Significance of Data Analytics in Cyber Security

The massive proliferation of data over the years has provided unprecedented opportunities to business organizations. However, these interconnected system of IoT devices and the availability of Big Data on multiple storage repositories have also increased the possibility of Cyber attacks and threats. It takes a single vulnerability for the cybercriminal to break into the system and cause massive damage to the business enterprise concerned. With the spiralling complexity of Cyberattacks, it has become imperative to go beyond the conventional preventive approach towards the PDR (Prevent – Detect – Respond) strategy. This is where Cyber Security Analytics come in. While the presence of data can be an invitation for a Cyber attack; combining Data Analytics and Cyber Security can also help in acquiring insights which can predict, identify and abate Cyber-attacks. In this blog, we will try to answer the question of what is Cyber Security Analytics, consider the different aspects of Data Analytics in Cyber Security, as well as look at the utility of Predictive Analytics in Cyber Security.

Cyber Security Analytics, a combination of Cyber Security and Data Analytics

Cyber Security Analytics is an advanced approach to Cyber Security that seeks to make use of the processes of data gathering, aggregation, attribution, and analysis; for extracting valuable insights. These insights in turn help in performing crucial security functions which help in detecting and mitigating cyber attacks and threats. Big Data Cyber Security Analytics solutions help in collating data from multiple sources like business applications, virus scanners, operating systems event logs, behavior data, and so on. Organizations try to aggregate this data into a single data set which can be used by security experts for the application of appropriate searches and algorithms (data analytics) for the prior detection of cyber attack indicators. Thus, Data Analytics in Cyber Security helps security analytics experts to identify potential threats at an early stage and consequently provide them with the chance to abate them before they manage to creep into the network infrastructure and result in data loss or breach or data leakage. Machine Learning Technology forms an important part of Cyber Security Analytics. It helps in providing for real-time threat and data analytics.

Cyber Security and Data Analytics: A Strong Union

Cyber Security Analytics, Security Analytics or Big Data Cyber Security Analytics are different names for the single phenomenon of Cyber Analytics which is the offspring of the union between Cyber Security and Data Analytics. Data Analytics deals with the management of data through its collection and storage as well as, with the techniques, processes and tools, which help in analyzing it. The main purpose is to extract meaningful insights which could help in undertaking business decisions, making predictions as well as improving its efficiency. Cyber Security deals with providing protection and security to servers, computers, mobiles, programs, systems and networks; with the objective of defending them against malicious digital attacks. Flowing from the above definition; Cyber Security Analytics can be understood as utilizing the power of Data Analytics for accomplishing a Cyber Security objective. When you use Data Analytics with the aim of comprehending data in a way which can diagnose vulnerabilities, describe risks, make predictions for possible malicious attacks and suggest protective measures; you are indulging in Big Data Cyber Security Analytics. There is a continuous exponential increase in the amount of data. Thus, there is an urgent need to comb and explore this data for the detection of possible threats. However, this is not possible without a robust Big Data Analytics framework which is made to fit within the Cyber Security system. Thus, Cyber Security Analytics help to forge a synergy between Cyber Security and Data Analytics in a way which could provide for a tough wall of safety.

Need for Cyber Security Analytics

In this section, we will look at some of the factors which have propelled the emergence of Big Data Cyber Security Analytics. 

·         According to BARC and KuppingerCole, “Big Data and Information Security” study, “only 6% of companies say they aren’t exposed to any cyber security risks”, and, “62% say digital security threats have increased in the past 12 months”

·         Cyber Security Analytics provides for the structuring of data in a way that it helps in a unified view of security breaches and threats which in turn allows for better planning and rapid resolution

·         Unprecedented advancement in the frequency and sophistication of Cyber-attacks has been one the major propellers towards a Data Analytics Cyber Security mechanism. While it takes a single vulnerability for an attacker to bring down the system; the threat itself can go undetected for unusually long periods of time. A strategic policy of Cyber Security and Data Analytics taken together help in maintaining track of threat patterns and thereby issue alerts on the event of any anomaly

Cyber Security Analytics: Use Cases

Cyber Security and Data Analytics combined have significant applications in the pursuit of maintaining a robust Cyber Security system. In this section, we will look at some of the use cases of Big Data Cyber Security Analytics. 

·         Scrutinizing Network Traffic: Data Analytics in Cyber Security help you to acquire a glimpse of your network traffic and thus provide you with the ability to identify any kind of network anomaly. Simultaneously, you can also use cloud security tools in order to perceive threats in the cloud environment. 

·         Detection of Insider Threats: Data breach or leakage can also be an intentional act of malicious insiders who possess access to sensitive data in the form of network credentials. Data Analytics Cyber Security tools can be utilized for sensing insider threats through keeping tabs on such activities as abnormal email usage, unsanctioned database requests, unusual login times and so on. 

·         Unwarranted Data Access: Unapproved data exfiltration can happen as a result of data theft or data loss. Cyber Security Analytics solutions can help in obstructing unlicensed channels of communication and prevent individuals from giving in their credentials to barred sites. 

·         Observe User Behavior in order to Perceive Threats: Data Analytics Cyber Security makes use of user and entity behavior analytics (UEBA) in order to develop algorithms which can help in discerning patterns of mischievous activity in user behavior.