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Designing Human-Targeted Random IDs

Designing Human-Targeted Random IDs

NOTE: We don't deal here with technical IDs used as primary keys in relational databases. See my previous article here if you seek a great way to generate them.

Context

During one of my recent projects, I have been asked to design a scheme of IDs highly usable by humans. The business requirement was mainly to create pseudo-random values that can't be inferred or guessed in order to be used as a secret token printed on some official documents for future controls.

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Evaluation of ML Algorithms for Intrusion Detection Systems

The last decade has seen rapid advancements in machine learning techniques enabling automation and predictions in scales never imagined before. This further prompts researchers and engineers to conceive new applications for these beautiful techniques. It wasn’t long before machine learning techniques were used in reinforcing network security systems.

The most common risk to a network’s security is an intrusion such as brute force, denial of service, or even an infiltration from within a network. With the changing patterns in network behavior, it is necessary to switch to a dynamic approach to detect and prevent such intrusions. A lot of research has been devoted to this field, and there is a universal acceptance that static datasets do not capture traffic compositions and interventions.