22 July, 2021

Types of Security AnalyticsThe COVID-19 pandemic forced organizations to adapt business models for the new standard of working from home, and with that, it also created a new set of opportunities for cyber criminals. Hackers quickly seized the opportunity to take advantage of increased security vulnerabilities due to the rapid shift to more cloud servers and network-connected devices; and as a result, cyber attacks jumped 238% globally between February and April 2020. Cyber attacks have also reached an unprecedented level of sophistication, with the potential to operate large scale, multi-vector data breaches that can infect multiple components of a security infrastructure.

With the surge of cyber exploits, both small and large, it’s increasingly important for cybersecurity professionals to develop an understanding of how to analyze data to prevent cyber attacks before they occur. Traditional security information and event management systems (SIEMs) are not equipped to keep up with dynamically changing networks and can miss significant security threats when they stand alone. To bolster cybersecurity infrastructures against complex cyber threats, professionals must learn how to leverage different types of security analytics, including cybersecurity analytics, information security analytics, big data analytics and predictive analytics. Filling various gaps that exist in these forms of data analytics is critical to improving current cybersecurity practices so that organizations can expand their capabilities of detecting existing threats to also predict and prevent attacks before they occur.

Take a quick glimpse at the differences between these types of security analytics and continue reading for more detail.

Cybersecurity analytics
  • Analysis of historical and real-time data to foster a proactive approach to security
Information security analytics
  • Protection of the confidentiality, integrity and availability of data
  • Includes data outside of cyberspace
Big data analytics
  • Examination of data sets that are too large for traditional security systems to process
Predictive analytics
  • Consolidation of historical data and statistical algorithms to gauge future outcomes

Cybersecurity Analytics

Cybersecurity analytics is the practice of applying data analytics to defend digital assets against malicious attacks. Traditional security analytics tools can detect cyber threats at a single moment during an assessment, but they often fail to prevent risks in between assessments. Cybersecurity analytics enhance traditional security analytics by consolidating current and historical data to detect cyber threats in real-time. This allows for a unified view of cybersecurity vulnerabilities so that on top of detecting existing security breaches, IT teams can predict future malicious activity and establish protective countermeasures.


Information Security Analytics

Information security analytics can be defined as a set of practices designed to keep data secure from unauthorized access, distribution, alteration or deletion. Information security has a broader scope than cybersecurity, as it applies to all information systems, including those outside cyberspace. The primary focus of information security programs is to ensure the confidentiality, integrity and availability of data, but these interests often collide. Thus, another important aspect of information security programs is risk management and mitigation based on competing security interests.


Big Data Analytics

Big data analytics is a specialization within the larger field of cybersecurity analytics, primarily distinguished by its scalability. Big data security analytics solutions are able to collect, integrate and store numerous, diverse data sources in real-time to detect potential cyber threats more quickly and effectively. Additionally, big data solutions offer users the ability to interact with and visualize data in a variety of ways.


Predictive Analytics

Predictive analytics apply statistical algorithms to historical data so that companies can predict future cyber attacks in real-time. When coupled with machine learning, predictive analytics provide organizations with the tools to protect their security infrastructure from potential threats before they occur.


Upskilling in the Cybersecurity Field

The field of cybersecurity is constantly evolving as hackers find new ways to extract and exploit sensitive data. A surefire way to stay current with the latest trends in cybersecurity is to obtain a master’s degree. Aside from covering traditional and contemporary methods for intrusion detection and assessing security risks, master’s programs also offer the opportunity to learn from industry experts who have experience working in a variety of cybersecurity functions themselves.


About the Master of Engineering in Cybersecurity Analytics at GW

The George Washington University has developed an online Master of Engineering in Cybersecurity Analytics program to prepare graduate students for addressing complex cybersecurity threats. The curriculum covers many concepts in cybersecurity, ranging from the foundational concepts of information security to cutting-edge technologies, such as predictive analytics.

In addition to the comprehensive scope of the curriculum, the cybersecurity analytics master’s program offers students the opportunity to network with GW’s experienced faculty, share their knowledge with one another and further refine their expertise through practical exercises.