Data Science for Cybersecurity; Pros and Cons

Introduction

In this article, we will explain the most important aspects of Data Science in the simplest way because we believe understanding is the key factor for learning. Subjects from different industries would prefer to better learn the landscape of their desire to study area.

The most precise explanation that relate to cybersecurity and data science is FUD or fear uncertainty and doubt. Data science has effectively illustrated FUD based assumptions and shifted the cybersecurity based conclusions more on facts. Many organizations examining their cybersecurity data science needs by employing experts to lend their expertise.

Cybersecurity Data Science is the fastest-growing profession focused on cybersecurity threats.  The Data scientist can pertain to their knowledge to the cybersecurity area to promote and protect against attacks and recognize skeptical behavior. They play a versatile part of a technical expert, analyst and a skilled interpreter and provide the solution for the problem and make the easiest possible ways. Many programmers are developing their techniques to establish better programs to resist threats, by using knowledge of data science.

Microsoft Data Science has become one of the most popular professions and the demand for data experts is thriving. Microsoft data science certification is offering from different portals which are intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning. The professional Microsoft Data Science Certification has the most significant factor in applied learning. Throughout this Professional Certificate course, you are exposed to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will deliver you with practical skills with applicability to real jobs that employers expect from a professional data expert.

How data science  is useful for cybersecurity programs

In cybersecurity, your objective is to specify the threats, stop intrusions and attacks and prevent frauds. Data science in cybersecurity can be used to help better recognize these threats. For instance, when it comes to specifying malware and spam, data from a vast range of copies can be used for  learning and training objectives, so that, malware and spam are properly inspected.

Data science can be used properly to  identify anomalies and abnormalities that may be resulted in by an intruder. Proper preventative measures can be taken to stop it from getting more serious. Data science can assist to connect 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. You can also check bitdefender vs malwarebytes which is the best.

Challenges overcome by Data science in  cybersecurity

Data science is used to overcome challenges in cybersecurity, but it appears with a set of challenges of its own that require to be overcome. Here are some of them.

·       Identifying the problem

To identify spam and malware is a lot easier than to identify behavioral abnormalities. There is a huge  of data available for training purposes to understand clearly.  Data science is used to evaluate all raw users behavior and connect the dots to provide solutions. By using large “data lakes,” you can compare the real-time activity to the data in the lake to help identify threats. The challenge would be having access to all that data, which comes from many logs and the systems.

• Domain expertise

Mechanism is the enormous misconceptions doing rounds and data scientists considered as reasonable at high-end tools.  Data scientists provide decent domain knowledge and gain subject matter expertise. The biggest challenge faced by data scientists is to apply domain knowledge to business solutions. Data scientists are considered as  a bridge between the IT department and the top management.

• Difficulties in protection

Encryption is a proper way of protecting sensitive information, it is added on our, list of big data security portals. Despite the expectation of encryption, this security measure is often ignored. Sensitive data is generally stored in the cloud which assists by the encryption for the to save and secure it.

Pros and Cons of Data Science Cybersecurity

However, there are numerous pros and cons of cybersecurity which we have listed the  Pros and Cons of the data science cybersecurity.

Pros of Data Science in Cybersecurity

1. Protection of Data from theft

The loss of data by theft is scary for the data owner to lose their data because theft of your data could result in a severe breach of privacy.  Data science has also assisted in data protection. Encryption helps to stop attackers from being attacking extremely valuable and sensitive data by providing different ways of protection.

1. Intrusion Detection

It is the most important factor to develop a system that is data-based and 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.

1. Safeguarding Information

Cybersecurity had been used to address this factor. To protect data, it provides an algorithm that could detect any issue and consider various detailed patterns to block.

1. Business Recovery

With continuous investment in Information Technology, awareness and efforts cybersecurity professionals could achieve goals against various other complex challenges. Data Science and big data with the cybersecurity are considered to reach their destination easily.

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.

1. A large amount of domain knowledge required

A health-care 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.

1. Costly

Cybersecurity provided many domains for the purpose of user’s requirement to acquire all domain of cybersecurity is expensive for the ordinary user.

 

Hope this article increases your knowledge and answers your questions regarding data science cybersecurity.

Keep learning!