Predictive Analytics in Combating Cybercrime: Do You Really Need It? This Will Help You Decide!

Feb 10, 2022
4 min read

Today, cybercrime is a trending word across industries and has become a devastating threat. Cybercrime is on the rise mostly due to the uncountable and latest range of methods used for attacks. The rapid and dynamic development of methods to assault cybersecurity has made defending the network from this type of crime a harder task. Nevertheless, as an extract of the great saying "Darkness cannot drive out darkness, only light can".

Predictive analytics has proved to be a ray of hope against cybercrime. Do you think that a prior warning can help prevent these cyber-attacks? Fortunately, there is an advanced way of doing so. As a prelude, I would like to introduce you to the concept of predictive analytics.

What is Predictive Analytics?

Analyzing historical data and using analytical techniques, such as statistical modeling and machine learning, to predict future outcomes is called predictive analytics. Predictive analytics is termed as a solution because it is designed to handle growing volumes of data and simultaneously predict the possibility of an attack. With the aid of sophisticated predictive analytics tools and models, people can predict the future of their businesses. In addition, predictive analytics can be used to detect and cease various types of criminal behavior before they cause serious damage. By studying user behavior and actions, an organization can notice anything out of the ordinary, such as cybercrimes. As predicted outcomes have grown, security professionals have begun to leverage predictive analytics. Individuals run multiple models simultaneously and combine the outcomes to find the most likely result. The greatest preferences of predictive analytics are its capacity to distinguish inconsistencies.

How Predictive Analytics Work?

The most commonly used methods in predictive analytics include:

Identifying Loopholes in Network

Cybercrime is a threat to all industries with varying impacts. For example, the methods used for attacking healthcare industries will differ from the methods used in software or aerospace industries. These variations depend on the data available, and it is always an important step to implement ways of protecting data together with restricting cybercrime from taking place. Predictive analytics use tools which are based on identifying network vulnerabilities. These tools help identify and observe malicious activities on a network and use this analysis to anticipate possible threats. It is important that these systems are up to date and specifically capable of identifying sophisticated attacks buried under the heaps of data in order for them to be effective.

Handling Massive Data with Attack Graphs

Predictive analytics deals with a huge data collection which can be exceptionally difficult to cross through and then process, analyze for required insights. These data results as an overall collection from databases, programs, devices, and servers.

Predictive analytics uses the method of attack graphs to handle such a heavy task. All paths of entry and exit to a cybersecurity network are represented by a cyber-attack graph.  They delineate ways in which an attacker can misuse the vulnerabilities of a network.

Lists Preventions to Avoid Future Attacks

Predictive analytics focuses more on preventive future attacks rather than explaining the reason and areas of attack in the past. It provides a smart move for you to identify weak areas along with solutions to identify patterns and detect abnormalities that can be easily avoided with certain preventions to save your data. Predictive analytics combines with hacker bots to detect and monitor network activity. It can also detect immediate irregularities in the data flow.

Predictive Analytics Tools

Here are three of the most popular predictive analytics tools:

· SAP Predictive Analytics

· SAS

· Microsoft Azure Machine Learning

These tools use big data, statistical algorithms, and machine learning methods to predict the probability of real-time and future results based on past data. The main objective of predictive analytics tools can be summarized in four points:

· Detect cybercrime

· Optimize the operation of network stream

· Improve overall performance and protect data

· Reduce future risks

Industries Using Predictive Analytics To Prevent Cybercrime

Predictive analytics is widely used among all industries to improve business with merely a minimum or negligible percentage of existing risk. It is a highly valued technology in industries like Retail, Marketing, Healthcare and Banking. Among these, banking and financial industries use predictive analytics to avoid cybercrimes. There are huge amounts of data and money in the financial industries which face extreme risk on a daily basis. Apart from detecting cyberattacks and protecting data, the banking sector takes the help of predictive analytics for optimizing customer-based decisions, avoiding fraud, enhancing risk management, and providing better customer solutions.

What Does the Future of Predictive Analytics Hold?

In conjunction with being a commanding subject for study and research at this time, predictive analytics has already made a position in terms of security in each large and small company. Security being the most elevated need of each company, predictive analytics have combined with branches of artificial intelligence, modelling etc., and have come up as the foremost successful shield for cybersecurity. There's continuously less chance for such cyber crimes to end totally in near future but the possibility and accessibility of these security technologies has multiplied up. These innovations have a mind-blowing potential to form our virtual and computerized world free of all wrongdoings.

Conclusion

By involving predictive analytics as a potential part, an organization can recognize the wide range of cybercrime taking place every day. Predictive analytics apply statistical algorithms for organizations to predict future cyber-attacks in real-time. Organizations can take an active part in sorting these cybercrime assaults by involving a group of experts and analysts who can research how often it is possible for cybercrime to occur. Analysts can gather internet data measurements and apply predictive analytics techniques to identify any vulnerabilities in their cybersecurity that could be exploited by cybercriminals. By combining machine learning with predictive analytics, companies can move forward with the efficiency of cybersecurity and intelligently dodge all approaching threats. This implies they are able to form a total analytics and visualization arrangement which helps hold up all information and reports. For way better help in cybersecurity, companies can too accomplice with analytics firm to work and imply predictive analytics.