Entropy in computing is like the spice in your digital soup - without enough of it, everything tastes bland and predictable
But what exactly is entropy in this context, and why should you care? Buckle up, because we’re about to dive into the realm of randomness, where not all chaos is created equal.
What is entropy?
Entropy in computing terms is a measure of randomness or unpredictability in data. The higher the entropy, the more random and unpredictable the data is. This concept is crucial in cryptography and security, where unpredictability is key to keeping information safe.
Before we dive deeper into entropy in computing, let’s quickly define what we mean by “strings” in computing. Essentially, in programming a string is a sequence of characters. It could be a word, a sentence, or even a whole paragraph.
Now, let’s explore how entropy (or the lack thereof) affects various aspects of computing security.
The password predicament: low entropy in initial passwords
Imagine you’re setting up a new system that generates initial passwords for users. You might be tempted to use something predictable, like:
- The current timestamp
- The user’s ID number
- A simple pattern like “Welcome123!”
The problem: these methods have low entropy. They’re predictable, and that’s exactly what we don’t want in passwords. There are real-world implications; if an attacker knows your password generation method, they could potentially guess a large number of user passwords, especially if they know when accounts were created.
In this case, best practice would be to use a cryptographically-secure random generator to create initial passwords. And for the love of all things secure, please enforce password changes on first login
The entropy vacuum: common low-entropy practices
Unix timestamp (Epoch):
Using Unix timestamps (the number of seconds since January 1, 1970) as seeds for random number generation or as part of filenames might seem clever, but it’s about as unpredictable as a clock (because it is). For example: `file-test-1631234567.txt`
The problem: anyone who knows when the file was created can guess the filename. It’s like hiding your house key under the welcome mat – the first place any self-respecting burglar would look.
Base64 encoding
Base64 encoding is not encryption. I repeat, BASE64 IS NOT ENCRYPTION. It’s more like writing your password in a different alphabet – anyone who knows the alphabet can read it. For example: `cGFzc3dvcmQxMjM=` (This is “password123” in Base64).
The problem: it’s easily reversible. Using Base64 to “hide” sensitive information is like wearing a name tag to a masquerade ball.
MD5 for password hashing
Oh, MD5. The participation trophy of cryptographic hash functions. It’s fast, it’s simple, and it’s about as secure as a papier-mâché safe. It was introduced in 1992 and weaknesses were identified in 1996 and 2005. By 2008, the Carnegie Mellon Software Engineering Institute stated it is “cryptographically broken and unsuitable for further use”.
The problem: MD5 is vulnerable to collision attacks and can be cracked faster than you can say “data breach.” Best practice would be to use modern, slow hash functions designed for password hashing, like bcrypt, scrypt, or Argon2.
## JWTs and QR Codes: when bad keys happen to good intentions
Even JWTs can fall victim to the entropy vacuum. Here’s one scenario: you’re generating JWTs or QR codes with a secret key that’s… let’s say, less than optimal. For example:
“secret”
“1234567890”
The name of your company
The problem: low-entropy keys are vulnerable to brute-force attacks. Best practice would be to use a cryptographically secure random generator to create your secret keys. Ensure you make them long – like, “I hope I never have to type this manually” long!
Enhancing entropy in computing: best practices
- Use cryptographically-secure random generators: your language of choice probably has one. In Python, it’s the “secrets” module. In Java, it’s “SecureRandom”. Use them.
- Salt your hashes: add a unique, random string to each password before hashing. It’s like adding a secret ingredient to each user’s password soup!
- Implement rate limiting: this won’t increase entropy, but it will make low-entropy systems harder to brute force. It’s like adding a timeout to a guessing game.
- Educate users: encourage (or enforce!) the use of strong, unique passwords. Yes, they’ll complain. No, you shouldn’t cave in.
- Regular security audits: periodically review your systems for low-entropy vulnerabilities. Think of it as spring cleaning, but for your code.
In conclusion
Entropy in computing is not just a theoretical concept; it has real-world implications for security. Low entropy in passwords, keys, and other security mechanisms is like building a fortress with cardboard walls. It might look secure at first glance, but it won’t stand up to any serious attack.
Remember, in the world of digital security, predictability is the enemy. Embrace the chaos of high entropy, and may your secrets remain secret, your passwords remain uncracked, and your data remain secure. After all, in the grand casino of cyber security, you want the odds stacked in your favour. Nothing stacks those odds quite like a healthy dose of entropy.
