment, their promise seemed less clear. It is, therefore, remarkable how quickly organizations across sectors and regions navigated through the so what and into the now what for these trends and went on to successfully traverse the new digital landscape. This journey from uncertainty to digital transformation informs our latest offering, Tech Trends 2019: Beyond the digital frontier. A persistent theme of every Tech Trends report has been the increasing, often mind-bending velocity of change. A decade ago many companies could achieve competitive advantage by embracing innovations and trends that were already underway. Today, this kind of reactive approach is no longer enough. To stay ahead of the game, companies must work methodically to sense new innovations and possibilities, make sense of their ambitions for tomorrow, and find the confidence to boldly go beyond the digital frontier. So here's to the next decade of opportunity, whatever it may be. Along the way, embrace that queasy feeling of uncertainty.
Taoyuan City taps edge computing to manage its streetlights. Organisations in Asia-Pacific are seeking out edge computing in search of faster response and cost savings, but they also have concerns about security and latency when large volumes of data are processed on such platforms. This ebook, based on the latest ZDNet / TechRepublic special feature, explores how the combination of 5G and edge computing will unleash new IT capabilities. A primary, and often cited, benefit of edge deployments are the rapid response times that would not be possible if data is sent back to a centralised network for processing. Taiwan's Taoyuan City, for instance, turned to edge technology in rolling out smart streetlights in its Qingpu district, using HPE's Edgeline EL10 Internet of Things (IoT) Gateway.