Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence Computers and mobile devices running rich operating systems have a plethora of security solutions and encryption protocols that can protect them against the multitude of threats they face as soon as they become connected to the Internet. Such is not the case with IoT. Of the billions of IoT devices presently in use, a considerable percentage are sporting low-end processing power and storage capacity and don't have the capability to become extended with security solutions. Yet they are connected to the Internet, nonetheless, which is an extremely hostile environment.

US-based tech firm Department 13 has shown off its anti-drone device Mesmer, which takes control of intruding drones and forces them to land. The ASX-listed firm's esky-sized device is intended for the protection of a fixed site, such as an airport, where it can use existing radar to detect intruding drones. Previous methods to deal with nuisance drones have ranged from blasting it from the sky with a shotgun, snaring it in a net, or shooting in with an anti-drone drone -- the US military has used Hellfire missiles and 30 kilowatt shipboard lasers to destroy drones. The agency warned local law enforcement of the emerging threat that drones pose. Conversely, Department 13 worked on a method that reduced the risk of any potential danger to nearby personnel or infrastructure.

Mark 2016 as the year that researchers applied artificial intelligence (AI) to the challenges of cybersecurity. If machines can steer our cars and predict our shopping habits, then why not watch over our networks and servers too? IBM in May 2016 announced Watson for Cyber Security, in which the IT behemoth began teaching its pattern-recognition supercomputer to learn the difference between safe and risky data. That could ease the burden on overworked cybersecurity professionals, IBM hopes. Several universities involved with that project began having students train the system within the past several weeks, explained IBM Watson's Jeb Linton, chief security architect.

It will come as no surprise to anyone familiar with the technology world that the rate of cyber attacks, the development of malware, and the exploitation of zero-day flaws makes is very difficult for IT teams and security specialists to keep up with let alone get ahead of cyber threats. Research from Symantec noted that nearly one million new malware threats emerge daily, and while there are many tools to make detecting rogue code an easier process, dealing with such an enormous amount of new threats appears to be an almost insurmountable task even for the best security teams and anti-virus systems. The answer to this, and the potential future of cyber security, looks to be the use of machine learning and artificial intelligence (AI) to apply clever computers and smart software to a problem that leaves humans on the back foot in the fight against hackers. Rather than sift through data harvested from across IT networks, machine learning algorithms can be trained to detect certain malware and threat signatures and proactively sniff out threats, bypassing the need for cyber security experts to disappear into a warren of file paths and scripts to find tell-tale signs of malware. Webroot is one such cyber security company applying machine learning techniques to power its threat intelligence service without requiring resource sapping and time-consuming manual processes.

The Bank of England is using its new fintech accelerator to work with startups on developing proof-of-concepts (POCs) in data analytics, information security and distributed ledgers. Launched in June, the accelerator is designed to boost the BofE's practical experience with fintech, with firms invited to apply to work with it on POCs that address challenges unique to the central bank. Speaking at Web Summit in Lisbon this week, BofE COO Charlotte Hogg invited new applications and gave an update on the project, revealing that the bank is working with BMLL Technologies on a POC that uses a machine learning platform, applied to historic limit order book data, to spot anomalies and facilitate the use of new tools in analytical capabilities. A second POC, with Enforcd, uses an analytic platform designed specifically to share public information on regulatory enforcement action. Meanwhile, two firms - Anomali and ThreatConnect - are working on technologies to collect, correlate, categorise and integrate cyber security intelligence data.

In all likelihood androids won't just one day spontaneously realize that we suck and rise up against us in a civil rights slave revolt. Instead, robots and animals will gradually follow the paths more naturally suited to them by moving along different, most likely conflicting, trajectories into the future. It's scary, we actually have no idea what's going on in the mind of a machine learning algorithm. We set them in motion and what they really do as a result is a black box mystery to us. Not only do our computers pick up on our racist and sexist biases, they see how much porn we look at and how many cat videos we post and they think that those things represent the human condition.

In this talk we will present some techniques that we use on a day to day basis in our research, where we combine our internet-wide data scanning and acquisition platform with ML/Data science techniques which allows us to find things faster or extract results in a more automated way. We will focus on practical cases and examples that even our audience at home will be able to use if they want. A couple of examples we will look at is how to classify images such as VNC screenshots, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs. We will also talk a bit about a data analysis and classification pipeline architecture, we will look at the different technologies and what they do and how they can be used. We will start by giving a very brief entry to the data science world and talk about: Technologies Techniques How these relate to infosec Algorithms and how they can be used How people can come into the world of data and machine learning Data visualization techniques and what are the best choices for different types of data A couple of examples we will look at is how to classify images such as VNC or x11 screenshots, OCR, we will look at network scans and using machine learning to classify them and also the use of natural language processing to analyze CVEs.

Symantec has launched Endpoint Protection 14, a new security solution which harnesses artificial intelligence to protect clients. Announced on November 1, the new security offering is powered by AI and machine learning on the endpoint and in the cloud. Symantec says that by harnessing machine learning to collate data and detect patterns and anomalies which may indicate a cyberattack, AI provides "a multi-layered solution able to stop advanced threats and respond at the endpoint regardless of how the attack is launched." Symantec Endpoint Protection combines machine learning, memory exploit mitigation, and threat intelligence provided by Symantec and Blue Coat, which combined their research and security operations in October after Symantec completed the acquisition of Blue Coat for $4.6 billion. The company also says that the solution is capable of 99.9 percent efficacy, low false positives, and a 70 percent carbon footprint reduction in comparison to past endpoint software.

Let's inject a virus into the attacking alien spacecraft and save Earth! Let's hack into the enemy mainframe with six keystrokes and abort the torpedo launch! Cybersecurity has long been a staple of science fiction, whether it's in movies like "Independence Day" or television shows like "Star Trek." Yet in our real 21st Century world, artificial intelligence is the new face of cybersecurity, even if it doesn't sound like Hal from "2001: A Space Odyssey." The most obvious place for added intelligence is to detect whether some pattern of network traffic is benign or hostile.

The new science-fiction film, it says, has created word-of-mouth, wowed audiences and earned a 100% Fresh rating on the movie review website Rotten Tomatoes. Denis Villeneuve's latest work, starring Amy Adams as a linguist chosen to communicate with alien visitors, may well be that. But it isn't easy to market a masterpiece -- especially a sci-fi masterpiece with spaceships that don't engage in dogfights, aliens who don't fire lasers and protagonists who don't throw punches. When "Arrival" touches down at 2,200 theaters this weekend, it will do so not only as one of the most well-regarded science-fiction movies in some time but as one of the greatest marketing puzzles in recent memory. The Paramount release is quiet, subtle and patient -- an artisanal offering in a time of studio fast food.