Machine Learning and Cyber Security

Due to the growing risk of hackers, companies need advanced technology to enhance their cybersecurity. Using machine learning to tackle cybersecurity issues could help companies to secure all digital assets efficiently.

Organizations generate and collect a large volume of data- structured and unstructured- to improve their services. However, the disorganized data is left unused due to lack of advanced technologies. The unstructured data may or may not contain sensitive information. Hence there is a need to secure all the collected digital assets. There is always a risk of hackers waiting for an opportunity to hack the system and steal the classified information. According to Security Intelligence report, “the global cost of cybercrime will reach $2 trillion by 2019, a threefold increase from the 2015 estimate of $500 billion.” This states the rate at which the hackers are hacking the system. This generated a need for advanced technologies that could assist organizations to secure their data efficiently. Even though analysts work on cybercrimes, there is a need for machines that can act smarter and help analysts to handle systems accurately. This technology is called machine learning. The application of machine learning in cybersecurity could prove beneficial to industries in securing their data.

Using Machine Learning to Secure Systems

Organizations, of course, hire analysts who can work to detect any malicious activities in their network. Since bad guys from behind the scenes are waiting for the right opportunity to damage the system by using new technologies, companies must understand benefits that machine learning offer and implement them in their respective sectors. The traditional cybersecurity methods and analysts fail to address the following problems:

• As a large volume of data pours into organizations, it becomes tedious for analysts to analyze and determine where precisely the malware has been injected.
• The second concern is an unqualified analyst who is unaware of the what and how of entire network security processes.
• The third issue is even if malware is detected in a network, the further processes, such as communicating with administrators takes enormous time. It is typically a slow process.

All of these reasons generated the need for more advanced and capable technologies that could assist analyst to detect malware and secure their systems.

Improving Cybersecurity with Machine Learning  

Let us first understand what is machine learning before we move on to how it can benefit cybersecurity. Machine learning is a type of AI, which gathers large chunks of data and trains machines to  make devices act smarter and as intelligent as humans. Here are ways how machine learning can help organizations improve their cybersecurity:

● Machine learning has a fantastic feature, called predictive analysis, which can predict the future outcomes of any system. Based on the datasets, machine learning can predict where, how, and when a hacker will place a malware. This can be a warning sign to analyst and alert them to be ready for any unwanted kinds of stuff happen.

●  Even if hackers hack the system, machine learning will collect the experience as datasets and train systems to detect similar malicious activity in future. 

●  The issue of discovering where hackers have placed the malware is solved with machine learning. Machine learning can search the entire system and the network and find the malware in no time.

●  Another fantastic feature machine learning exhibits is ‘recommending.’ Similar to a YouTube’s recommendation for videos, machine learning can recommend analyst with actions that could be useful for securing their system. Machine learning algorithms find patterns in data and gain insights about it, once networks are trained with datasets.

Now that the idea behind implementing machine learning for cybersecurity is before you, you must apply this technique in your business to overcome challenges faced by traditional security methods. Machine learning technology does not aim at replacing analysts, instead serves as a platform to secure all digital assets efficiently.