5 Promising Ways Big Data Deters Cybersecurity Threats

Undoubtedly, companies are blind, deaf, and in the middle of a freeway without big data analytics. Data is the new science whereas big data leverages the answer. Data production rates are evolving at a tremendous pace simultaneously with the human population. Humans produce mind-boggling amounts of data (2.5 quintillion bytes)  on a regular basis and the pace is only accelerating with the evolution of the Internet of Things (IoT). The world has generated 90% of data only in the past two years. Also, according to certain predictions, it is predicted that the world will store 200 zettabytes of data by the year 2025. 

Cybercrimes are progressing by leaps and bounds in parallel to the data production rate. There would be no wrong in saying that cyber attacks seem to be breeding like rabbits. The globe faces more than 10,000 malicious files and 100,000 malicious websites on a daily basis. Phishing attacks account for over 80% of the reported security incidents. As of January 2021, Google has registered more than 2 million phishing sites.  Since the pandemic outbreak, remote workers are also the target of alarming cyberattacks. Hence, people are aware of each and every cyberattack that is encountered all around the world due to their effortless access to the internet.

Predictive Modeling in Data Science

Predictive modeling in data science is used to answer the question "What is going to happen in the future, based on known past behaviors?" Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer "yes or no" types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. Forecast Model: One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

Which Model Is Right For You?

To find out which predictive model is best for your analysis, you need to do your homework.