Big Data for Cybersecurity

What is big data ?

Simply put, big data can be defined as a large amount of systematic and informal information that because of its large size, cannot be processed using a standard database and software strategies. It is now widely used by companies as a way of finding patterns and behaviors, while also allowing for the detection of advanced threats and machine learning.

According to Forbes, the largest market for data statistics was estimated at $ 203 billion back in 2017. As companies look to adequately protect themselves from the growing threat of cybercrime and face ever-increasing data, the market value will undoubtedly increase significantly. as the years go by.

However, the increase in big data usage has not been seen by cybercriminals, as cybercriminals now make big data companies more targeted. The growing number of data breaches that will occur in recent years is a clear indication of the vulnerability of big data. But, what exactly are the big data challenges in cybersecurity? In the next section, we will look deeper into that.

What challenges do cybersecurity experts face?

Keeping the growth and functioning of the business at the same time protecting sensitive information is becoming increasingly difficult due to the ever-increasing number of cybersecurity threats. Therefore, it is clear that preventing data breaches is one of the biggest data challenges in internet security.

On a daily basis, many sensitive records are processed by businesses around the world. If this information were to end up in the wrong hands, the results could be disastrous, as has been shown in previous data breaches, including the aforementioned.

Traditional defense tools and technologies used for data mining purposes and to prevent cyber attacks are not enough for most businesses — especially those that handle such large data. That is why big data data is now so widely used by cyber security professionals.

However, another challenge for businesses is that data analysts often do not have the information they need to respond effectively to any threats that may arise. But, as artificial intelligence (AI) and machine learning continue to use information and data with big data, hopefully this will be a small problem for businesses in the years to come.

PDR (Prevent — Detect — Respond)is good but complex security challenges require more details about what is happening within the system. This information can help security professionals avoid potential attacks before a cybercriminal has a chance to exploit them.

So, what is the solution and how can we use logical reasoning to avoid attacks? To reach that level, we need to integrate two major areas — Big Data Analytics and Cybersecurity. Once we combine both domains, we can achieve Security Statistics closer to real-time.

Big Data Analytics And Cyber Security — Synergy

According to a Verizon report, 62% of attacks are related to robberies, 51% are malware attacks and 43% use social media to find ways to obtain data or access.

Cyber ​​attackers are also evolving day by day and have begun to use powerful AI systems to discard criminal emails to steal sensitive information for targeted victims. They can also use malware and install ransomware in real time.

It is not easy to go through the methods and techniques of attack, especially in those areas where large amounts of data are involved. In addition, the threatening patterns are also altered to make the attack more subtle. In this case, the only option is to use a data-driven method to stay one step ahead of all online threats in any organization.

There are a variety of data sources including server logs, application logs and entry-level acquisition programs that capture the largest amounts of data. Groups responding to threats often ask questions to detect threats in the system but it becomes difficult to detect them as the amount of data continues to grow very rapidly.

Big Data in Cyber Security

The main purpose of capturing Big Data in Cybersecurity is to improve the detection of cyber threats that can occur in a complex way. Detection of any system must be quick to detect major and minor changes in the system.

Complex analysis needs to be done quickly in real time. Advanced analytical methods are needed to analyze current and historical data from multiple data sources that is only possible with a large data-based solution framework.

Uses of Big Data Analytics in preventing Security Incidents

1:- Predictions using Machine Learning

Machine learning algorithms combined with data from defense systems can help analyze historical and current data for studying and predicting threatening patterns. This method can help to locate the contact areas of the attackers before any attacks are made.

In addition, it can also help with real-time data breaches. These algorithms can automatically link information to determine the risk pattern. For example, confusing discovery, malware detection and similar-looking predictions are popular usage situations.

2:- Automation and Monitoring at Scale

A large percentage of cyberattacks are caused by the ignorance of employees in any organization. Employees are unaware of online threats in most cases and do not know what to do in different situations, so it is easy to be targeted by attackers.

Big data analysis can help monitor a large set of system / user activities to prevent threats. This method can prevent many data breaches. Security experts can also perform these procedures automatically to minimize data breaches and speed up the recovery process in the event of an attack. Businesses can use data from a range of monitoring tools such as Nagios, Splunk, OSSEC etc.

3:- Intrusion Detection in real time

It is difficult to monitor and track the risk in real time but big data analysis can solve this problem by performing this process automatically on the scale. Intrusion Detection Systems (IDS) can be continuously upgraded with real-time statistics so that there is a comprehensive way to detect any malicious activity that is taking place in the system. These systems prevent threats before any attacker can gain unauthorized access to the system. For example, we can integrate other data sets from proxy logs, fine / secure domains and track the life of systems.

4:- Risk Management Reporting

Security information is important to keep your online security strong and that is exactly what analysis and reporting can help us with. Big data collections collect data from a variety of data sources and systems to benefit from root analysis. Some reporting metrics may be different about verification, user management, events, activities during non-business hours etc.

Conclusion

Cyber ​​Security Data Analytics can do wonders and can really strengthen organizations’ ability to deal with data breaches and breaches. Big Data Analytics can help better detection, risk management, monitoring, and automation. Machine learning algorithms can help with the analysis of threatening and guessing patterns.Overall, it would be good to suggest that big data analysis can be of great benefit to companies by gaining growth and performance information that can help further business. Big data and is undoubtedly the best way forward when it comes to cyber security, as detecting threats as quickly as possible is now easier than ever.