Artificial Reality | The PatternEx Blog

More Morphing in the Security Services Space

We have more morphing in the security services space. When ISPs came into being in the mid-1990s, there came a need to provide some security services to customers who did not have a capability to provide such for themselves. Hence MSSPs were born. For many years, adoption of MSSP services remained quite low. Many potential customers just could not bring themselves to trust a 3rd party with their security needs.

 Morphing

Topics: Artificial Intelligence Evolution machine learning SOC

The Value of Threat Intelligence

I recently read through a report from a well known threat intelligence (TI) vendor that self-servingly claimed that TI programs “save businesses big money”. Would you really expect a vendor to say that their TI service is not worth the money that they are charging you? No, of course not. But, I was struck by the audacity of this vendor’s report, and specifically the unsubstantiated claims in it. Statements such as “Healthy organizations have threat intelligence infrastructure in place.” certainly don’t appear to be objective.

Topics: Artificial Intelligence Threat Detection SOC analytics

The Benefits of Transfer Learning with AI for Cyber Security

Transfer learning is not new in information security. It has been in use for many years. For example, anti-malware vendors have exchanged samples of malware between their own proprietary collections of such (so-called zoos). That is a form of transfer learning. Similarly, Snort Community rules are a form of transfer learning. Community rules can be written by anyone, and used by any organization. ISACs are another form of transfer learning. Security-related is shared within a community. All of these examples (zoos, community rules, ISACs) involve known bads (e.g., malware, exploits, IP addresses, domains).

Topics: Artificial Intelligence CyberSecurity Labels Virtual Analysts Threat Detection Transfer Learning AI

How machine learning creates virtual analysts

The security industry has done a great job of creating a lot of noise around the rise of “machine learning” or “artificial intelligence.” The industry says that rules are the problem—too many missed attacks and false alarms—and that machine learning is the answer.

Topics: Artificial Intelligence machine learning

PatternEx Co-Founder Keynotes "Cybersecurity & AI get real: Attacks. Players. Solutions"

PatternEx co-founder Dr. Kalyan Veeramachaneni is keynoting an event presented by MIT ILP, CSAIL Alliance program and MIT Startup Exchange (STEX).

Topics: Artificial Intelligence MIT Kalyan Veeramachaneni

Labels in AI: Where The Human Meets the Machine

  1. A song streams from your Pandora app, and you click “thumbs up!”
  2. A parent reads a book to a baby, taps an image of a Labrador and says, “dog!”
  3. A student looks at the results of a Google search and clicks the fourth link.

What do all three of these things have in common with a data scientist's work? And why should an InfoSec professional care?

Topics: Artificial Intelligence Labels

Attacks in the Abstract: Detecting New Attacks with AI

Consider Funshion malware. Sometimes classified as "aggressive malware," the base code is over four years old and is still bypassing endpoint protections. Funshion makes minor modifications to itself, rendering it invisible to the rules or signatures designed to catch it. Today there are well over a dozen variants in the wild, each designed to beat static rules. Each variant is essentially a new attack that rules cannot stop.

The good news is that AI has been able to do what rules cannot: understand that subtle variations of malware are still malware. This means AI can detect known attacks as well as attacks it has never seen before. This distinction alone puts it well beyond the capabilities of rules. So how does it work?

Topics: Artificial Intelligence Funshion Abstractions

The True InfoSec Talent Gap

We don't need more InfoSec analysts to write rules and investigate rules. We need more InfoSec analysts to train Artificial Infrastructures to detect attacks.

Topics: Artificial Intelligence Virtual Analysts

Artificial Intelligence: Primer and Deep Dive

Since our launch in February of this year, we have had many discussions with security leaders about the application of Artificial Intelligence in InfoSec. In those discussions, we have noticed there is a level of confusion concerning AI concepts (e.g. “what is the difference between ML and AI?” and “What is Deep Learning?”).

Topics: Artificial Intelligence CyberSecurity

Advanced Analytics and Cyber Security

Gartner published a research note on April 4th analyzing how the rapidly emerging technology of security analytics will impact existing markets and companies. If you are a Gartner client, it is called "The Fast Evolving State of Security Analytics, 2016" and you can find it here (Gartner Subscription Required).

Topics: Artificial Intelligence CyberSecurity Gartner Group