Big data analytics in cybersecurity
When we talk about big data analytics in cyber security, it reflects the capacity of collecting enormous amounts of digital information. It works by extracting, visualizing, and analyzing futuristic insights so that disastrous cyber threats and attacks can be predicted well in advance.
A stronger and robust cyber defense posture enables organizations to get a more firm idea of all the activities and actions that can potentially lead to cyber-attacks.
As the world's information is getting doubled every year, big data analytics is bringing more and more data into play. Connected devices and the Internet of Things have made available an unprecedentedly large surface that always remains under the threat of attack by cyber attackers.
Whenever a new technology or system develops, so do new threats and vulnerabilities along with it. Security has undoubtedly become a moving target as unprecedented amounts of data are generated in various forms at unpredictable speeds.
According to Ernst & Young - “Global companies are in a hurry to adopt new technologies and media, leaving threats to security as an afterthought”
In such a scenario as described above, fast and accurate detection of vulnerabilities and cyber threats is needed in this era of the internet of things. Big data analytics tools are accompanied by a multitude of advantages that help in making cyber threat analysis systems more robust and infallible.
Big data gathered from networks, computers, sensors, and cloud systems, enables system admins and analysts to get to know the details of vulnerabilities and cyber threats accurately. They can then plan a better framework for developing security solutions to cope with the threats. For any cyber security solution to be effective, big data analytics is gradually becoming a prerequisite.
As explained by the research paper, the major reason behind this development is the fact that large volumes of valuable data are generated in various forms at surprisingly high speeds.
In order to put a limit on the vulnerability of the systems and boost their resilience, big data analytics is bound to act as the key. Big data analytics tools have been widely used for the past few years.
New approaches in the field of cybersecurity or cyber defense are being developed keeping numerous aspects in consideration. The aspects that need to be kept in mind include:
unified data representation,
zero-day attack detection,
data sharing across threat detection systems,
sampling and dimensionality reduction,
resource-constrained data processing, and
time-series analysis for anomaly detection.
How big data analytics is making cyber security robust?
Business leaders are prioritizing fast and accurate detection of modern cyber security threats, leading to the exponential rise of big data analytics in cyber security networks.
Due to the incorporation of big data analytics in cyber defense, enormously large volumes of data can be easily processed within no time. This, in turn, makes possible the quick discovery of vulnerabilities and anomalies so that overall resilience can be boosted to a significant extent.
Elements that big data analytics offers such as statistics, machine learning algorithms, predictive models, and computing systems help to raise the standard of business intelligence and cyber security networks.
Cyber security analysts and defense engineers can figure out statistical models or artificial intelligence-based algorithms based on the analysis of historical data isolated from a large variety of sources.
Creating a baseline for normal activity enables experts to quickly detect vulnerabilities. Therefore, it can be said that harnessing the power of big data analytics in cyber security networks is making cyber analysts able to predict cyber attacks.
Combining technologies and new-age solutions like artificial learning, data mining, machine learning, natural language processing, and statistics is enabling big data analysts to detect variations from the norm to predict imminent attacks.
Big data analytics provides automated monitoring and threat detection systems that facilitate real-time detection and continuous monitoring of the environment.
As many organizations bear witness to cyber security challenges emerging from insider threats and negligence, it becomes pertinent to assign credentials to authorized users.
Automated monitoring resolves this concern, by collecting user behavior data and analyzing it. In case any unusual or potentially harmful action is detected, automated monitoring generates an alert well within time.
Latest path-breaking innovations like Security Information and Event Management (SIEM) systems have come all the way from Intrusion Detection System (IDS).
In fact, SIEM is evolving still with the unprecedented power of Machine Learning, enabling cyber security analysts and cyber defense engineers to better handle diverse, unstructured large data sets and to reduce time to consolidation, correlation, and insights.
All businesses small or large alike can opt for big data analytics solutions for strengthening their cyber security.
Combining Machine Learning and AI, businesses are empowered by real-time detection, automated monitoring, and data intelligence for boosting resiliency has enabled businesses of all sizes to replace traditional security tools with big data analytics in cyber defense looking forward to overcoming cyber security challenges.
(Related reading: AI in cybersecurity)
Applications of Big Data Analytics in Cyber Security
Statistical techniques and AI help in detecting potential frauds
Big data analytics plays a tremendous role in fraud detection.
Some techniques for statistical techniques in data analysis include pre-integrated techniques for error detection, validation, rectification, and filling the gaps in the form of missing or wrong data.
Algorithms for quick and effortless detection of anomalies
Previous models, firewalls, and threat detection solutions were basically outdated in their approach towards intrusion response, proving inadequate in defending against modern cybersecurity threats.
Now, after the incorporation of big data analytics in cyber security networks cyber defense engineers and data scientists are empowered to match algorithms for detecting anomalies in the behavior of transactions and users.
Big data analytics not only strengthens cyber security networks through the above method but also aids in eliminating false alarms, estimating potential risks, and predicting futuristic insights.
Big Data Analytics boosts Security Intelligence
Conventionally, the process of cyber defense management has been knowledge-intensive as well as time-consuming.
Thanks to the rapidly expanding depth of big data analytics, the time taken to correlate data for forensics purposes and generate actionable security actions has been drastically reduced.
Calculation of Statistical Parameters
A multitude of statistical parameters including averages, quintiles, performance metrics, and probability distributions can be calculated in no time using big data analytics.
Clustering and classification has become easier
Patterns and associations among large groups of data can be easily clustered and classified by cyber security professionals and data scientists by utilizing big data analytics. (source)
While reaching the end of the blog, it can be successfully stated that big data analytics has revolutionized the way cyber security analysts used to approach vulnerabilities and anomalies.
It has overwhelmingly positive effects in leveling the challenges that were faced by cyber security professionals and data scientists. They are now empowered to quickly identify anomalies by visualizing and analyzing tremendous amounts of data.
Once they are able to access these enormously large datasets, it becomes a cakewalk for data scientists to decide the framework for feature engineering.
Big data analytics combined with the power of Machine Learning and data mining has not only addressed the severe challenges faced earlier by data scientists and cyber defense engineers but has also enabled them to find futuristic insights and harness the massive parallel computing of big data for strengthening cyber security.