Hello everyone, we’ll be discussing What is Cyber Crime, What is confusion matrix and What role it plays in the world of cybersecurity.
So let’s start by understanding these simple terms:-
What is Cyber Crime ?
Cybercrime is a criminal activity that either targets or uses a computer, a computer network, or a networked device. Most, but not all, cybercrime is committed by cybercriminals or hackers in order to make money.
Cybercrime is carried out by individuals or organizations. Some cybercriminals are organized, use advanced techniques, and are highly technically skilled whereas rest are novice hackers.
Types of Cybercrime
Some of the different types of cybercrimes:
• 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 cyber extortion.
•Cryptojacking — where hackers mine cryptocurrency using resources they do not own.
•Cyberespionage — where hackers access government or company data.
Some Common types of Cyber Attacks
▪️ Malware - is a term used to describe malicious software, including spyware, ransomware, viruses, and worms.
▪️ Man-in-the-middle attack.
▪️ SQL injection.
▪️ Denial-of-service attack - A denial-of-service attack (DoS attack) is a cyber-attack in which the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to the Internet.A denial-of-service (DoS) attack occurs when legitimate users are unable to access information systems, devices, or other network resources due to the actions of a malicious cyber threat actor. Services affected may include email, websites, online accounts (e.g., banking), or other services that rely on the affected computer or network.
How do you avoid being part of the problem ?
While there is no way to completely avoid becoming a target of a DoS or DDoS attack, there are proactive steps administrators can take to reduce the effects of an attack on their network.
▪️ Enroll in a DoS protection service that detects abnormal traffic flows and redirects traffic away from your network.
▪️ Create a disaster recovery plan to ensure successful and efficient communication, mitigation, and recovery in the event of an attack.▪️Install and maintain antivirus software.
▪️ Install a firewall and configure it to restrict traffic coming into and leaving your computer.
▪️ Evaluate security settings and follow good security practices in order to minimalize the access.
What is Confusion Matrix?
When we have data, then after data cleaning, pre-processing, the first step we do is to feed it to a model and get output in probabilities. But how can we measure the effectiveness of our model. We know that, Better the effectiveness, better the performance, and this is exactly what we want. And this is where the Confusion matrix comes into the limelight.
Confusion Matrix is a performance measurement for machine learning classification.
Confusion Matrix is a concept that is used to find the accuracy of the model that we create in Machine learning or we can explain it as a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known.
The basic terms of the Confusion matrix are:
i) True Positive [ TP ]: In TP, the Machine Learning model predicted right and it was actually right.
ii) True Negative [ TN ]: In TN, the Machine Learning model predicted right but actually it was the wrong prediction, also called False alarm.
iii) False Positive [ FP ]: In FP, the model predicts the wrong but actually it was right
iv) False Negative [FN ]: In FN, the model predicted wrong and actually it as wrong.
There are basically two types of error in the confusion matrix:
a) False Negative
b) False Positive
The most dangerous error is False Positive [FP] error which states that the machine predicted false but it was not false it was true.
For example, the machine predicted student fails but actually student was a pass.
This error causes problems in the cybersecurity world where the tools used are based on machine learning or ai, it may give a False Negative error that may cause dangerous impacts.
Therefore the role of the confusion matrix is important in the field of machine learning.
For calculating the accuracy of the model:
You can compute the accuracy-test from the confusion matrix:
Need for Confusion Matrix in Machine learning:
1. It evaluates the performance of the classification models, when they make predictions on test data, and tells how good our classification model is.
2. 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.
3. With the help of the confusion matrix, we can calculate the different parameters for the model, such as accuracy, precision, etc.
This research presents new cyber attack detection and classification system to classify cyber-attacks. In this, we developed the performance of IDS using a parallel support vector machine for distributed cyber-attack detection and classification. The new PSVM is shown more efficient for the detection and classification of different types of cyber-attacks compared to SDF. The experimental results on the KDD99 benchmark dataset manifest that the proposed algorithm achieved a high detection rate on different types of network attacks.
Thank You For Reading!!!