PinchofKnowledgeonCyberCrime

CyberCrime

Cybercriminals are basically done to buy and sell malware online (bascially on the dark web) while also trading in services that test how robust a virus is, business intelligence dashboards to track malware deployment, and tech support (that’s right — crooks can contact a criminal helpline to troubleshoot their illegal hacking server or other malfeasance!).

Every year, impacting individuals, businesses, and even governments. It is estimated by Experts that cybercrime damages will reach $6trillion by year 2k21, making it one of the most lucrative criminal enterprises in the world.

The Identity Theft

In Identity theft is generally a crime in which someone trys to steals your personal information. Identity theft includes many types of personal information and give rise to fraud, ranging from financial theft to the use of a data to receive for credit.

If a hacker wants to commit identity theft they first need to access enough information of their victim’s personal data to commit the crime. Here are several ways they can get that access:

• Phishing: Cybercrooks use “bait” in the form of fraudulent messages to lure victims to fake sites where they unwittingly enter personal information like usernames, passwords, or bank details.

• Pharming: Taking it one step deeper than phishing, pharming uses malware to reroute unsuspecting internet surfers to fake versions of websites, where they unknowingly enter their personal details.

• Keylogging: This type of malware secretly loging everything you type, capturing your account information and other personal details by reading RAM.

• Sniffing: If you’re connected to an unsecured, unencrypted public Wi-Fi network, hackers can steal your data by “sniffing” your internet traffic with special tools (unless you have a vpn, of course).

Tips to prevent identity theft  :  While hackers have many ways to steal personal data/information, there are also some good ways you can prevent identity theft. Avoid accessing your personal accounts (especially online banking) on public Wi-Fi, and consider setting up a monitoring service to make sure your online accounts haven’t been breached.

Cyberbullying

Cyberbullyng refers to all kinds of online harassment, including stalking, sexual harassment, doxxing (exposing someone’s personal information, like their physical address, online without their consent), and fraping (breaking into someone’s social media and making fake posts on their behalf).

Cryptojacking

Cryptojacking is when hackers break into your device and use it to mine cryptocurrency without your knowledge or consent. Cryptominers do this by using JavaScript to infect your device after you visit an infected website. This can cause performance issues and high electric bills for you — and earn big profits for the cryptojackers. 

Cyberextortion

Cyberextortion is just what it sounds like — a digital version of the nightmare that is extortion. One of the most common forms is ransomware, when hackers infect your computer with malware that encrypts all your files until you pay them a ransom to unlock them. Cyberextortion can also refer to blackmailing victims using their personal info, photos, and video; or threatening businesses using methods like botnet-driven DDOS attacks. 

Cyberespionage

As mentioned, many cybercriminals these days are actually state-sponsored groups. Whether it’s the North Koreans, the Iranians, or even the US’s own NSA-affiliated Equation Group, world powers use hacker groups as one weapon in the complicated matrix of global politics. Stealing classified intelligence and using malware to attack nuclear plants are just two ways in which state-sponsored groups can do some frightening things on the world stage.

What is a Confusion Matrix?

A confusion matrix is a summary of prediction results on a classification problem.

The number of correct and incorrect predictions are summarized with count values and broken down by each class. This is the key to the confusion matrix.

The confusion matrix shows the ways in which your classification model
is confused when it makes predictions.

It gives you insight not only into the errors being made by your classifier but more importantly the types of errors that are being made.

It is this breakdown that overcomes the limitation of using classification accuracy alone.

How to Calculate a Confusion Matrix

Below is the process for calculating a confusion Matrix.

1. You need a test dataset or a validation dataset with expected outcome values.
2. Make a prediction for each row in your test dataset.
3. From the expected outcomes and predictions count:
   1. The number of correct predictions for each class.
   2. The number of incorrect predictions for each class, organized by the class that was predicted.

These numbers are then organized into a table, or a matrix as follows:

• Expected down the side: Each row of the matrix corresponds to a predicted class.
• Predicted across the top: Each column of the matrix corresponds to an actual class.

The counts of correct and incorrect classification are then filled into the table.

The total number of correct predictions for a class go into the expected row for that class value and the predicted column for that class value.

In the same way, the total number of incorrect predictions for a class go into the expected row for that class value and the predicted column for that class value.

This matrix can be used for 2-class problems where it is very easy to understand, but can easily be applied to problems with 3 or more class values, by adding more rows and columns to the confusion matrix.

Let’s make this explanation of creating a confusion matrix concrete with an example.

2-Class Confusion Matrix Case Study

Let’s pretend we have a two-class classification problem of predicting whether a photograph contains a man or a woman.

We have a test dataset of 10 records with expected outcomes and a set of predictions from our classification algorithm.

1 2 3 4 5 6 7 8 9 10 11

Expected, Predicted man, woman man, man woman, woman man, man woman, man woman, woman woman, woman man, man man, woman woman, woman

Let’s start off and calculate the classification accuracy for this set of predictions.

The algorithm made 7 of the 10 predictions correct with an accuracy of 70%.

1 2	accuracy = total correct predictions / total predictions made * 100 accuracy = 7 / 10 * 100

But what type of errors were made?

Let’s turn our results into a confusion matrix.

First, we must calculate the number of correct predictions for each class.

1 2	men classified as men: 3 women classified as women: 4

Now, we can calculate the number of incorrect predictions for each class, organized by the predicted value.

1 2	men classified as women: 2 woman classified as men : 1

We can now arrange these values into the 2-class confusion matrix:

1 2 3

men women men 3 1 women 2 4

We can learn a lot from this table.

• The total actual men in the dataset is the sum of the values on the men column (3 + 2)
• The total actual women in the dataset is the sum of values in the women column (1 +4).
• The correct values are organized in a diagonal line from top left to bottom-right of the matrix (3 + 4).
• More errors were made by predicting men as women than predicting women as men.

Two-Class Problems Are Special

In a two-class problem, we are often looking to discriminate between observations with a specific outcome, from normal observations.

Such as a disease state or event from no disease state or no event.

In this way, we can assign the event row as “positive” and the no-event row as “negative“. We can then assign the event column of predictions as “true” and the no-event as “false“.

This gives us:

• “true positive” for correctly predicted event values.
• “false positive” for incorrectly predicted event values.
• “true negative” for correctly predicted no-event values.
• “false negative” for incorrectly predicted no-event values.

We can summarize this in the confusion matrix as follows:

1 2 3

event no-event event true positive false positive no-event false negative true negative

This can help in calculating more advanced classification metrics such as precision, recall, specificity and sensitivity of our classifier.

For example, classification accuracy is calculated as true positives + true negatives.

The dark web

After a data heist, the thieves need to turn that stolen data into profits — this is where the darkweb  comes in. The dark web is a part of the internet that you won’t find in your everyday Google searches, because you need special software, such as the Tor Browser, to access it. And you need to know where you’re going, because search engines don’t index the dark web.