In this Help Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know. Hi, this is Chris Morales and I'm Head of Security Analytics at Vectra, and in this Help Net Security podcast I want to talk about machine learning fundamentals that I think we all need to know as cybersecurity professionals. AI has become very used within our industry more and more, and here at Vectra we are an AI company as well. As you start to hear more about AI, you have to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just kind of a quick understanding of the different algorithms or understanding when you need to use certain algorithms for specific jobs. There has always been a very muddled use of the terms artificial intelligence, data science and machine learning.
The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.) embedded with electronics, software, sensors, actuators, and network connectivity that enables these "smart objects" to collect and exchange data. In other words, Internet of things is a global infrastructure for the information society. IoT allows advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies. For example, the smart refrigerator in your kitchen (at home) can send you an alert (or notification) on your smartphone (while you are leaving office) when you're out of milk or gas. Your wearable or smartwatch can warn you if there is something wrong with your pulse or heart-rate. Additionally, all this information gets recorded. Later, the software after looking at the data can provide you information like: you are likely to run of milk on Wednesday, run out of gas in two weeks, or likely to get a heart attack in three months (so, time for a check-up and take precautions).
The world of cyber security is becoming increasingly more difficult for businesses looking to protect their assets. Attacks are becoming more sophisticated, with cyber criminals making use of ever advancing technologies. Even with modern machine driven security systems, it is becoming increasingly complex for businesses to differentiate between a genuine visitor and criminals attempting to breach or bring down their systems. With news that MIT has developed Artificial Intelligence (AI) capable of detecting 85% of cyber attacks - and still learning - does the future of cyber security lie with robots? Built by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx, this artificial intelligence platform known as AI2 will spark an interesting debate about the role of AI in protecting an organization from cyber attacks.
The new technologies like Machine Learning, Internet of Things, Deep Learning, NLP, Artificial Intelligence, Cloud, Big data and Predictive analytics are having a massive impact in India. This post is a Beginners Guide to Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing (NLP), Deep Learning, Big Data Analytics and Blockchain. While big data is all about data, patterns (or trends) insights & impacts, internet of things is about data, devices, and connectivity. The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.)
Artificial intelligence covers everything from machine learning to business intelligence. Machine learning, in particular, has become a highly useful tool in our modern work environment. Machine learning, in short, means you can make machines learn from data and make decisions without explicitly telling them, what to do. Cyber security is one of the key domains, where machine learning is extremely helpful. Cyber security companies deal with a lot of data and high dimensionality of data.