The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018. It is expected to grow to 274 billion U.S. dollars in 2022.
Big data refers to data sets that are too large or too complex for traditional data processing applications. The term often refers to the use of predictive analytics and other methods of extracting value from data. Businesses harness big data through raw storage and processing power, along with strong analytics capabilities and skills.
Businesses are increasingly relying on big data to identify popular cyber-attacks such as phishing, ransomware, and malware, and to build patches that prevent these attacks.
How Big Data Can Help Businesses Combat Cyber Threats?
To make the most of big data, businesses need to know how to analyze it correctly and use it for mitigating cyber threats. This is where big data analytics comes in handy.
Big data analytics analyze or assess large, varied volumes of data. It is used to retrieve critical information for strengthening cybersecurity.
Businesses combine big data analytics and machine learning to perform a thorough analysis of past and existing data. It helps them identify certain risk patterns in their network. Based on the results, businesses further use machine learning to strengthen their cybersecurity parameters. They also receive alerts whenever there is a deviation in the normal sequence of things and consequently, counter cybersecurity threats.
Organizations can also hire, who leverage big data and improve their security. Here’s how IT support providers make use of big data to counter cyber threats.
1. Intrusion Detection System
Managed IT service providers make use of the Intrusion Detection System (IDS) to analyze the traffic that passes through a business’s network. IDS makes use of this analysis to detect and identify malicious traffic and cyber-attacks.
IT support providers leverage big data to extract the information required to monitor company networks. Big data uses several analytics methods to analyze and store data in the intrusion detection system. Let us look at these methods.
- Signature-Based Detection Method: Signature-based IDS detects known attacks by looking for specific data patterns known as signatures. They include byte sequences in network traffic and known malicious instruction sequences used by malware.
- Anomaly-Based Detection Method: Anomaly-based IDS is used to detect unknown attacks. It makes use of machine learning to develop a model of trustworthy activities. It further compares new behavior against this model and enables the detection of previously unknown attacks.
2. User Entity and Behavior Analytics (UEBA)
UEBA makes use of behavioral analytics, algorithms, and big data to track user behavior. It analyzes big data from apps opened, files accessed, emails sent/read, VoIP telephony usage, and network activity.
Using UEBA, IT support providers collect huge amounts of data on how users behave when on a business network or otherwise accessing business data.
Managed service providers further analyze big data and determine what behavior is normal and what is suspicious, and consistently monitor the latter as a potential security risk.
UEBA prevents damages caused by both outsider and insider threats. A few important use cases for UEBA include:
- Compromised User Accounts: IT support providers use UEBA to detect and investigate abnormal account activities to safeguard businesses from cyber threats. The abnormal activities include accessing critical information that a user does not normally try to access.
- Data Theft: UEBA is also used by IT support providers to prevent and minimize data theft. It helps IT support providers detect if a user is downloading information that they normally should not be allowed to. IT support providers can further restrict the account’s access and prevent data theft.
- Insider Threats: Employees have access to critical business information such as proprietary rights, credit and financial information, and compliance information. They can act as insider threats if they decide to go rogue. IT support providers can leverage UEBA to detect abnormal behaviors and limit employee access to crucial company data. For instance, if an employee is accessing the data longer than usual or at strange times of the day, UEBA can help IT support providers detect it and minimize the damage they can cause.
3. Predictive Models
IT support providers use big data analytics to create a predictive model that can issue an alert as soon as it sees an entry point for a cybersecurity attack. They make use of machine learning and artificial intelligence to develop such a mechanism.
Simply put, analytics-based solutions enable IT support providers to predict possible cybersecurity threats based on the alerts issued and gear up to counter them.
4. Penetration Testing
Penetration testing is a simulated malware attack against a business’s network to check for exploitable vulnerabilities. It gives IT support providers insight into a business’s database and safeguards it from hackers. Penetration testing involves the following five steps.
- Planning and reconnaissance
- Gaining access
- Maintaining access
- Analysis and web application firewall protection
Businesses can improve their cyberthreat-detection mechanisms by leveraging the power of big data through managed IT service providers. While an intrusion detection system can help a business identify and mitigate cyber threats, user entity and behavior analytics can monitor behavioral changes and analyze user-based threats. Also, periodic penetration tests can ensure that a business’s analytics program is working efficiently.