Cybercriminals today are launching sophisticated cyberattacks on organizations’ computer systems. In the wake of such advances, machine learning has become an essential element of organizational cybersecurity programs, which are critical for maintaining digital systems’ security and efficient functioning.
Today, Cyber Defense Magazine estimates that there are more than 376,000 threats created daily. In 2018 alone, attackers deployed more than 10.5 billion malware attacks. Such large numbers are possible because of advances in hacking-as-a-service, as well as open-source development, which lowers barriers for people to create malware variants. Most of these cyber threats are capable of spreading in minutes or hours and are known as “zero-day attacks.”
Zero-day attacks occur when cybercriminals spot security vulnerabilities in an organization's system before their targets and exploit them to introduce malware such as viruses. Because targets are the last to spot the vulnerabilities, they have zero days to launch a defense, hence “zero-day attacks.” According to Ponemon Institute, a research group, 80 percent of successful attacks are zero-day attacks. To bolster their defenses against such threats, organizations must adopt new cybersecurity practices that leverage machine learning.
Traditional cybersecurity solutions rely on blacklist or whitelist-based models. This means they can only identify threats that have been discovered and blacklisted. They are, therefore, powerless against threats that have never been discovered. Machine learning-based cyber protection should be the first line of defense for modern organizations intent on safeguarding their data and digital operations.
Machine learning is a subset of artificial intelligence through which computer engineers feed computers troves of data and the computers use algorithms to learn how to react to the data as humans would. This process can be refined over time to improve the efficiency of the computers.
In cybersecurity, computer engineers feed computers with millions of internal and external data points. The computers then analyze the data, recognize patterns, and build predictive capabilities similarly to humans. Enhanced with machine-learning capabilities, computer systems can accurately predict whether files or processes are demonstrating abnormal activity and take necessary actions to contain threats.
Machine-learning systems can provide malware protection within digital networks, at endpoints, and in the cloud. Regarding digital networks, organizations process millions of units of valuable data belonging to their users. It is physically impossible for employees to inspect all this data. With machine learning, however, they can rely on computer systems as a first line of defense to inspect the data and filter it for suspicious activity in real time. Even if the data moving along the network is encrypted, machine learning can evaluate it without decrypting it, flagging threats hiding in encrypted files. Not only can the technique detect insider threats, it can also pinpoint incidents of policy violations.
Concerning end point protection, machine learning can stop malicious attacks from gaining entry into an organization’s digital systems at points of data input or output. Common entry points for malware today are mobile devices used by employees to perform office work. However, malware can gain entry into a computer network through connected devices that users may not be particularly cautious about, for instance, a fish tank. Machine-learning security systems can spot threats at all endpoints based on the behaviors and attributes of malware, stopping them in their tracks.
Finally, for companies that use cloud systems in their day-to-day operations, machine learning can protect data by evaluating logins for suspicious activity. It can also assess the IP reputations of cloud platforms and apps.