Data Is The Secret Sauce To Effective Machine Learning Recipe

In the modern world of innovations around automation, the replacement of humans with machines is talked about on a daily basis in the media. Some of the most talked about use cases include driver-less cars and automated checkout at retail outlets. Additionally, there are some not so obvious use cases such as Google, Facebook and Amazon recommending products that you are most likely be interested in purchasing based on data they collect while you shop or browse online; or Netflix putting the show or movies that you are most likely to watch at the top of your list on their app. The primary element used by all these companies to accomplish this feat is data.
The data, when aligned to real business needs, can provide great insight that is not otherwise possible. Let’s discuss why and how data plays a very important role in facilitating an automated solution suited to customer needs, specifically in the world of cybersecurity for effective defense techniques.
A Thief in the Night
Imagine a scenario where a thief is breaking into your house; similar to an attacker
entering your network. The thief’s constraints in attempting to steal anything include
the limited time on hand, the need to find locations where the valuables are kept, and the weight and the size of the valuables he or she plans to take, among many other constraints. The thief is likely to use abnormal means to enter the house, such as breaking the door or entering through an open window, and will be searching for valuables at random once he or she gets in. Additionally, these activities have to happen rapidly before the thief exits.
The behavior of the thief is quite different from the normal resident of the house. The normal inhabitant of the house is likely to repeat most of his or her activities to a certain degree. These regular activities can be learned and mathematically defined as a set of expected behaviors of the normal occupant of the premises. Similarly, some of the characteristics of a thief can be learned from experience and modeled for a mitigation strategy to be put in place. One real-life mitigation example we are all familiar with is the alarm system in the house –it provides the response to an unauthorized entry in some fixed amount of time. However, more sophisticated thieves diligently account for this mitigation and evolve their methods over time to bypass the alarm, allowing them to commit crimes more freely.
Like sophisticated thieves in the example above, cyber-attacks are growing in sophistication and in numbers. The damage resulting from this increase is felt in multiple circles – the political arena, financial institutions and certainly by individual citizens. The effect of some of these damages can last a lifetime and can bring the victims to the brink of disaster. Emerging modern data science techniques, when combined with myriad types of available data, can help mitigate this to a good extent.. Read More