security


Soon, your most important security expert won't be a person

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Automated security systems now apply AI techniques to massive databases of security logs, building baseline behavioural models for different days and times of the week; if particular activity strays too far from this norm, it can be instantly flagged, investigated, and actioned in real time. This has led firms like IBM, Amazon Web Services, Microsoft Azure, Unisys and startups like BigML, Ersatz and DataRobot and to offer machine learning as a service (MLaaS), providing API-based access to the core libraries necessary to apply machine learning techniques to large data sets. In the short term, however, AI is still on a short leash within many security environments: a recent Carbon Black survey of 410 cybersecurity researchers found that 74 percent still see AI-driven cybersecurity solutions as flawed and 70 percent said they can be bypassed by attackers. Over time, tools will become more sophisticated and ever-larger security data sets help learning algorithms add ever more nuance to their detection mechanisms.


Garbage in, garbage out: a cautionary tale about machine learning

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That's the message Sophos data scientist Hillary Sanders delivered at Black Hat USA 2017 on Wednesday in a talk called "Garbage in, Garbage Out: How Purportedly Great Machine Learning Models Can Be Screwed Up By Bad Data". The next best option, she said, is to test how sensitive one's models are to new datasets they weren't trained on, and pick training datasets and model configurations that perform consistently well on a variety of test sets, not just the test datasets that originate from the same parent as the model's training dataset. But Sanders suggested some starting points. To get a more accurate measurement, Sanders ran Black Hat attendees through some sensitivity results from the same deep learning model designed to detect malicious URLs, trained and tested across three different sources of URL data.


Improving Customer Service and Security With Data Analytics

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Data analytics exacerbates trade-offs between security and service; the analytical processes on data can, at a minimum, raise privacy concerns for individuals because much of marketing analytics tries to learn as much as possible about potential customers. The process continues, potentially escalating to security challenge questions based on shared secrets, until the bank is convinced of our identity. But data and machine learning, specifically speech processing, offer a great example of an invisible way that analytics can simultaneously help improve security and service. The technology itself isn't that new, but speech processing has progressed to the point now where financial services companies can match a caller's voice to their prior calls, allowing the authentication process to occur behind the scenes as the customer service conversation progresses.


AI/BOTS: Machine Learning on Online Fraud PYMNTS.com

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Within the retail industry specifically, artificial intelligence (AI) is moving the ball for a lot of merchants looking to not only streamline their business operations but provide a more personalized experience for consumers. Stemming from AI, machine learning is helping technology move at a swifter rate, and as more people bring their shopping needs into the online world, it's likely that machine learning will play a larger role moving forward. From enhancing the supply chain process to learning more about consumers' shopping behavior, machine learning is a technology that's at the forefront of retail advancements and innovation. With machine learning, retailers will have a system that continually improves upon itself to become more in tune with the overall businesses' daily activities, including supply chain operations, manufacturing and consumer behavioral preferences, to name a few.


AI/BOTS: Machine Learning on Online Fraud PYMNTS.com

#artificialintelligence

Within the retail industry specifically, artificial intelligence (AI) is moving the ball for a lot of merchants looking to not only streamline their business operations but provide a more personalized experience for consumers. Stemming from AI, machine learning is helping technology move at a swifter rate, and as more people bring their shopping needs into the online world, it's likely that machine learning will play a larger role moving forward. From enhancing the supply chain process to learning more about consumers' shopping behavior, machine learning is a technology that's at the forefront of retail advancements and innovation. With machine learning, retailers will have a system that continually improves upon itself to become more in tune with the overall businesses' daily activities, including supply chain operations, manufacturing and consumer behavioral preferences, to name a few.


Using AI to spot malware patterns

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Today there are more threats coming in from more places and AV solutions miss many things. Each type of malware leaves behind a signature, or fingerprint, so if one can collect the data and analyze it, the causal good and causal bad patterns can be found. It's the final 10 percent that almost all AV systems miss but where machine learning shines. It was new and almost all the traditional AV solutions missed it initially.


Your Robot Vacuum Cleaner Will Soon Collect And Sell Data About You And Your Home

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That vision has its fans, from investors to the likes of Amazon, Apple and Alphabet, who are all pushing artificially intelligent voice assistants as smart home interfaces... Angle told Reuters that iRobot, which made Roomba compatible with Amazon's Alexa voice assistant in March, could reach a deal to sell its maps to one or more of the Big Three in the next couple of years. Especially in the cell phone era when every step you take is collected, tracked, monetized and sold by cellular companies, app makers, and every advertising and metric company in between. But there's an awful lot of data these robots collect that you may not particularly want shared, and our proud tradition of overlong, convoluted terms of service traditionally won't make that clear.


cybersecurity-artificial-intelligence-startups-market-map

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Two unicorn companies valued at over $1B are included in the map: the automated endpoint protection company Tanium and the predictive intelligence company Cylance. Mobile Security: Included in this category are startups such as Appthority, which provides a cloud-based platform that automatically identifies and grades risky behavior in mobile apps including known and unknown malware, new malware used in targeted attacks, corporate data ex-filtration, and intellectual property exposure. Similarly, Skycure's predictive technology leverages massive crowd knowledge to proactively identify threats to secure mobile devices. Behavioral Analytics / Anomaly Detection: Startups in this category include Darktrace which uses advanced mathematics and machine learning to detect anomalous behavior in organizations' systems and networks in order detect cyber-attacks.


New Microsoft cloud service uses AI to find bugs in your code

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Microsoft Security Risk Detection, made publicly available Friday, uses artificial intelligence (AI) to help software developers find bugs in their code and other vulnerabilities. Microsoft's David Molnar, who leads the group behind Microsoft Security Risk Detection, said in the post that the tool performs fuzz testing, a QA method for finding buggy code and security problems. To conduct its fuzz testing, Microsoft Security Risk Detection asks "what if" questions to determine the root cause of a given issue, the post said. By automating some of the common security processes with bug testing, the tool has improved digital transformation efforts as well, Molnar said in the post.


indoor-robots-gaining-momentum-and-notoriety

Robohub

Recent events demonstrate the growing presence of indoor mobile robots: (1) Savioke's hotel butler robot won the 2017 IERA inventors award; (2) Knightscope's security robot mistook a reflecting pond for a solid floor and dove in face-first to the delight of Twitterdom and the media; and (3) the sale of robotic hospital delivery provider Aethon to a Singaporean conglomerate. Travis Deyle, CEO of Silicon Valley startup Cobalt Robotics which is developing indoor robots for security purposes, in an article in IEEE Spectrum, posited that commercial spaces are the next big marketplace for robotics and that there's a massive, untapped market in each of the commercial spaces shown in his chart below: "Commercial spaces could serve as a great stepping stone on the path toward general-purpose home robots by driving scale, volume, and capabilities. The International Federation of Robotics (IFR) and the IEEE Robotics and Automation Society (IEEE/RAS) jointly sponsor an annual IERA (Innovation and Entrepreneurship in Robotics and Automation) Award which this year was presented to the Relay butler robot made by Savioke, a Silicon Valley startup. Listed below are a few of the companies in the emerging mobile robot indoor commercial marketplaces described in Deyle's chart above.