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AI and the future of cybersecurity work

Posted by Sohrob Kazerounian on Nov 7, 2018 8:08:00 AM

In February 2014, journalist Martin Wolf wrote a piece for the London Financial Times[1] titled Enslave the robots and free the poor. He began the piece with the following quote:

“In 1955, Walter Reuther, head of the US car workers’ union, told of a visit to a new automatically operated Ford plant. Pointing to all the robots, his host asked: How are you going to collect union dues from those guys? Mr. Reuther replied: And how are you going to get them to buy Fords?”

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Topics: AI, machine learning, deep learning


Most attacks against energy and utilities occur in the enterprise IT network

Posted by Chris Morales on Nov 1, 2018 5:00:00 AM

The United States has not been hit by a paralyzing cyberattack on critical infrastructure like the one that sidelined Ukraine in 2015. That attack disabled Ukraine's power grid, leaving more than 700,000 people in the dark.

But the enterprise IT networks inside energy and utilities networks have been infiltrated for years. Based on an analysis by the U.S. Department of Homeland Security (DHS) and FBI, these networks have been compromised since at least March 2016 by nation-state actors who perform reconnaissance activities looking industrial control system (ICS) designs and blueprints to steal.

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Topics: attacker behavior, cybersecurity, Threat Detection, AI, critical infrastructure, IT, cyberattackers


Integrating with Microsoft to detect cyberattacks in Azure hybrid clouds

Posted by Gareth Bradshaw on Sep 25, 2018 5:58:37 AM

Microsoft unveiled the Azure Virtual Network TAP, and Vectra announced its first-mover advantage as a development partner and the demonstration of its Cognito platform operating in Azure hybrid cloud environments.

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Topics: AI, machine learning, deep learning, cloud, Microsoft


Near and long-term directions for adversarial AI in cybersecurity

Posted by Sohrob Kazerounian on Sep 12, 2018 6:00:00 AM

The frenetic pace at which artificial intelligence (AI) has advanced in the past few years has begun to have transformative effects across a wide variety of fields. Coupled with an increasingly (inter)-connected world in which cyberattacks occur with alarming frequency and scale, it is no wonder that the field of cybersecurity has now turned its eye to AI and machine learning (ML) in order to detect and defend against adversaries.

The use of AI in cybersecurity not only expands the scope of what a single security expert is able to monitor, but importantly, it also enables the discovery of attacks that would have otherwise been undetectable by a human. Just as it was nearly inevitable that AI would be used for defensive purposes, it is undeniable that AI systems will soon be put to use for attack purposes.

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Topics: AI, machine learning, deep learning


2018 Black Hat Superpower Survey: It's about time and talent

Posted by Chris Morales on Aug 22, 2018 2:57:12 PM

2018 Black Hat survey: It’s about time and talent

We love Black Hat. It’s the best place to learn what information security practitioners really care about and what is the truth of our industry. Because we want to always be relevant to customers, we figured Black Hat is an ideal event to ask what matters.

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Topics: attacker behavior, cybersecurity, Threat Detection, AI, SOC


Choosing an optimal algorithm for AI in cybersecurity

Posted by Sohrob Kazerounian on Aug 15, 2018 6:00:00 AM

In the last blog post, we alluded to the No-Free-Lunch (NFL) theorems for search and optimization. While NFL theorems are criminally misunderstood and misrepresented in the service of crude generalizations intended to make a point, I intend to deploy a crude NFL generalization to make just such a point.

You see, NFL theorems (roughly) state that given a universe of problem sets where an algorithm’s goal is to learn a function that maps a set of input data X to a set of target labels Y, for any subset of problems where algorithm A outperforms algorithm B, there will be a subset of problems where B outperforms A. In fact, averaging their results over the space of all possible problems, the performance of algorithms A and B will be the same.

With some hand waving, we can construct an NFL theorem for the cybersecurity domain:  Over the set of all possible attack vectors that could be employed by a hacker, no single detection algorithm can outperform all others across the full spectrum of attacks.

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Topics: AI, machine learning, deep learning


Types of learning that cybersecurity AI should leverage

Posted by Sohrob Kazerounian on Jul 18, 2018 6:00:00 AM

Despite the recent explosion in machine learning and artificial intelligence (AI) research, there is no singular method or algorithm that works best in all cases.

In fact, this notion has been formalized and shown mathematically in a result known as the No Free Lunch theorem (Wolpert and Macready 1997).

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Topics: AI, machine learning, deep learning


Neural networks and deep learning

Posted by Sohrob Kazerounian on Jun 13, 2018 6:00:00 AM

Deep learning refers to a family of machine learning algorithms that can be used for supervised, unsupervised and reinforcement learning. 

These algorithms are becoming popular after many years in the wilderness. The name comes from the realization that the addition of increasing numbers of layers typically in a neural network enables a model to learn increasingly complex representations of the data.

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Topics: AI, machine learning, deep learning


Giving incident responders deeper context about what happened

Posted by Cognito on Jun 4, 2018 9:54:43 AM

If you’re joining me for the first time, I want to introduce myself. I am Cognito, the AI cybersecurity platform from Vectra. My passion is hunting-down cyberattackers – whether they’re hiding in data centers and cloud workloads or user and IoT devices.

Cybersecurity analysts are overwhelmed with security events that need to be triaged, analyzed, correlated and prioritized. If you’re an analyst, you probably have some incredible skills but are being held back by tedious, manual work.

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Topics: AI, cybersecurity, Cyberattacks, Threat Detection, Data Center, cloud, network security, IoT, Malware Attacks


How algorithms learn and adapt

Posted by Sohrob Kazerounian on May 24, 2018 12:59:06 PM

There are numerous techniques for creating algorithms that are capable of learning and adapting over time. Broadly speaking, we can organize these algorithms into one of three categories – supervised, unsupervised, and reinforcement learning.

Supervised learning refers to situations in which each instance of input data is accompanied by a desired or target value for that input. When the target values are a set of finite discrete categories, the learning task is often known as a classification problem. When the targets are one or more continuous variables, the task is called regression.

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Topics: AI, machine learning


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