Confusion Matrix and Cyber Security

Introduction to Confusion Matrix -

The confusion matrix is a matrix used to determine the performance of the classification models for a given set of test data. It can only be determined if the true values for test data are known. The matrix itself can be easily understood, but the related terminologies may be confusing. Since it shows the errors in the model performance in the form of a matrix, hence also known as an error matrix. Some features of Confusion matrix are given below:

  • For the 2 prediction classes of classifiers, the matrix is of 2*2 table, for 3 classes, it is 3*3 table, and so on.
  • The matrix is divided into two dimensions, that are predicted values and actual values along with the total number of predictions.
  • It looks like the below table:

The above table has the following cases:

1.True Negative:

  • The predicted value matches the actual value
  • The actual value was negative and the model predicted a negative value

2.True Positive:

  • The predicted value matches the actual value
  • The actual value was positive and the model predicted a positive value

3.False Negative:

  • The predicted value was falsely predicted
  • The actual value was positive but the model predicted a negative value
  • Also known as the Type 2 error

4.False Positive:

  • The predicted value was falsely predicted
  • The actual value was negative but the model predicted a positive value
  • Also known as the Type 1 error

Need for Confusion Matrix in Machine learning -

  • It evaluates the performance of the classification models, when they make predictions on test data, and tells how good our classification model is.
  • It not only tells the error made by the classifiers but also the type of errors such as it is either type-I or type-II error.
  • With the help of the confusion matrix, we can calculate the different parameters for the model, such as accuracy, precision, etc.

Introduction to Cyber Security -

Cyber security is the application of technologies, processes and controls to protect systems, networks, programs, devices and data from cyber attacks. It aims to reduce the risk of cyber attacks and protect against the unauthorized exploitation of systems, networks and technologies.

  • Network security is the practice of securing a computer network from intruders, whether targeted attackers or opportunistic malware.
  • Application security focuses on keeping software and devices free of threats. A compromised application could provide access to the data its designed to protect. Successful security begins in the design stage, well before a program or device is deployed.
  • Information security protects the integrity and privacy of data, both in storage and in transit.
  • Operational security includes the processes and decisions for handling and protecting data assets. The permissions users have when accessing a network and the procedures that determine how and where data may be stored or shared all fall under this umbrella.
  • Disaster recovery and business continuity define how an organization responds to a cyber-security incident or any other event that causes the loss of operations or data. Disaster recovery policies dictate how the organization restores its operations and information to return to the same operating capacity as before the event. Business continuity is the plan the organization falls back on while trying to operate without certain resources.
  • In recent years, botnets have been considered one of the major security threats among all types of malware operating on the Internet . Botnets have been constantly evolving on the global Internet in both scale and sophistication of control techniques. Each botnet member is called a bot. A bot is a malware created by a hacking group (called botmaster) that allows them to control infected computer systems remotely .
  • Now here in Confusion Matrix “False PositiveType II error tell that these attack are not there but Machine Learning Model is giving wrong information . It will be very critical because if we don’t know that attack are there then we can’t protect our data from cracker .
  • False Negative” Type I error tell that there are attack going on but actually not (ML model predicted wrong)
  • True Positive” and “True Negative” in both cases ML model predict the right answer.

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Govind Bhardwaj

Govind Bhardwaj

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