Confusion matrix and Cyber Crime
What is a Confusion Matrix?
The confusion matrix gives very fruitful information about the predicted performance of the estimator or model that use in machine learning. Let’s see a confusion matrix.
What is inside the confusion matrix?
Actual values are true binary values “0” and”1". The prediction value that comes after fitting the model is also confusing because it is not predicted all values properly. So, these four terms are born to know the evaluation performance.
Let’s decipher the matrix:
· The target variable has two values: Positive or Negative
· The columns represent the actual values of the target variable
· The rows represent the predicted values of the target variable
Understanding True Positive, True Negative, False Positive and False Negative in a Confusion Matrix
We can obtain four different combinations from the predicted and actual values of a classifier:
· True Positive(TP): The number of times our actual positive values are equal to the predicted positive. You predicted a positive value, and it is correct.
· The predicted value matches the actual value
· The actual value was positive and the model predicted a positive value
· True Negative(TN): The number of times our actual negative values are equal to predicted negative values. You predicted a negative value, and it is actually negative.
· The predicted value matches the actual value
· The actual value was negative and the model predicted a negative value.
· False Positive(FP): — Type 1 error.
The number of times our model wrongly predicts negative values as positives. You predicted a negative value, and it is actually 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
· False Negative(FN): Type 2 error
The number of times our model wrongly predicts negative values as positives. You predicted a negative value, and it is actually positive.
· 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.
Accuracy and Components of Confusion Matrix
To find how accurate our model is, we use the following metrics:
- 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.
- Accuracy: 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.
- Recall: It is used to calculate the model’s ability to predict positive values. It is the true positives divided by the total number of actual positive values.
- 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.
The basic definitions for Regression and classification we use in machine learning for confusion matrix.
Regression:
Regression (or prediction) is simple. The knowledge about the existing data is utilized to have an idea of the new data. Take an example of house prices prediction. In cybersecurity, it can be applied to fraud detection. The features (e.g., the total amount of suspicious transaction, location, etc.) determine a probability of fraudulent actions.
Classification:
Classification is also straightforward. Imagine you have two piles of pictures classified by type (e.g., dogs and cats). In terms of cybersecurity, a spam filter separating spams from other messages can serve as an example. Spam filters are probably the first ML approach applied to Cybersecurity tasks.
Cyber Crime:
Cybercrime has become a very recognizable theme in the present worldwide features. Particularly, somewhat recently, Internet use has been developing quickly. Be that as it may, as the Internet turns into a piece of the everyday exercises, cybercrime is additionally on the ascent. Cybercrime will cost almost $6 trillion for each annum by 2021 according to the network protection adventures report in 2020. For criminal operations, cybercriminals use any organization processing gadgets as an essential method for correspondence with a casualties’ gadgets, so assailants get benefit as far as account, exposure and others by misusing the weaknesses over the framework. Cybercrimes are consistently expanding day by day. Assessing cybercrime assaults and giving defensive measures by manual strategies utilizing existing specialized methodologies and furthermore examinations has regularly neglected to control cybercrime assaults. Existing writing in the space of cybercrime offenses experiences an absence of a calculation techniques to anticipate cybercrime, particularly on unstructured information.
Accordingly, this investigation proposes an adaptable computational device utilizing AI strategies to dissect cybercrimes rate at a state shrewd in a country that assists with characterizing cybercrimes. Security investigation with the relationship of information scientific methodologies help us for dissecting and ordering offenses from India-based incorporated information that might be either organized or unstructured. The fundamental strength of this work is trying examination reports, which characterize the offenses precisely with 99% exactness.
At present, cybercrime is being utilized with different wordings like PC wrongdoing, e-wrongdoing, Internet wrongdoing, and so forth cybercrime model for characterization that is subject to the part of the PCs’ insight in cybercrime. Also, different scientific classifications are proposed by the specialists, researchers and clients to arrange the cybercrimes. The developing pace of cybercrime dangers is expanding step by step. Presently, there is no secure efficient and dependable apparatus on surveys of cybercrimes because of an absence of record upkeep at concerned workplaces in light of different reasons like casualties’ presumptions on police reaction, absence of consciousness of the clients about IT (data innovation) follow up on cybercrimes and the failure of the casualties to be perceived that they have been deceived.
As the Digital Age flourishes, more and more people are switching to working online and having businesses that revolve around all things digital and technological. A well-known example of this is the marketing industry. In recent years the marketing industry has converted to being almost entirely digital; thus creating the genre of marketing: digital marketing. Almost every company has or has the ability to reap the benefits of digital marketing, making this industry a lucrative and important one.
As more people are beginning or expanding their careers in digital marketing, there are some things that they should know; most notably, how to keep their digital marketing company safe from cybercrime. Cybercrime can impact and ruin people’s lives as hackers can steal, exploit, and tamper with personal information and accounts. And for a business that exists only digitally, it’s important to take the necessary precautions in order to keep the business safe.
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