Role of big data in cyber security
With hackers using AGILE methodologies to develop Ransomware, enterprises have geared up for their defense through big data – that provides the most advanced form of cyber security till date. So let discuss the role of big data in cyber security.
As per Bowie State University, 84% of big data users have effectively blocked security threats in advance have cited that they were using big data analytics to deal with security concerns. This is the reason big data application development is one of the most sought after skills in the area of risk management and cloud security.
Big data not only deals with heavy risk security threats like malware, Ransomware, compromised devices, zero-day attacks, and ill-meaning insiders but also relatively low-risk breaches like denial of services, web-based attacks, and spear phishing.
Concerns about big data implementations in cyber security
However, there are many challenges that go into utilizing the full potential of big data. Due to its ever-growing inflow and unstructured format, data analytics tools have to be fast in their processing speed while separating the actual threat-related information amongst the noise of the social media chatter.
The most prevalent reasons for not being able to leverage big data to its optimum were cited in Cracking the Data Conundrum as:
- Data volume too high to process (49%)
- Lack of proper infrastructure to collect big data (33%)
- Data loses relevance till the time it reaches the cyber security processing algorithms (30%)
Big data requirements for accurate results
Artificial intelligence algorithms aided by machine learning an process big data, given the right infrastructure and tools. Three V’s are required in the big data sets to run them through these cyber security mechanisms:
- Volume
Big data is the base element for running cyber security threat detection algorithms even in a test environment. If the data set is smaller, there may be incidences of false positives.
- Variability
Generally, the second element in regular data analytics would be variety – meaning big data sets from as varied channels as possible. However, in addition to variety, variability is required. It is perhaps even more important than variety. Variability is the change in parameter values of the big data sets between their minimum and maximum values.
- Velocity
Considering the rate at which big data is flowing in, every 5 years, the big data volume increases by 10 times. If a data analyst decides to scan through this amount of data, the speed has to be competitive. This requires the cyber security algorithms to be highly automated.
Utilizing the above characteristics of, the big data consulting company team should also empower the cyber security tools to identify and respond to threats in real-time. Accuracy is of prime importance here.
The right methodologies to use on big data for effective cyber security
The same question you may also like to ask a security professional here: AskSecPro: Feature-Based Malware Detection and Get secure sitelock solutions.
It is essential to note that big data alone will not solve cyber security concerns, but big data analytics and aligned applications will. Also Artificial Intelligence(AI) is Transforming Cybersecurity in Current Times. Following are the expected approaches that these applications can take to allay the security threats:
- Predictive analysis
While analyzing big data for patterns, various social media trends can be identified. The cumulative results of these trends can be applied to predict the direction of public opinion about the topic – say a brand or a product or an enterprise itself. These predictions can help analysts foresee the hackers that might target their systems or even competitors that would try to sabotage their brand image through malwares.
- Real-time data processing
One of the most critical factors in algorithm implementation is that the big data reaching it should be fresh, as social media trends change in as much in as much as one second! Real-time data processing provides accurate threat predictions based on the public opinions or unusual user behavior patterns on social media.
- Connecting with the customers
The Sentimental analysis is another technique that can be applied to judge whether a disgruntled customer can indulge in cyber security breaches. On the other side of this coin, the sentimental analysis can also keep the customers safe from information theft or other cyber crimes by implementing a cyber security protocol from the enterprise running on their software products.
In conclusion
Big data plays a major role in cyber security implementations at the enterprise level. Without the massive volume of data sets available for analysis and the right skills for big data application development, the cyber security algorithms are prone to yield false results that may damage the reputation of the enterprise.
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