Hackers forced a Tesla to enter the wrong lane


Hackers have demonstrated how they could trick a Tesla Model S to enter into the wrong lane by using a method called "adversarial attack," a way of manipulating a machine learning (ML) model. The Tesla Autopilot recognises lanes and assists control by identifying road traffic markings. The researchers from the Keen Security Lab of Chinese tech giant Tencent showed that by placing interference stickers on the road, the autopilot system could be fed information that would force it to make an abnormal judgement and make the vehicle enter a wrong lane. "In this demonstration, the researchers adjusted the physical environment (e.g. "This is not a real world concern given that a driver can easily override autopilot at any time by using the steering wheel or brakes and should be prepared to do so at all times," the spokesperson said.

Artificial Intelligence Can Now Manipulate Medical Images Well...


Sometime in the early 2000s, while sitting in my dentist's chair, I began to wonder about the potential real-world pain that someone could potentially inflict on another human being simply by hacking the new digital x-ray system that the dentist had installed. Would it be possible, for example, for a hacker to modify the digital images from the x-rays so that the dentist would not be able to find and repair painful cavities, or to cause the dentist to perform an unnecessary root canal, filling, or other procedure? How certain could I be that the images of my own teeth were not tampered with? Several years later, when I had my a digital MRI after an auto accident, I wondered even further – could hackers modify images in such a manner so as to cause a person to have his head cut open to remove a tumor when, in fact, he had no tumors? Or to cause a scan to appear normal when the victim actually had a life threatening condition requiring immediate attention?

Hacker AI vs. Enterprise AI: A New Threat


The adversarial use of artificial intelligence (AI) and machine learning (ML) in malicious ways by attackers may be embryonic, but the prospect is becoming real. It's evolutionary: AI and ML gradually have found their way out of the labs and deployed for security defenses, and now they're increasingly being weaponized to overcome these defenses by subverting the same logic and underlying functionality. Hackers and CISOs alike have access to the power of these developments, some of which are turning into off-the-shelf offerings that are plug-and-play capabilities enabling hackers to get up and running quickly. It was only a matter of time before hackers started taking advantage of the flexibility of AI to find weaknesses as enterprises roll it out in their defensive strategies. The intent of intelligence-based assaults remains the same as "regular" hacking.

Smart talking: are our devices threatening our privacy?

The Guardian

On 21 November 2015, James Bates had three friends over to watch the Arkansas Razorbacks play the Mississippi State Bulldogs. Bates, who lived in Bentonville, Arkansas, and his friends drank beer and did vodka shots as a tight football game unfolded. After the Razorbacks lost 51–50, one of the men went home; the others went out to Bates's hot tub and continued to drink. Bates would later say that he went to bed around 1am and that the other two men – one of whom was named Victor Collins – planned to crash at his house for the night. When Bates got up the next morning, he didn't see either of his friends. But when he opened his back door, he saw a body floating face-down in the hot tub. A grim local affair, the death of Victor Collins would never have attracted international attention if it were not for a facet of the investigation that pitted the Bentonville authorities against one of the world's most powerful companies – Amazon. Collins' death triggered a broad debate about privacy in the voice-computing era, a discussion that makes the big tech companies squirm.

The Quantum Revolution Is Coming, Ready Or Not


The Redmond, Washington-based tech giant is competing with Alphabet Inc.'s Google, International Business Machines Corp. and a clutch of small, specialized companies to develop quantum computers – machines that, in theory, will be many times more powerful than existing computers by bending the laws of physics. Two recent articles caught my eye. The first was in Financemagnates.com It was an interview with Michael Bancroft of Bloomberg TV, in which he spoke of the spreading popularity of blockchain technology, not just for protecting cryptocurrencies but for a growing number of uses including cybersecurity. He said that before too long, "what we're likely to see is blockchain being employed for cybersecurity… [in] governments who are looking to secure important files and records safe from hackers."

The Future of Cybersecurity is A.I. vs. A.I.


Nicole Eagan believes a robot uprising draws nigh. As the chief executive of Darktrace, a cybersecurity "unicorn," or private firm valued at more than $1 billion, Eagan helps companies spot intruders in corporate networks, quarantine them, and defend data. The British firm's technology uses machine learning techniques to gain an understanding of the internal state of customers' networks and then watches for telltale deviations from the norm that may indicate foul play. While Darktrace uses A.I. techniques for defense, the company anticipates that thieves and spies will soon catch up. "I expect that we're going to see artificial intelligence used by the attackers," says Eagan, noting that there already have been "early glimpses" of that future coming to pass.

Create your cyber get well plan


As the amount of data created daily increases (already at 2.5 Quadrillion bytes a day allegedly [1]) ML techniques are allowing us to cluster, organise and appropriate this data into actionable information. This is especially true in the realm of Cyber Security. Don't be scared of the term Machine Learning, it really just means a computer that can learn to do something without being explicitly programmed for that task. The process typically involves training the machine to do a task (i.e. Let's have a quick look at some of the ways we encounter ML every day in Cyber Security.

Keep your skills sharp and pay whatever you want for these 10 online course bundles


If you want to guarantee your own long-term success, your best bet is investing in your growth. That doesn't necessarily mean you need to spend thousands of dollars and hours sitting in a classroom after a long day at work (hallelujah). Thanks to that wonderful and wild thing called the internet, you can learn the most desired skills without changing out of your pajamas. The job market is constantly shifting -- ensure you can evolve with it by taking an online class and keeping your skills sharp. The courses listed here cover everything from lessons in coding to professional photography.

AI Advancements Are Making It Easier to Hack Biometric Systems


Biometric systems may not be the best way to protect your data as hackers find new creative ways to steal your identity. Fingerprint technology and face scanners were the ultimate forms of security in an age where your data and identity were crucial, or so we thought. In an age, where every few months it seems as if there is another major security breach at a top corporation, and almost your entire identity exists in the digital realm, it is important to protect yourself. If you are a next-gen smartphone user, there is a good chance that your smart device is equipped with a fingerprint scanner or facial recognition technology, or potentially both. When biometric systems first went commercial, they promised the ultimate forms of security technology.

Why is Russia so good at getting women into technology?


A stubborn brother and 3,000 Russian rubles were all it took to convince Elena Tverdokhlebova to go into science and technology. "I was 10 years old when my brother, who was studying for the university admissions exams, gave me a math problem to solve," she says. He was jumping around the living room offering her 100 rubles, then 1,000, and finally 3,000 if she could do it. "To his surprise, I was able to solve it, and he gave me 3,000 rubles, about $100 at that time," she says. This small incentive and the support she received from her family convinced Tverdokhlebova to study math and later computer science.