Note: This is a guest post written by Dan Martin
Whenever we have an idea, we assume the outcomes before analyzing the loopholes. It was always the case before data science came into play. The decisions made by the organizations upon facing a threat had always been ambiguous and subjective.
But, after data science was taken into consideration for cybersecurity, the scary darkness of decision making has mostly been factual, informed, and more effective.
Although data science tools like machine learning models make the job of cybersecurity experts easy, the contribution of the experts to the field is still relevant and critical. You’d often need to hire data security experts to make your raw data comprehensible by ML models and to unlock the full potential of their relationship.
What Is Data Science
Describing data science as the study of data is belittling to its importance. Data science is a critical set of tools that are used to collect, study, process, and extract valuable information from sample data available through data mining.
And when paired with machine learning models, it can accurately predict and conclude future behaviors of customers, employees, and cyber attackers.
As the online presence of your company rises, so does the importance of employing extensive cybersecurity measures. But, only having SSL certificates and blocking malicious bots are often not enough. In addition, You’d need to understand how attackers think and operate, which can be comprehended through data science and machine learning.
In 2020, the global average data breach cost was around $3.86 million. Which included the downtime, revenue loss, discovering, and responding to the attack. This number increases manifolds if ransomware attacks are included in the stat.
Cybersecurity is the practice of protecting yourself from cyber attacks that can cost you reputation, money, and even your business operations.
While the term cybersecurity has been there from the day of inception of the internet, it’s coming more into focus with the rising number of data breaches, Dos, and DDoS attacks. To prevent that from happening to your business, let’s discuss how you can implement machine learning in accordance with data science in your favor.
Data science in today’s world is mostly dependent on big data and machine learning. There are several ways machine learning and data science can be utilized to fix security vulnerabilities.
Today, it is easier for data scientists and everyone involved to make use of data using different ML models as there are various processes and tools that help manage these models. Cybersecurity ML models can now be stored in ML registries so that they can be used and accessed whenever needed.
The machine learning model registry is a central repository designed for testing, analysis, and deployment and is used to publish ML models. This way, you won’t need to create a new model from scratch all the time. The registries help to train your model with already available data. ML model registry also provides collaborative services for lifecycle management.
Regression methods and classifications come under supervised learning. The supervised learning method is a task-driven approach and is effective in predicting particular security threats that have been discovered before.
It’d not always be possible for you to predict the threat depending on previous data. In a malware attack, the dynamic behavior of the threat makes it almost impossible to detect before the attack is executed.
Unsupervised learning models like the bottom-up clustering approach and the principal component analysis are particularly effective in detecting such threats.
As the main task of unsupervised learning is to find patterns and structures in raw unlabeled data, you’d need to utilize the means of it to detect policy violations, anomalies, and ransomware attacks.
Deep learning or neural networks are data science models that are inspired by biological neural networks in humans. Deep learning is used when large data is available in complex formats like audio, video, and images.
While the training time for neural network models like multi-layered perceptron (MLP) and convolution neural network (CNN) is significantly higher, the same can’t be said for the testing time.
Now as we’ve discussed the types of models and how data science helps enrich them, let’s understand how you can benefit from them directly.
The game of detection and prevention of breaches between cybercriminals and experts has never been more challenging. With increasing access to tools, methods, and styles, hackers can become a nuisance if you aren’t prepared for their next move.
Interestingly, despite taking preventative measures, hackers always had their way. But only after the introduction of data science and machine learning in cybersecurity, the rate of data breaches has seen a significant drop.
As machine learning covers every corner of the network looking for loopholes, finding vulnerabilities in your network has never been easier and safer.
It’s often not enough to train the ML models with previous breach data to prevent the next one. With ample data and ML model training, it’s now possible to even detect the behavioral patterns of the attackers to prevent future cybercrimes like ransomware and malware attacks.
The attackers generally follow a simple set of behaviors that starts from harmlessly surfing through the domain and ends up in finding loopholes. Whenever that certain behavioral pattern is noticed in any of the users, previously modeled machine learning algorithms can notify you or prevent such attacks.
The fundamental theory behind incorporating data science and ML in cybersecurity is learning from previous similar events. The data science experts extract past data of attacks and how the organizations handled them to analyze and come up with a possible solution for the ongoing breach.
Data science is also being used to prevent frauds like abnormal credit card purchases and false identifications. There have been instances, where declined credit card transactions were used to drain money from an organization. As these issues have already been patched, you can be ready for the same type of attacks happening to your company by using data science tools.
Data science and machine learning have come a long way in the past decade in terms of cybersecurity. But there are vast amounts of uncharted territory that still can be explored to unlock the maximum potential of the symbiotic relationship of data science and data security.
As time goes by, there will be much more data available for the machine learning models to train and it’ll be more challenging for the hackers to keep up to the pace. But, the role of data scientists still remains important as the false positive cases happen far too often to leave machine learning models to work on their own.