Task 05 👨🏻‍💻

Task Description 📄

📌 Create a blog/article/video about cyber crime cases where they talk about confusion matrix or its two types of error.

❄️ Submit the link of blog/article or video

⚠️ Note: This is a compulsory individual task.

⏱️ Deadline: 6th June | 11:59P.M

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Confusion Matrix —

A Confusion matrix is the comparison summary of the predicted results and the actual results in any classification problem use case. The comparison summary is extremely necessary to determine the performance of the model after it is trained with some training data.

For a binary classification use case, a Confusion Matrix is a 2×2 matrix which is as shown below

From the above figure:
We have,

Actual Class 1 value= 1 which is similar to Positive value in a binary outcome.

Actual Class 2 value = 0 which is similar to a negative value in binary outcome.

The left side index of the confusion matrix basically indicates the Actual Values and the top column indicates the Predicted Values.

There are various components that exist when we create a confusion matrix. The components are mentioned below

  • Positive(P): The predicted result is Positive (Example: Image is a cat)
  • Negative(N): the predicted result is Negative (Example: Images is not a cat)
  • True Positive(TP): Here TP basically indicates the predicted and the actual values is 1(True)
  • True Negative(TN): Here TN indicates the predicted and the actual value is 0(False)

False Negative(FN): Here FN indicates the predicted value is 0(Negative) and Actual value is 1. Here both values do not match. Hence it is False Negative.

False Positive(FP): Here FP indicates the predicted value is 1(Positive) and the actual value is 0. Here again both values mismatches. Hence it is False Positive.

Accuracy and Components of Confusion Matrix

After the confusion matrix is created and we determine all the components values, it becomes quite easy for us to calculate the accuracy. So, let us have a look at the components to understand this better.

Classification Accuracy

From the above formula, the sum of TP (True Positive) and the TN (True Negative) are the correct predicted results. Hence in order to calculate the accuracy in percentage, we divide with all the other components. However, there are some problems in the accuracy and we cannot completely depend on it.

Let us consider that our dataset is completely imbalanced. In this Scenario, 98% accuracy can be good or bad based on the problem statement. Hence we have some more key terms which will help us to be sure about the accuracy we calculate. The terms are as given below:

TPR (True Positive Rate) or Sensitivity:

True Positive rate which is also known as Sensitivity measures the percentage of the True Positive with respect to the Total Actual Positives which is indicated by (TP+ FN)

TNR (True Negative Rate) or Specificity:

True Negative Rate or Specificity measures the proportion of actual negatives with respect to the Total Negatives

False Positive Rate(FPR):

False Positive Rate is the percentage of Predicted False Positive (FP) to the Total No of Predicted Positive Results (TP + FP).

False Negative Rate (FNR):

False Negative Rate is the percentage of Predicted False Negative (FP) to the Total No of Predicted Negative Results (TN + FN).

An Overview of False Positives and False Negatives

Understanding the differences between false positives and false negatives, and how they’re related to cybersecurity is important for anyone working in information security. Why? Investigating false positives is a waste of time as well as resources and distracts your team from focusing on real cyber incidents (alerts) originating from your SIEM.

On the flip side, missing false negatives (uncaught threats) increases your cyber risk, reduces your ability respond to those attackers, and in the event of a data breach, could lead to the end of your business…

What Are False Positives?

False positives are mislabeled security alerts, indicating there is a threat when in actuality, there isn’t. These false/non-malicious alerts (SIEM events) increase noise for already over-worked security teams and can include software bugs, poorly written software, or unrecognized network traffic.

By default, most security teams are conditioned to ignore false positives. Unfortunately, this practice of ignoring security alerts — no matter how trivial they may seem — can create alert fatigue and cause your team to miss actual, important alerts related to a real/malicious cyber threats (as was the case with the Target data breach).

These false alarms account for roughly 40% of the alerts cybersecurity teams receive on a daily basis and at large organizations can be overwhelming and a huge waste of time.

What Are False Negatives?

False negatives are uncaught cyber threats — overlooked by security tooling because they’re dormant, highly sophisticated (i.e. file-less or capable of lateral movement) or the security infrastructure in place lacks the technological ability to detect these attacks.

These advanced/hidden cyber threats are capable of evading prevention technologies, like next-gen firewalls, antivirus software, and endpoint detection and response (EDR) platforms trained to look for “known” attacks and malware.

No cybersecurity or data breach prevention technology can block 100% of the threats they encounter. False positives are among the 1% (roughly) of malicious malware and cyber threats most methods of prevention are prone to miss.

Strengthening Your Cybersecurity Posture

The existence of both false positives and false negatives begs the question: Does your cybersecurity strategy include proactive measures? Most security programs rely on preventative and reactive components — — establishing strong defenses against the attacks those tools know exist. On the other hand, proactive security measures include implementing incident response policies and procedures and proactively hunting for hidden/unknown attacks.

Thanks -





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