What Is Cyber Crime and How to protect yourself against cybercrime? What is Confusing Matrix In Machine Learning?

What is cyber crime →

Cybercrime is criminal activity that either targets or uses a computer, a computer network or a networked device.

Types of cybercrime →

Here are some specific examples of the different types of cybercrime:

  • Email and internet fraud.
  • Identity fraud (where personal information is stolen and used).
  • Theft of financial or card payment data.
  • Theft and sale of corporate data.
  • Cyberextortion (demanding money to prevent a threatened attack).
  • Ransomware attacks (a type of cyberextortion).
  • Cryptojacking (where hackers mine cryptocurrency using resources they do not own).
  • Cyberespionage (where hackers access government or company data).

Mainly 6 Reasons Why Cybercriminals Love the New Business Model

Beginning in 2006, innovations in malware, banking Trojans and ransomware created a new type of business model for cybercriminals: Rather than concentrating all their efforts on penetrating high-quality targets, they can steal small amounts of money from numerous victims.

  1. Attack skill level is low compared to techniques such as spear-phishing — regular olé’ phishing is good enough for weak targets.
  2. Highly coveted zero-day vulnerabilities are no longer required for profitable attacks — mainstream CVE vulnerabilities with known exploits and existing patches will do, as many victims don’t patch regularly.
  3. Any standard endpoint is a potential source of revenue, making lateral movement toward the crown jewels irrelevant.
  4. When you attack the world, the sky is the limit — the amount of potential revenues is endless.
  5. Less effort and more profit means better ROI.

How to protect yourself against cybercrime →

5 ways to protect yourself from cybercrime →

1. Keep everything up to date

Many breaches, including the 2017 one at the Equifax credit bureau that exposed the financial information of almost every American adult, boil down to someone leaving out-of-date software running. Most major computer companies issue regular updates to protect against newly emerging vulnerabilities.

2. Use strong, unique passwords

Remembering passwords, especially complicated ones, isn’t fun, which is why so much work is going into finding better alternatives. For the time being, though, it’s important to use unique passwords that are different for each site, and not easy-to-hack things like “123456” or “password.”

3. Enable multi-factor authentication

In many situations, websites are requiring users not only to provide a strong password but also to type in a separate code from an app, text message or email message when logging in. It is an extra step, and it is not perfect , but multi-factor authentication makes it much harder for a hacker to break into your accounts.

4. Encrypt and back up your most important data

If you can, encrypt the data that’s stored on your smartphone and computer. If a hacker copies your files, all he’ll get is gibberish, rather than, for instance, your address book and financial records. This often stalling software or changing system settings. Some manufactures do this without users even knowing, which helps improve everyone’s security.

5. Be careful using public Wi-Fi

When using public Wi-Fi, anyone nearby who is connected to the same network can listen in on what your computer is sending and receiving across the internet. You can use free browsers like Tor, which was originally developed to provide secure communication for INDIAN NAVY to encrypt your traffic and camouflage what you’re doing online.

What is Confusing Metrix in Machine Learning ?

The following are the topics :

  • What are Confusion Matrices, and why do we need them?
  • How to create a 2x2 Confusion Matrix?
  • Confusion Matrix Metrics
  • Scaling a Confusion Matrix
  • Confusion Matrix with Python

What Are Confusion Matrices, and Why Do We Need Them?

Classification Models have multiple categorical outputs. Most error measures will calculate the total error in our model, but we cannot find individual instances of errors in our model. The model might misclassify some categories more than others, but we cannot see this using a standard accuracy measure.

How to Create a 2x2 Confusion Matrix?

We can obtain four different combinations from the predicted and actual values of a classifier:

  • False Positive: The number of times our model wrongly predicts negative values as positives. You predicted a negative value, and it is actually positive.
  • True Negative: The number of times our actual negative values are equal to predicted negative values. You predicted a negative value, and it is actually negative.
  • False Negative: The number of times our model wrongly predicts negative values as positives. You predicted a negative value, and it is actually positive.

Confusion Matrix Metrics

In this case:

Accuracy = (86 +79) / (86 + 79 + 12 + 10) = 0.8823 = 88.23%

In this case,

Precision = 86 / (86 + 12) = 0.8775 = 87.75%

In this case,

Recall = 86 / (86 + 10) = 0.8983 = 89.83%

Scaling a Confusion Matrix

To scale a confusion matrix, increase the number of rows and columns. All the True Positives will be along the diagonal. The other values will be False Positives or False Negatives.

Confusion Matrix With Python

We’ll build a logistic regression model using a heart attack dataset to predict if a patient is at risk of a heart attack.

Conclusion

The Best Guide to Confusion Matrix, we have looked at what a confusion matrix is and why we use confusion matrices. We then looked at how to create a 2X2 confusion matrix and calculate the confusion matrix metrics using it. We took a look at how confusion matrices can be scaled up to include more than two classification classes and finally got hands-on experience with confusion matrices by implementing them in Python.

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