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Machine Learning and Security by David Freeman, Clarence Chio

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Chapter 6. Protecting the Consumer Web

Most of our discussion so far has focused on preventing hackers from getting into a computer or network, detecting them after they’ve achieved a breach, and mitigating effects of the breach. However, it is not necessary for an attacker to breach a network in order to gain economically. In this chapter we consider attackers who use a consumer-facing website or app’s functionality to achieve their goals.

When referring to the “consumer web,” we mean any service accessible over the public internet that provides a product to individual consumers; the service can be free or fee-based. We distinguish the consumer web from enterprise services provided to an organization, and from internal networks within a company.

The consumer web has many different attack surfaces; these include account access, payment interfaces, and content generation. Social networks provide another dimension of vulnerability, as attackers can take advantage of the social graph to achieve their goals.

However, the consumer web also has some built-in properties that work to the defender’s advantage. The foremost of these is scale: any site that is subject to attack is also subject to a much larger amount of legitimate traffic. This means that when building your defense, you have a large database of legitimate patterns you can use to train your algorithms. Anomaly detection, as discussed in Chapter 3, can be appropriate here, especially if you don’t have a lot of labeled data. ...

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