Cyber Security and Confusion Matrix
What is Confusion Matrix?
Confusion Matrix is a Matrix used to measure the performance of Machine Learning, mainly in Supervised Learning. Where each row represents instances of the actual class and each column represents instances of the predicted class. as the name suggests Confusion Matrix Because there in machine learning it will predict a false value or it will predict wrong because the model of machine learning confuse in actual and predict value.
Why does it happen?
As we know machine learning first recognize pattern and according to model whatever it predict because of trained model which ultimately store some pattern to recognize or predict value . It happens because whatever pattern we make it having some missing feature that will reflect on the wrong prediction but according to the pattern it is right but actually it wrong.
In Diagram :
TP: True Positive
TN: True Negative
FT: False Positive
FN: False Negative
here, there are two types of error which false negative and false positive.
What is Mean by this TP, TN, FT, FN?
let see, for example, we take a database of titanic where we will predict according to the database and model how many survived and how many not survived. After we made a model we find that 50 people are survived means we predict 50 people are survived. according to the positive approach its a good or positive news. but what we found our prediction is wrong . from the 50 there is 40 survived and 10 is not, so 40 is known as True Positive and 10 is known as False Positive .like that according to model prediction we got 30 not survived but in actual 40 is not survived and 10 is survived, Here we predict wrong in negative approach where that 40 is known as True Negative and 10 is known as False Negative.
There are 2 Typer of Error
Type 1: False Positive: It is a very harmful error. Because it predicts wrong in positive approach which will lead many diasters.
Type 2: False Negative: It is an error where it will predict wrong in the Negative Approach value.
We can measure accuracy in machine learning :
Accuracy = TN+TP / (TN+TP+FN+FP)
How is Confusion Matrix related to cybercrime attacks?
We are in the world of computer science. Most of the data that are very important to us or the data which protect our privacy are online. We are using different social media, banking, official work everything online. Yes, it has made our life very easy; just in a single click, we can do many things, and in a single click, we can access and store our data online. But there is also the risk with this.
As internet usage has grown in number, cyber attacks and cyber threats have been huge issues.
What is Cyber Attack?
A cyber attack is an attack on the servers or computer in the public or private internet where the attacker seeks to expose, damage, alter, disable or try stealing the current data or changing the system configuration, and that is done unauthorized. The act of doing this cyberattack is called cybercrime.
- Some of the examples of cyber attacks are:
- Stealing corporate attack and hacking servers
- Exposing someone privacy and harassing
- Stealing bank details and card details
- Phishing Sites and Scam
- IoT device hacking
- Flooding the servers with unnecessary traffic
These are a few examples of Cyber Attacks. There are many examples in the list.
What is the solution being used in the industry to prevent it?
The IT industry is trying its best to protect the data and protect servers. Many different techniques and applications have been developed to prevent cybercrimes. We even have some organization which is specifically working for the security of the Internet. Different techniques are being used. Some of the techniques that we can see or are using currently are:
- Protecting Data In Cloud
- End to End Encryptions
- SSH Key and Certificates
- Automate Monitoring Process
And Many More…
We have lots of other options and techniques being used by different users and service providers.
Here we will discuss one of the approaches and briefly discuss a small component of that approach.
Machine Learning and Cyber Security
Nowadays, it has been common that every company usually has a lot of data to handle. Here we talk more about the servers and storage security. Human efforts are less likely to be useful and work nowadays, and they are slow also. We also need everything to be automatic, and manual always has some issues. Machine Learning helps the team to manage the servers and keep them safe.
The machine, when combining with human intelligence we can achieve great things at great speed. On the basics of the older pattern of attacks and the threats that the servers might have to deal with, Machine can be trained to recognize that pattern, and every time a new attack happens or when the traffic is being exchanged Machine Learned model can keep an eye in every packet or their activities. When some malicious activity or attack happens, the Machine can warn the security department team, and then the team can look upon that threat before some big mishap may happen. In some cases, Machine Even can solve the issue as set by the user what to do in such a situation. Like shutting the ingress networks or blocking some suspected IP or network for a limited time until developers look upon it.
But some cases where we can’t able to detect malicious IP and system is been hacked because according to a pattern we set in a machine we consider as Normal IP but in Actual it is not. and this error is False Positive Error which very harmful. and sometimes machines will block normal IP because of IP pattern is not match with regular IP . this kind of error is known as False Negative.
Conclusion: We will moving towards to Automation and Machine Learning world, and with it, cybercrime also increases. Hackers also will find a new way to hack machines. Even Machines Learning having good accuracy but we will always need to be aware of that kind of situation also be happening in the future.