The Role of Data Science in Cyber Security
Data science is a wide-reaching field, but its uses aren’t all generic. Data Science can be applied more specifically to the field of cybersecurity, to help improve information security and help protect computers. As two of the largest and quickest improving fields of interest, data science and cybersecurity are important for each other. As time goes on and technology advances, these two industries are becoming even larger and more important in our daily lives. In fact, the cybersecurity industry alone is expected to be valued at $35 billion by 2025.
With the help of Data Science knowledge , individuals can apply their skills to cybersecurity to protect the information, computers, and networks from breaches or attacks. Potentially, the many applications of data science to cybersecurity could help save millions of dollars.
What’s the relationship between data science and cybersecurity?
You may be thinking that data science is beneficial for all businesses and sectors- and you’d be correct. But, unlike other areas data science may be applied, cybersecurity is much more closely intertwined with the field. In short, almost the entirety of cybersecurity is based around gathering and protecting the information, and the use of data science thrives and relies on that information. By applying data science to a set of data, data scientists can discover patterns, trends and hidden information that may help improve computer security.
Like the data science industry, the cybersecurity industry is always looking for quick-minded individuals who can solve problems and protect cybersecurity information. When you learn python for data analysis, that same programming experience can easily be applied to different problems for cybersecurity, but without having to learn an entirely different field.
How can we use data science to help cybersecurity?
As an example, when analyzing the data surrounding previous cyber attacks, you may be able to identify a particular area of weakness- or a particular time an attack might come. You may be able to predict behaviour based on the information you have, which could result in potentially bad situations being flagged before the information is actually stolen.
Data for deep learning and training
Data isn’t just random, it often forms a pattern- whether we as humans can see that or not. As a data scientist, it’s your job to organize that data in a meaningful way. Often times, the data collected for cybersecurity measures hold the key to finding out when or where a future attack may be. By using this data to teach a machine to identify these attacks and their foreboding features, the system can help alert humans to issues before they happen or even activate safeguarding measures. Because of the way this system feeds off data, it will become more reliable and more accurate over time, allowing it to spot potential loopholes other prevention methods may miss.
Identify abnormalities
When dealing with large data sets, it becomes easier to identify abnormalities in user behavior, devices or activities as they happen. With a well put together system, these abnormalities can be sorted and flagged if needed, potentially preventing future attacks.
Predict potential problems
Having such a large set of data to analyse is not only useful for identifying threats or weaknesses, but also to predict such things in the future, thereby saving money and time. Commonly, cybersecurity programmes focus on a small range of potential threats or a single viable sequence of events, but the real-life humans organising attacks aren’t always so easily predictable.Data science models are much more accurate than other means of predicting potential threats.
Recommend the right action to take
When an abnormal action, a sequence of events or a point of weakness is detected a human being normally analyses the data and makes a decision. Unfortunately, though, human beings are prone to human error. A data science model can not only identify when a threat could be coming, but it can also help advise on the best course of action depending on previous actions taken. This can be especially valuable when a complicated situation comes up.
Protect valuable information
A data attack can cause the loss of valuable data and information, hence why cybersecurity is such a large industry- charged with protecting millions of sets of information all over the world. But, paired with data science, the normal tricks used to protect information become significantly more accurate. In fact, data science can even help businesses decide which computer security programme they need to develop and where they need to focus on.
Being able to identify the right abnormalities
It’s all well and good identifying abnormalities, but, unlike computers, normal human behaviour has masses of variation. In our day to day lives, we create our own abnormalities and the data reflects that. Thankfully, when machines are fed this data they’re able to tell the difference- preventing false positives and the loss of money that comes with them.
There’s no limit to the application of data science to cybersecurity
Using data science to improve cybersecurity is a relatively new phenomenon. As both industries improve more and more applications for data science in the field of cybersecurity are found by the year. A few years ago data science wasn’t used for cybersecurity, but now data science widely contributes to statistical methodology, machine learning, Big Data analytics for network modelling, anomaly detection, forensics, risk management and more- and on top of this, it’s likely to find more applications year after year.
Technology is constantly improving and becoming more effective
From developing software to specifically tune itself to the actions of each user and more accurately identify abnormal behaviour, to using large amounts of data to identify how malware changes and how cybersecurity should change with it. Technology is always improving, and part of that is the increased application of data science in the field of cybertechnology.