The cybersecurity industry is trending upwards. More people are sharing sensitive data online, which entices criminals to find new ways to attack internet users. Every industry faces change, but the change the cybersecurity industry is facing may threaten job security. What Change is Threatening Job Security? A couple of trends are doing this.

Machine learning is a science that uses existing data on a subject to train a computer how to identify related data. Just like with humans, the more training a machine learning algorithm gets, the more likely it is to succeed at its task. We have an extensive amount of information on attacks that can be used to train machines. After all, new attacks come out every day and over a hundred million malware samples have been collected each year since 2014. This information, as well as the historical information, can be fed into machine learning algorithms to better understand the attacks that haven't happened yet.

Continuing on the trail of smart security systems exhibiting security issues, now joins Amazon's smart doorbells. Researchers found a serious vulnerability in the Ring Video Doorbell. Researchers from Bitdefender have discovered a serious security vulnerability in Amazon's Ring Video Doorbell. As elaborated in a white paper, the researchers found that the Ring Video Doorbell actually exposed the homes' security to attacks over WiFi. Exploiting the flaw required an attacker to be in close proximity to the target home.

Recent discussion around enterprise cybersecurity has focused so heavily on the cloud and the apps contained within that you would think the cloud is the only area worth considering. But it is important to remember that cloud security begins with any touchpoint in the process and therefore can be vulnerable at any of those sections. With so many parties involved such as customers, users, partners along with APIs, special applications, devices and now IoT, the cloud must be able to reach across and get and gather realtime data and react to know and unknown situations. Therefore we must carefully consider how best to secure the cloud as it stretches across so many platforms and scenarios. Let's start with the idea that any real security architecture begins with a platform that can incorporate what is known and react to what is not known.

Dragos Gavrilut, Bitdefender's Antimalware Research Manager, talks about the capabilities of current security-centric machine learning algorithms and how they're going to evolve over time. Dragos manages a team of 80 people that develop heuristic develops heuristic detections, cloud-based services, system testing services, disinfection routines, Android and iOS analysis, event correlation algorithms, data mining, IoT and cybersecurity analysis. He splits his time as a lecturer at the Alexandru Ioan Cuza University of Iași, where he received his Ph.D. in 2012. He received his B.Sc. and M.Sc. in computer science from the same university. The interview is split in three short parts, each addressing specific machine learning topics.

Ten years ago, antivirus companies the world over were about to face an important milestone in their history: the arrival of the millionth piece of malware known to man. Back in the day, each so-called "virus" was important: it had to be isolated, documented formally and dealt with by dozens of researchers gathered around a desk. Today, anti-malware companies get a million pieces of malware or more every two days. So it's easy to understand that these teams of reverse engineers now hardly have the time or resources to manually sift through this flood of malware. Collecting, inspecting and fixing things up for the customer are tedious tasks now offloaded to automated systems controlled by artificial intelligence.

Artificial intelligence and machine learning have become key drivers of innovation. Machine-learning algorithms significantly improve detection time for modern threats, as they can analyze large amounts of data significantly faster than any human could. If trained to accurately detect various types of malware behavior, machine-learning algorithms can have a high detection rate, even on new or unknown samples. The merging of human ingenuity with the speed and relentless data analysis of machine learning significantly accelerates reactions against new malware, offering protection even from previously unknown samples – advanced persistent threats, zero-day attacks, and ransomware. Detecting ransomware, for example, requires several algorithms, each specialized in detecting specific families with individual behaviors.

Machine learning is an exciting space in the field of artificial intelligence (AI) and has been a stalwart of computing machine efforts since the beginning of integrating computers with enterprises. Yet some organizations are increasingly overwhelmed with too much "big data", new technologies and figuring out how to keep everything secure. As one top IT security expert who wished to remain anonymous noted, "The increase in communications and information accumulation will require not just one program to filter the financial data professionals need from the avalanche of data available but automation of automation of such processes." Such a program, or rather a platform, could scan fundamental information to compile complex datasets together and determine what info is important. Some examples would be determining influencing factors for price movement in global logistics and how weather impacts supply chains. Then the organization could attack weaknesses in that chain to make sure they meet upcoming product demand.

Ben Dickson is a software engineer and the founder of TechTalks. 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.

When it comes to artificial intelligence, people typically envision a Sci-Fi world where robots take over humanity as we know it. But artificial intelligence is already here, improving everyday technologies such as ecommerce, surveillance systems and many others. To shed some light on how AI is used in this industry, we've asked Cristina Vatamanu, malware researcher at Bitdefender's Antimalware Labs, to answer a few questions. For the past 6 years, Cristina has demonstrated strong expertise in reverse engineering, exploit analysis, threat analysis and automated systems. She is now pursuing a PhD in Machine Learning theory in malware detection systems at "Gheorghe Asachi" Technical University in Iasi.