Data Science For Cybersecurity: Pros and Cons

In cybersecurity, your objective is to specify the threats, stop intrusions and attacks and prevent frauds. Data science in cybersecurity can help better recognize and prevent cyber threats. For instance, when it comes to identifying malware and spam, data can help in learning and training objectives to properly inspect malware and spam.

Data science can assist in connecting the dots between minor abnormalities and use them to create a bigger image of what might be going on. To prevent fraud, the method is the same.

Pros and Cons of Data Science in Cybersecurity

Here are the pros and cons of data science in cybersecurity.

Pros of Data Science in Cybersecurity

1. Protection of Data from theft

The loss of data by theft is scary because data theft can result in a severe breach of privacy. Data science can also assist in data protection. Encryption helps to stop attackers from being attacking extremely valuable and sensitive data by providing different ways of protection.

2. Intrusion Detection

It is the most important factor to develop a system that can specify problems within a network and trigger solutions and provide appropriate responses for them. It assists in analyzing their effectiveness and also get to the most appropriate solution for the breach.

3. Safeguarding Information

To protect data, data scientists can develop an algorithm that could detect any issue and consider various detailed patterns to block.

4. Business Challenges

With continuous investment in Information Technology, awareness, and efforts cybersecurity professionals can achieve goals against various other complex business challenges.

Cons of Data Science in Cybersecurity

1. Mastering Data Science is near to impossible

Being a combination of many fields like Statistics, Mathematics, and Computer Science it is now far from possible to master each field.

2. Domain knowledge

A healthcare industry working on an analysis of the genomics sequence will require a suitable employee with some knowledge of genetics and molecular biology. However, it becomes problematic for a Data Scientist from a different background to acquire specific domain knowledge. This also makes it difficult to transfer the domain from one industry to another.

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