Machine Learning
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 humans 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.
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.
User Behavior Modeling
Some cyber threats 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 cyber threats 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.
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 attem
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 cyber attacks as they become more and more tech-savvy.
Take into account the devastating Not Petya attack that utilized Eternal Blue, 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 cyber attacks.
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