MECHANISM OF AL, CYBERSECURITY, AND CLOUD

1) STARTING FROM SCRATCH OF CYBERSECURITY ANALYSIS;

Let’s have an explanation that how things are being firstly let us understand that what is the security level of ML.

1. Baseline:

It starts from baseline in which we enable simply security that is of passwords applying, etc

2. Evolving;

Mean that is under-process the period in which the security mechanism is being developed.

3. Intermediate:

This can be said as half of the security has been achieved like using Ip address recognitions, subnets issues resolving.

4. Advanced;

After intermediate, the security goes towards the advanced level the other is like identifying the emails that are junk or spam emails.

5. Innovative;

The security mechanism brings innovation. Its examples are like the modern firewall applying in the computer system.

1) WHAT ROLE ALGORITHMS PLAY;

TABLE SHOWS SOME IMPORTANT AI ALGORITHMS WHICH HELPS TO IMPROVE SECURITY

2) HOW TYPE OF DATA IN AI HELP TO SUPPORT SECURITY;

User Data: Collection and analyzing user access and activities from AD, Proxy, VPN, and applications.

Application Data: Collection and analysis of calls, data exchange.

Endpoint Data: Analyzing the internal endpoints such as files, processes, memory, registry, and many more by installing agents.

Network Data: Collecting and analyzing the packets.

3) APPLYING MACHINE LEARNING TO SECURITY DOMAINS;

As we know that the machine learns is categorized into categories there data security is also divided each layer is responsible to perform, manage, invest and control the relevant data security.

1. Unsupervised –continuous range;

This includes the security mechanism like

1) Behavioral analytics,

2) IoT security,

3) Insider threat detection,

4) network domain detection,

5) network traffic profiling.

2.Supervised –batch range;

1) Ruleset generation

2) Malware generation

3) C2 detection

4) THE ML DESIGN OF CYBERSECURITY;

So, a simple flow of how things happen concerning ML security is shown in the diagram where it starting from the entering of data from Data Acquisition and data curation it goes in the life cycle of phases: BUILD THE DATA, TRAIN THE DATA, AND TEST THE DATA when in a given time these processes are completed the mechanism move towards the deployment where the results can be seen if the results are not satisfactory it then move towards the update section where the updates are being completed for the update we will go for the same life cycle phase.