The shift of businesses to big data and the interconnectedness of devices via the Internet of Things (IoT) has provided hackers with a more extensive range of devices to attack. This has resulted in companies stepping up cybersecurity to secure broader parts of their network.

Nevertheless, all it'd take for a hacker to facilitate a security breach is to discover a new malware signature undetected by firewalls and antiviruses. Although the association of big data and IoT places businesses at an even increased risk of getting their data stolen, it can also be used to boost cybersecurity.

Interestingly, firms are starting to acknowledge cybersecurity's importance. They are now implementing various techniques. There have been increased Google search queries on cybersecurity like 'how to get a Turkish IP address' and 'best antiviruses for PCs.' The demand for cybersecurity tools like VPNs and malware protection software has increased because of the high levels of cybercrime.

This article will reveal how big data helps the cybersecurity field become better.

How Big Data Fosters the Cybersecurity Sector

When tech-savvy individuals work with big data, the first thing that comes to mind is how to protect this mass of data. Hence, the idea that big data could be leveraged to improve cybersecurity doesn't occur instantly. However, the concept of using big data to better cybersecurity is not just sensible but efficient.

Research conducted by Bowie University has revealed that around 9 in 10 businesses that utilize big data fend off internet threats better. The key to this improved performance is big data analytics.

Big data analytics involves analyzing massive volumes of data using specialized tools like Excel and Python to gain business insights. This data could be relational in the form of tables or non-relational in the form of plain text with keys and values. Data could also be unstructured in the case of images, videos, and audio.

Big data analytics aims to help businesses make intelligent decisions, understand market trends better, and get a feel of what customers prefer to enhance efficiency and customer satisfaction.

However, the field has morphed into one that facilitates the retrieval of crucial information from large amounts of data which helps in various sectors, including cybersecurity. Big data analytics for cybersecurity works when past data is analyzed to predict future trends.

When machine learning is merged with big data analytics, firms can better analyze historical data to determine what constitutes normal behavior. The outliers are considered abnormal behavior and an alert for a potential cyber-attack pop-up.

For instance, if the usual work period for an organization is 9 to 5, machine learning will recognize that pattern to establish average behavior. If there's a login made to the business network at an unusual time, the system would be alerted to prevent that potential digital security attack. Nevertheless, the applications of machines and deep learning go beyond this example.

Historical Data Analysis for Cyber-Attack Visualizations

By leveraging big data, firms can forecast cyberattacks and develop competent measures to avert them. For instance, an organization that has previously fallen victim to an internet attack can perform an analysis of the hacker's activity. By leveraging machine learning, the patterns can be formed from the hacker's actions to model a possible security breach attempt.

Nevertheless, analyzing historical data to predict cyber-attacks isn't restricted to companies that have experienced them. A firm without any prior digital security breaches can explore the vast amounts of industry data to spot the patterns made by hackers.

The penultimate result would be a visualization of the events leading up to the attack. Subsequently, machine learning engineers can implement preventive measures to stop possible online attacks.

This way, even though hackers are targeting companies that collect large amounts of data for attacks, these firms can, in turn, use the weapon provided by big data to frustrate cybercriminals.

Conclusion

Hackers have always been attracted to large amounts of data. That's why IoT and big data have increased the risk of experiencing a cyber-attack. Nevertheless, even though firms are looking to protect the sensitive data they hold, it could be used as a weapon against cyber criminals.

Analyzing historical data and leveraging machine learning techniques can draw patterns from typical hacking behavior. Machine learning engineers can then create models to predict and prevent a future attack.