We can’t imagine our lives without machines, but with the rise of hacking and cyber crime, there are some who are trying to limit the influence of machines. A machine can now do multiple tasks based on a simple voice command – Google, Alexa, Siri, etc. When hackers are able to break into smart devices, bank accounts, computer networks, etc. it creates chaos! Imagine losing a large chunk of your money overnight, at a time when you really need them. Machine is man made and any man made thing can be easily copied or manipulated by another man. By placing our implicit trust in a machine for important functions such as banking, we may lose big in case this machine fails.
What is Machine Learning?
Most people consider Artificial Intelligence and Machine Learning to be the same thing, but actually Machine Learning is a subset of AI which provides the ability to the systems to automatically learn and experience new things without being explicitly programmed for it. Machine learning focuses on the development of computer programs that can access data from different platforms and use it for themselves. The main aim of machine learning is to allow computers to learn automatically without human intervention or assistance.
Machine learning was first introduced by a group of British intelligence agents. They founded a new cybersecurity company called Darktrace.
The company partnered with mathematicians to develop a software/ device that would use machine learning to detect cyber attacks/ crimes. The bedrock of this system takes a new approach to prevent crimes. Whenever a device/ system is accessed by hackers/ criminals, they need to steal the data and transfer it to other devices on the network and devices outside the network.
Scientists have worked on isolating the devices which show an abnormal behavior. Even when a device may be compromised, by isolating the device, it may not be able to send and receive data, thus, making it useless. Therefore, simply hacking into a device is no longer enough! A hacker has to build his own path out of the system with all the data that he has stolen.
To help the machine recognize a new instance of suspicious behavior, the programmers used a new technique called unsupervised learning. Traditionally, machine learning is dependent on the supervision of humans. However, in this situation, the machine changes it’s algorithm according to the specified conditions and it doesn’t need humans to specify what to look for. That is truly path breaking! No more supervision or inputs required for fraud prevention.
The vast majority of machine-learning applications depend on supervised learning which involves feeding a machine with massive amounts labeled data to train it to recognize a narrowly defined pattern. If you want your machine to recognize humans who are wearing suits, you feed it hundreds or thousands of images of people who are wearing suits and of people who are not, all the while explaining it clear which ones are which.
In the field of cybersecurity, supervised learning seems to be a problem, as you can train your machine with threats that your system has faced before. This makes it vulnerable to any threat that it has not seen in advance. Unknown threats are not deduced by machines and other supervised learning work. Additionally, there are data sets available, which make it easy for hackers to predict what an algorithm is trained for. This again makes it easy for hackers to break into a system.
This is why the new system of unsupervised learning excels. The machines look through massive amounts of unlabeled data and it doesn’t follow a typical pattern. So it is able to detect threats that the system has never faced before. Darktrace sets up physical and digital sensors around the network of the client to get an exact detail of the activity.
The data obtained is then funneled to over 60 different unsupervised algorithms that compete with each other to find any suspicious behavior in the system. In case of any red flags, the suspicious device/ network/ software is ring fenced and isolated from the rest of the system in order to stop the hack in its tracks.
Machine learning has helped detect, prevent and fight many cyber crimes across the globe. As the industry grows, the importance of machine learning is growing day by day. The need to learn and the need for experienced hands to manage it is also increasing. The ability to design a software that can detect and prevent crimes without any active efforts on the part of the programmer will surely be a skill.
BSE Institute, a 100% subsidiary of BSE India, provides many short-term online courses on BSEVarsity. It offers courses for students and working professionals who wish to upgrade their skill and knowledge in a constantly evolving workplace. A basic course on Master the fundamentals of Machine Learning can help students and senior professionals to enhance knowledge in the new domain of machine learning.
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