Data Science & Cybersecurity —
Security professionals used to search; what is big data analytics? Why is machine learning applications so important? Why did InfoSec Professionals require to learn about data science? What to know about "data bots" as a data science professional? Differences in data science vs machine learning? How to crack cybersecurity jobs with data science advantage? And so on...
In simple words; Data science is a multi-sided field that uses scientific techniques, methods, algorithms, and security practices to extract information and insights from the data in several formats, that includes both structured and unstructured, comparable to data mining.
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Data is the secret sauce now. Governments, businesses (both large and small), and organizations across various industries are now realizing the potential of data to uncover new paradigms of success and growth. They are harnessing Big Data to optimize business processes, increase efficiency in productivity, boost sales and revenue, and most importantly enhance the process of decision-making. With the help of Data Science tools such as Machine Learning, Artificial Intelligence, Deep Learning, and Big Data Analytics, businesses can now get access to meaningful insights hidden within massive data sets. The information thus gained becomes the invaluable asset that companies seek to guard with their life.
However, just collecting huge amounts of data and extracting meaningful information from them isn’t enough - there also comes the responsibility to safeguard the information. The cyber threat is a grave issue in the world that is dominated by crucial data. The most challenging fact is that cyber threats have become more malicious and vicious today than ever.
In the previous article, I was talking about; Top 3 Challenges for Staying Safe Online While You Surfing The Internet 2018 | Beginners Guide for making aware about how it's so important to protect your data and keep you safe from online frauds. But in continuing to that, today I want to share some aspects of data science and why it is so important in cybersecurity.
“The sophistication, brutality, and scope of attacks have also raised. We’ve migrated beyond merely defending against criminals. We’re now fighting back opposite to nation-states, organized crime, and a troubling new trend: criminal groups hacking on behalf of rogue nations.”
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Cybercriminals now use sophisticated technology to hack into data systems and deploy covert malware to completely throw off the security measures of the traditional security systems such as Security Event and Information Management (SIEM). The cybercrime landscape has changed so drastically that the present cybersecurity tools are incapable of protecting and detecting complex malicious malware.
This is where Data Science can help create a significant and lasting impact.
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Data Science and Cyber SecurityOrganizations can no longer afford to rely on conventional security systems and tools. Instead, they have to adopt the PDR approach - Prevent, Detect, and Respond. Data Science tools not just help in gathering and analyzing data, but they can also become the key to promoting cyber resilience.
Let's look at how Data Science can enhance cybersecurity:
Big Data AnalyticsBig Data tools facilitate automated collection, organization, and analysis of vast amounts of data to extract useful information, trends, and patterns from it. Moreover, Big Data techniques are not only capable of exploiting raw data but also historical data. This allows data scientists and analysts to get an all-comprehensive view of potential risks and threats.
The 2016 Big Data Cybersecurity Analytics Research Report states that nearly 72 percent of respondents maintain that Big Data Analytics helped immensely in identifying complex and advanced cyber threats.
Combining Big Data analytics with historical data, analysts can create statistical baselines to distinguish between ‘normal’ and ‘anomaly’ in baselines. It becomes possible to combine real-time analysis and historical analysis to identify new deviations that have occurred in the past. Thus, leveraging historical data, they can quickly detect when data is deviating from the specified norms and react promptly to eliminate risks. In this manner, historical data can give birth to new opportunities for predictive, statistical, and machine learning models.
Big Data security analytics can effectively filter out the statistical noise to reduce enormous flows of security events down to more manageable, concise, and structured alerts. Not just that, advanced Big Data security analytical tools can facilitate multiple automated processes to detect and respond to threats. With the help of advanced Big Data frameworks, we can now track and monitor raw logs in real-time and raise alerts the moment any abnormal user behavior is detected within an organization’s network. This has proved to be very helpful in identifying an insider security breaches.
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Machine LearningOver the years, Machine Learning (ML) has gained a lot of importance in the tech sector. Corporate firms and businesses spread across various industries are leveraging ML technologies for enhanced personalized recommendations (Amazon, Netflix, Spotify), voice and speech recognition, and so much more. Now, Machine Learning algorithms are being deployed in security systems to fortify Information Security (InfoSec).
For cyber systems to be resilient and robust, it needs to have such data infrastructure that has the power to automatically identify potentially malicious malware and traffic and also distinguish between normal, benign network traffic and abnormal, harmful malicious traffic. This is where ML comes in. ML algorithms can be used to develop ‘classifiers’ that can narrow down possibilities in binary, good or bad and positive or negative. For instance, from the perspective of network security, ‘positive’ data indicates malicious traffic with the potential for cyberattack or malware infection, whereas, ‘negative’ data indicates normal data and traffic.
Such supervised Machine Learning algorithms can be very advantageous for organizations by helping them identify threats in real-time and taking a proactive stance to prevent the breach of data. Another emerging trend in ML for security is Endpoint Security that leverages deep learning techniques to identify and segregate malware in real-time. Supervised ML algorithms like Random Forest, XGBoost, and Deep Learning Networks (ANN, RNN, CNN) are becoming increasingly crucial in distinguishing between benign data and malicious data; detecting DNS tunnels, malware, and insider attacks, other threats that can be identified from millions of labeled samples (both benign and malware).
Although Data Science is rapidly transforming and fortifying cybersecurity, technology alone cannot prevent the breach of data. Technology has to be backed by the right amount of team effort. Apart from installing security measures such as Firewalls, multi-factor authentication, and data encryption, organizations must actively educate their team on how to use these technologies to detect and respond to cyber threats.
“Hackers routinely target workers who are dangerously oblivious to proper cybersecurity practices. Managers who care about protecting their clients, their firms and themselves must prioritize educating employees of all levels on how breaches occur.”
- Tech Center
Thus, the real progress towards cyber security will only come when human intelligence and discretion is combined with innovative technologies.
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