Why Network Security needs to have Big Data analytics?
Network security protects the integrity and usability of any enterprise’s vast network and data connection. In a broader sense, it consists of certain practices and policies that monitor and prevent data misuse, unauthorized access, and modification of the existing data in any computer network. Network security is concerned with shielding computer networks from cyber attacks.
What is Big Data analytics?
Big data analytics is the strategy of examining a huge amount of data to identify correlations, hidden patterns, and other useful insights. With modern technology, it has become extremely convenient to analyze tons of unnecessary information to extract data.
Big data has existed for a very long time. Even before the term ‘big data analytics’ was coined, people were still using basic analytics practices to uncover future trends and useful insights. Well, times have changed and basic analytics has transformed into big data analytics. Generally, big data analytics is used by organizations to browse through and identify the latest opportunities. This, in turn, leads to efficient operations, smart business moves, and higher profits.
What is the connection between Big Data analytics and Network Security?
Big data analytics is concerned with extracting useful data from a bulk of available data and network security is about creating strategies that could protect an enterprise’s computer networks. The era of connected devices has created a larger ground for cybercriminals to carry out exploitive measures. This has created a requirement to have a system in place that could track or detect any attack before it has even taken place. The evolution of enormous data storage facilities has introduced big data which goes through unprecedented amounts of useful data at a high speed.
Analytics has always been a crucial factor in cyber resilience. The cyberattacks are becoming more highly developed and advanced and the attackers only require to make one successful attempt to get inside an enterprise’s networks. This forces the organizations to rethink their network security concepts. To protect one’s assets, one must move beyond prevention towards the ‘Prevent, Detect, and Respond’ paradigm.
At the center of this problem is the requirement for enhanced detection. This is where big data analytics meets network security. With the assistance of improved detection capabilities, the networks are able to pinpoint changes in the use pattern.
In response to any detected changes, the networks can perform quick complex analysis and execute complex correlations through various data sources that range from application logs to network events, servers, and users’ activities.
This approach needs advanced analytics and the ability to perform real-time analysis on tons of present data. Combining analytics present state with network security provides companies to enhance their cyber strength.
By utilizing big data gathered from computers, networks, cloud systems, and sensors, cybersecurity analysts along with intrusion detection and prevention areas can uncover important data quickly. Discovering such data will enable enterprises to identify vulnerabilities, predict cyberattacks, and enhance calculated network security solutions in response.
Combating Network’s Cyber Threats through Big Data analytics:
Usually, the network security departments relied on two basic analytic techniques to identify any security anomaly; correlation rules and network vulnerabilities. With the first technique, the enterprises defined different rules that specified a chain of events which could detect any anomaly. Such anomalies that presented vulnerability, security threat, or any security incident were dealt with immediately.
The second strategy involved risk assessment and network vulnerabilities. This strategy worked by scanning the networks to identify or pick up attack patterns or security loopholes such as insecure protocol and/or open ports.
Although both these methods were good at detecting security anomalies, they suffered from two major drawbacks, i.e. false positives and unexpected events. Since these networks had predefined rules, the chances of false positives were always on the horizon. Further, these predefined rules were not made to deal with any new type of threats. This left the existing network security incompetent in terms of dealing with new attacks.
Big data analytics assists companies by identifying hidden risks and insider threats; the latter of which is difficult to detect since the users have access to all corporate systems. With the support of big data analytics, enterprises can:
•Detect and deal with anomalies in device or personnel behaviors. This can be achieved by producing a model persona of users, devices or a group of networking devices. This will enable enterprises to detect and deal with such behavior that is not according to their predefined set of rules.
•Identify inconsistencies in networks. By creating a model of how network traffic should behave under normal circumstances, the security systems will be able to pinpoint unusual behavior during any time.
•With the support of machine learning, big data analysis can learn from previous intrusions to hinder any such attacks from taking place in the future.
•Big data analytics combined with machine learning algorithms can perform deep learning to pick out threats of any malware attack. It intelligently analyzes the binaries transferred from the download or emails and tries to figure out the nature of the binaries even if they were not flagged as a potential threat. This is done in a bid to understand if it is a malicious program or a benign one.
Conclusion:
Big data analytics offer companies the hope that their processes and enterprises can be secured in the event of any breach. By incorporating big data analytics, companies can enhance their management techniques and work on their threat-detection mechanisms to secure their basis.
As much as big data analytics is useful, it can easily become ineffective if it is poorly utilized. Big data analytics security solutions, backed with machine learning and artificial intelligence, can give hope to organizations that their networks can be kept safe in the event of unauthorized access.
Overall, Big data analytics online training represents huge opportunities for organizations that go beyond basic business intelligence. It provides a chance to fortify the network’s defenses. However, solely relying on big data analytics will not produce positive results. Instead, the enterprises must learn to share the risks and responsibilities associated with the protection of data.
