Ajay Mory

Jun 6, 2021

10 min read

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

What is cyber crime →

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

  1. Attacks require less effort as they target “low-hanging fruit” (i.e., individuals or organizations with sub-par security).
  2. Attack skill level is low compared to techniques such as spear-phishing — regular olé’ phishing is good enough for weak targets.
  3. 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.
  4. Any standard endpoint is a potential source of revenue, making lateral movement toward the crown jewels irrelevant.
  5. When you attack the world, the sky is the limit — the amount of potential revenues is endless.
  6. 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

2. Use strong, unique passwords

3. Enable multi-factor authentication

4. Encrypt and back up your most important data

5. Be careful using public Wi-Fi

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?

How to Create a 2x2 Confusion Matrix?

  • True Positive: The number of times our actual positive values are equal to the predicted positive. You predicted a positive value, and it is correct.
  • 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

  • Accuracy: The accuracy is used to find the portion of correctly classified values. It tells us how often our classifier is right. It is the sum of all true values divided by total values.

In this case:

  • Precision: Precision is used to calculate the model’s ability to classify positive values correctly. It is the true positives divided by the total number of predicted positive values.

In this case,

  • Recall: It is used to calculate the model’s ability to predict positive values. “How often does the model predict the correct positive values?”. It is the true positives divided by the total number of actual positive values.

In this case,

  • F1-Score: It is the harmonic mean of Recall and Precision. It is useful when you need to take both Precision and Recall into account.

Scaling a Confusion Matrix

Confusion Matrix With Python

Conclusion