Companies still struggle with ransomware, phishing, data breaches and other attacks that bypass their security and affect their budgets. Enterprises know they are in dire need of technology that will safeguard their infrastructures from known, unknown, and undisclosed vulnerabilities.
By 2022, data protection and cyber terrorism will increase security needs, generating growth of over $200 billion for the cybersecurity market, Markets and Markets finds. The increasing number of aggressive attacks on critical infrastructures and the overall sophistication of malicious software released in the wild have prompted security experts to resort to machine learning algorithms to improve cybersecurity.
Although experts have repeatedly pointed out that machine learning is not a silver bullet or a universal solution, it’s one of the ingredients in a sturdy cybersecurity solution. The uses and ramifications of machine learning in cybersecurity are far-reaching as the technology has a great deal of potential to revolutionize the industry’s approach to security, as well as enhance productivity through automation, cybersecurity skill gap removal and decrease in human errors.
Machine learning algorithms are trained to make rigorous predictions based on the analysis of extensive datasets and, although they are fairly accurate, they need to be mixed with other security layers as they may also generate false positives. Another issue to consider is data quality and the volume used to train the algorithm.
In cybersecurity, machine learning has proven extremely effective at improving malware detection by instantly identifying and removing malicious programs, yet it suffers some constraints generated by the level of accuracy in datasets and attack patterns in current breaches. The industry has not excluded the importance of human engineers in updating machine learning algorithms and ensuring the datasets on which learning and predictions are based are accurate and complex.
Machine learning has been put to great use in multiple sectors but it has become popular in the security landscape due to its ability to categorize data and analyze malicious code in real-time to ensure threats are blocked from moving laterally across networks. So far, machine learning has proven far more thorough and efficient in advanced endpoint protection and in blocking breaches than traditional security. Machine learning can be extremely valuable for IoT-network security, a segment heavily targeted by attacks and difficult to protect due to difficulties in categorizing abnormalities and the large number of protocols involved.