Cyber attacks are a significant problem for businesses and in an age where “data is the new oil”, companies have more to lose from a cyber attack than before. For example, there were more than a billion malware attacks from June to November 2016, costing firms millions of dollars. Furthermore, most technologies are proving inadequate against malware attacks. Therefore cybersecurity is at the forefront of many organisations’ priorities.
One reason is the sheer volume of data being made available, given the hundreds of thousands of files reported and shared on a daily basis for examination, creating a massive backlog. The backlog is compounded by the fact that infrastructure cannot cope with the volume of data because it cannot be scaled to match that volume. Therefore, companies need a new solution to solve this problem: Data analytics.
How can data analytics improve cybersecurity?
Data analytics improves cybersecurity because of its ability to collect, clean and analyse large volumes of data. Companies need an infrastructure capable of handling massive data volumes because of the sheer amount collected daily. For example, healthcare companies collect petabytes of structured and unstructured data, daily. Therefore, data analytics is better equipped to combat cybersecurity than current infrastructure.
With data analytics, companies can embrace a completely new paradigm in cybersecurity: PDR (Prevent, Detect, Respond), a more suitable defence paradigm. Companies cannot block all malware attacks because cyber attacks are constantly evolving and a hacker only needs to succeed once to irreparably damaging the company’s reputation.
To counter cyber attacks, companies need to a system that allows them to detect threats in real-time and respond immediately. An immediate response is not possible with current infrastructure, however, with data analytics, companies can develop the response systems they need to detect and block threats.
How will data analytics work?
Data analytics works by detecting anomalies in the data. Companies can establish anomalies by integrating information about past transactions to the analytics models. Data analytics models will analyse this data to ‘learn’ what is normal and what is an anomaly. Therefore, when data passes through the analytics model, it will be assessed, then tagged as normal or an anomaly, based on past data. If the data is an anomaly, it will be flagged as a potential cyber attack.
As an example, a health insurance company can detect fraudulent claims through data analytics. The analytics model will analyse each claim and compare it against past claims that were fraud attacks. If the claim is very similar to the fraud claim, then it will be flagged.
Monitor employee activity
Sometimes, cyber attacks occur with help from the inside. Employees acting with a party or on their own have effective means to attack the system. However, data analytics models can scan employee activity to make sure they are not trying to get unauthorised access to sensitive information.
Data analytics allows systems administrators and HR representatives to study employee activity and determine if they are suspicious or not. Employees with access to sensitive information should get unique logins to distinguish them from would-be fraudsters. Data analytics can survey employee activity in real time, therefore, and, if there is any suspicious activity, system administrators will know about it immediately.
Developing an IDS
Data analytics lays the groundwork for an Intrusion Detection System (IDS). An IDS monitors traffic on all business systems, especially on segments where malicious traffic might pass through. If the system detects any harmful traffic, it will send out notifications to system administrators. Thus, administrators will be warned and react to a cyber attack.
IDS is seen as a progressive measurement and should be combined with firewalls, data encryption, and multi-factor authentication for a secure network. IDS must be used on company systems containing vital business information, or systems connecting online. Companies who use IDS can monitor traffic coming through the business network. Therefore, if businesses want to keep their data safe, it is important to create an IDS to improve cybersecurity.
Malware attacks are constantly evolving and a cyber attack can cost companies millions of dollars. Fortunately, with data analytics, its possible to develop a comprehensive security system. For example, as discussed, companies can use data analytics to adopt a PDR paradigm to improve security. A PDR paradigm (Prevention, Detection, Respond) not only blocks threats but detects a threat in real-time, giving administrators enough time to respond. Thus, companies are better boost data security with data analytics.
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