Designing Autonomous AI by Kence Anderson

Author:Kence Anderson
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2022-06-10T00:00:00+00:00

Detect

Have you ever played the childhood game “one of these things is not like the other?” In this game, you look at multiple objects (see Figure 4-16 for an example) to determine which one is different (somehow doesn’t match the pattern). When you play this game, you’re looking for anomalies.

Figure 4-16. Some of these objects look similar but belong in different categories.

Detecting anomalies is an important perception skill that informs decision-making. One company that I worked with wanted to use AI for cybersecurity to stop cyberattacks like the distributed denial-of-service (DDoS) attack in 2018 that used over 1,000 different autonomous bots to disrupt the GitHub code repository site for over 20 minutes. In a DDoS attack, hackers purposefully generate fake traffic to a website—so much traffic, in fact, that the website can’t function. The first step in countering a DDoS attack is detecting one. It’s hard to tell whether a sudden spike in traffic is due to a legitimate spike in customer demand (this would be a very good thing) or the beginning of a DDoS attack (a very bad thing). My prescription was that the AI should have one module that learns to detect anomalies in web traffic and classify them as either a traffic spike or DDoS attack and another module that accepts the first module’s conclusions and passes them to the decision-making module, which takes action to stop attacks but lets valuable, legitimate traffic through.

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