AI News, Using Artificial Intelligence in Cybersecurity

Seeing AI to AI: Artificial Intelligence and its Impact on Cybersecurity

Heightened Authentication Biometrics, the science of establishing someone’s identity, is another field that AI continues to impact, heightening both cyber and physical security within organizations seeking to protect their information.

This allows threat actors to more easily conduct brute force attacks to guess these passwords and, once they have been obtained, access other accounts that use the same username/password combinations.

AI biometrics provide a solution to this problem by offering validation for a characteristic that is hard to mimic, that you do not need to remember, and likely does not ever change.

Behavioral characteristics are based on unique behaviors like your voice, the way you walk, or the way you type and interact with a device.

Rather than a single-access verification, AI can validate these biometrics in an ongoing process to provide continuous authentication, such as in the case of a person using a computer.

AI can monitor the patterns of the individual using the device, such as their typing style and error rates, and provide a constant validation of their authenticity.

For example, a user’s Twitter posts could be collected to find personal information or key topics that the user is interested in, and the AI program can then craft an email mentioning that information.

These generated faces look entirely real to humans, and some can even fool other AI programs whose sole duty is to detect phony faces.

By providing an extended voice sample, AI can create an artificial voice that sounds like the person being sampled, able to express words that were not even in the initial sample.

In this age of disinformation campaigns, AI could potentially spread massive amounts of fake media, driving political polarization further and harming public trust.

When combined with the power of a neural net, a threat actor would be able to quickly gather information about a target system or software and learn its weaknesses.

Microsoft developed a method to apply this approach to improve their software as well, showing that neural fuzzing also has a constructive side, resulting in more secure code that is harder to exploit.

This technology can replicate human clicking patterns and website navigation to make traffic look more legitimate and fool intrusion detection systems.

For cybersecurity, the primary benefits center around quicker analysis and mitigation of threats, while concerns focus on the ability for less skilled hackers to deploy more sophisticated cyber and technology-based attacks.

Artificial Intelligence in Cybersecurity Is Vulnerable

By Celeste Fralick, chief data scientist and senior principal engineer, McAfee Just about everyone in the cybersecurity field has accepted the notion that adversaries are just as smart as we are.

Anytime we celebrate the latest threat detection and prevention breakthrough, we’re well aware that the bad guys are at work devising ways to evade or disrupt it.

As a consequence, both vendors and enterprise security teams need to be extra vigilant about continually monitoring AI-based security systems to ensure that they are doing what they are meant to do as they evolve and adapt to the changing threat landscape.

Here’s the bottom line: as the amount of data increases, reliance on AI will inevitably increase too, so we can pretty much guarantee that adversaries will exploit vulnerabilities within our analytics.

In this case, adversaries deluge the system with false negatives (malware disguised as benign code), causing security analysts to completely ignore alerts or de-prioritize them.

Another example is poisioning attacks, which inject false data with the intent of poisoning the training data set and creating biases to certain classifications.

Researchers at UC Berkeley discovered that, by injecting crafted non-speech noise that sounds like white noise into your voice assistant, commands like “unlock your front door” or “place $1,000 purchase” could be executed.2 This gives you a taste of some of the research that’s been done in AML.

On a positive note, forward-thinking cybersecurity vendors are working in concert with researchers at other organizations to develop simulation experiments with the aim of testing and attacking existing AI algorithms and models.

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