Apart from artificial intelligence, cybersecurity now also uses big data and machine learning in detecting threats.
Big Data and Machine Learning
The use of big data and machine learning (ML) can be useful to detect insider threats by analyzing the patterns and behavior of employees.
For example, if a network employee uses his or her work computer for personal work or activities, then the system could flag this activity as suspicious. A similar approach, moreover, could be used to detect unusual Internet browsing patterns.
Big data analysis could also be used to find anomalous behavior such as viewing pornography while at work. The use of machine learning can uncover patterns in these behaviors and generate alerts for security personnel to investigate further.
Cybersecurity Use of Machine Learning and Data Analytics
Machine learning and data analytics can be used in many ways to improve cybersecurity. These include, for instance:
1. Data analytics can be useful to aggregate and analyze network traffic, user activities, and assets.
2. Data analytics can be useful to detect anomalous behavior and data patterns that indicate a security risk.
3. Data analytics and ML can also be useful for threat hunting and threat remediation efforts.
3. Data analytics and ML can be useful to analyze malware binaries to determine the nature of the threat and the potential impact of the threat on the organization.
4. ML can be useful to detect zero-day attacks by analyzing patterns in data such as network traffic, host activities, and asset information.
5. Data analytics and ML can be useful to improve cybersecurity by improving user awareness of risks, threats, and indicators.
Benefits of Big Data and Machine Learning
Big data and ML applications in cybersecurity bring more advantages than conventional methods. Here are some of them, for instance:
1. Greater volumes of data
Big data enables the processing of big amounts of data. Thus, making cybersecurity monitoring and detection faster and efficient.
2. Faster detection
ML and big data can process and analyze the data faster than conventional methods.
3. Greater accuracy
ML and big data can produce more accurate results than conventional methods because the technology can learn from previous events to detect new threats.
4. Greater convenience
Because of the automation provided by big data and ML, security personnel has less work to do.
5. Low cost of implementation
Because of advances in artificial intelligence, ML and big data are relatively easier to implement than conventional methods.
6. Better compliance with regulations
Big data can help organizations meet regulatory requirements, including GDPR requirements for obtaining consent for processing personal data. For example, big data can help identify users that consented to the processing of their data.
Big Data and Machine Learning Challenges
Despite the numerous advantages of big data and ML, there are some challenges. Here are some of them:
1. Data overload
Because of the vast amounts of data involved in big data and machine learning, security personnel may experience data overload. To overcome this challenge, security personnel need to develop efficient ways to access, process, and analyze the data.
2. Security skills gap
The security skills gap is one of the biggest challenges for big data and machine learning applications in cybersecurity. The gap can be addressed by training more people in cybersecurity skills.
