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Top 5 Applications of Machine Learning in Cyber Security

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  • Difficulty Level : Easy
  • Last Updated : 11 Dec, 2020

Cybersecurity is a critical part of any company. Not only companies but even governments need top-class cybersecurity to make sure that their data remains private and is not hacked or leaked for all the world to see! And with the increasing popularity of Artificial Intelligence and Machine Learning, these technologies are even becoming key players in the field of cybersecurity. Machine Learning has many applications in Cyber Security including identifying cyber threats, improving available antivirus software, fighting cyber-crime that also uses AI capabilities, and so on.


The last point is extremely relevant as many cybercriminals also use Artificial Intelligence and Machine Learning to improve and enhance their cyberattacks. According to a study conducted by Capgemini Research Institute, AI is necessary for cybersecurity because hackers are already using it for cyberattacks. 75% of the surveyed executives also believe that AI allows for a faster response to security breaches. Therefore, Machine Learning based cybersecurity software is fast becoming a necessity and not only a luxury.

So let’s see the top 5 Applications of Machine Learning in Cyber Security which companies can use so that they are safe and secure. Companies can easily do this by first implementing AI in their existing CyberSecurity protocols and then move on to specialty AI and ML Cybersecurity vendors. This can be done by using predictive analytics to detect threats and malicious activity, using natural language processing for security, enhancing biometric-based login techniques, etc.

1. Cyber Threat Identification

Cybersecurity is a very important component of all companies. After all, if a hacker manages to enter their systems, they are toast! The most difficult component of cybersecurity is finding out if the connection requests into the system are legitimate and any suspicious looking activities such as receiving and sending large amounts of data are the work of professionals in the company or some cyber threats. This is very difficult to identify for cybersecurity professionals, especially in large companies where requests range in the thousands all the time and human s are not always accurate. That’s where machine learning can provide a lot of help to professionals. A cyber threat identification system that is powered by AI and ML can be used to monitor all outgoing and incoming calls as well as all requests to the system to monitor suspicious activity. For example, Versive is an artificial intelligence vendor that provides cybersecurity software in conjugation with AI.

2. AI-based Antivirus Software

It is commonly recommended to install Antivirus before using any system. This is because antivirus protects your system by scanning any new files on the network to identify if they might match with a known virus or malware signature. However, this traditional antivirus requires constant upgrades to keep up with all the upgrades in the new viruses and malware being created. That’s where machine learning can be extremely helpful. Antivirus software that is integrated with machine learning tries to identify any virus or malware by its abnormal behavior rather than its signature. In this way, it can manage threats that are common and previously encountered and also new threats from viruses or malware that were recently created. For example, Cylance a software company has created a smart antivirus that learns how to detect viruses or malware from scratch and thus does not depend on identifying their signatures to detect them.

3. User Behavior Modeling

Some cyberthreats can attack a particular company by stealing the login credentials of any of their users and then illegally logging into the network. This is very difficult to detect by normal antivirus as the user credentials are authentic and the cyberattack may even happen without anyone knowing. Here, machine learning algorithms can provide help by using user behavior modeling. The machine learning algorithm can be trained to identify the behavior of each user such as their login and logout patterns. Then any time a user behaves out of their normal behavioral method, the machine learning algorithm can identify it and alert the cybersecurity team that something is out of the ordinary. Of course, some changes in user behavior patterns and entirely natural but this will still help in catching more cyberthreats than conventional methods. For example, there is a cybersecurity software provided by Darktrace that uses machine learning to identify the normal behavioral patterns of all the users in a system by analyzing the network traffic information.

4. Fighting AI Threats

Many hackers are now taking advantage of technology and using machine learning to find the holes in security and hack systems. Therefore, it is very important that companies fight fire with fire and use machine learning for cybersecurity as well. This might even become the standard protocol for defending against cyberattacks as they become more and more tech-savvy. Take into account the devastating NotPetya attack that utilized EternalBlue, a software hole in Microsoft’s Windows OS. These types of attacks can get even more devastating in the future with the help of artificial intelligence and machine learning unless cybersecurity software also uses the same technology. An example of this is Crowdstrike, a cybersecurity technology company that uses Falcon Platform which is a security software imbued with artificial intelligence to handle various cyberattacks.

5. Email Monitoring

It is very important to monitor the official Email accounts of employees in a company to prevent cybersecurity attacks such as phishing. Phishing attacks can be done by sending fraudulent Emails to employees and asking them for private information such as sensitive information related to their job, their banking and credit card details, company passwords, etc. Cybersecurity software along with machine learning can be used to avoid these phishing traps by monitoring the employees’ professional emails to check if any features indicate a cybersecurity threat. Natural language processing can also be used to scan the Emails and see if there is anything suspicious such as some patterns and phrases that may indicate that the Email is a phishing attempt. For example, Tessian is a famous software company that provides Email monitoring software that can be used to check if an email is a phishing attempt or a data breach. This is done using natural language processing and anomaly detection technologies to identify threats.

Future of Machine Learning and Cybersecurity

Machine learning is still a comparatively new addition to the field of cybersecurity. However, the above given 5 applications of Machine Learning in Cybersecurity are a good start in this field. The only thing to keep in mind is that machine learning algorithms should minimize their false positives i.e. actions that they identify as malicious or part of a cyberattack but that are not. Companies need to ensure that they consult with their cybersecurity specialists who can provide the best solutions in identifying and handling new and different types of cyberattacks with even more precision using machine learning.

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