Computers are not only becoming more powerful, but they’re also becoming smarter, which is to say they’re becoming more capable of performing tasks that only humans have been able to do. The type of computing that emulates human-like intelligence is called artificial intelligence, or AI, and one of the most powerful developments in AI is machine learning.
The power of machine learning stems from its ability to enable computer programs to improve themselves by learning through experience. It is the breakthrough that has enabled cutting-edge developments such as self-driving cars. It is also the technology behind virtual personal assistants, such as Siri and Alexa, that understand and communicate with natural language.
By emulating the learning style of humans, machine learning software is able to perform many useful functions when it’s applied to large amounts of data. Consequently, this technology is presently having a big impact on many different industries, not the least of which is cybersecurity.
Why is machine learning such a powerful tool?
Machine learning is particularly useful in various applications of data analysis. It learns from data, identifies patterns, automates model building, and makes decisions, and it does all these things with little or no human intervention.
Machine learning algorithms can apply complex mathematical formulas to large data sets repeatedly, and as the software learns and adapts to new data, the process becomes faster and produces better solutions. These abilities are very advantageous in the field of cybersecurity, which utilizes large data sets and behavior pattern analysis.
Stemming the tide of cybercrime
The cybersecurity industry has struggled to keep pace with the growth of cybercrime. In 2005, there were 8.3 million reports of identity theft. By 2014, this number rose to $17.6 million. The amount of money consumers have paid to unlock computers from ransomware grew from $1 million in 2005 to $24 million in 2015.
Consequently, businesses face rising costs due to security threats. The annual cost of combating cybercrime for the average business rose from $24,000 in 2005 to $1.5 million in 2015. Organizations have struggled to acquire enough personnel with the skill sets needed to deal with these rising security threats. Fortunately, the advancements in security software with machine learning have helped fill these gaps.
Emerging technologies
Machine Learning (ML) uses advanced algorithms to learn from previous incidents, which makes it more capable the next time threats arise. ML also identifies patterns of malicious activity and analyzes large sets of data to identify new imminent threats.
Advanced AI and machine learning applications are being utilized in cutting-edge cybersecurity services such as Managed Detection and Response MDR. These services combine the skill of human analysts, forensic investigation tools, and anomaly detection software to respond to threats in real time, 24 hours a day.
Other examples of machine learning applications are being offered at IBM. Intelligent Finding Analytics (IFA) is a machine learning tool from IBM that helps reduce false positives from security testing. This, in turn, reduces the workload on security teams and boosts their productivity. IBM also offers a technology called Intelligent Code Analytics (ICA), which provides cognitive computing features that enhance security software’s ability to analyze new or unfamiliar computer coding languages.
Machine Learning and AI are powerful tools to enhance the capabilities of cybersecurity teams, but they should not be viewed as a silver bullet. When new technologies are developed, they not only bolster security, but they also fall into the hands of criminals who can use them for malicious deeds. Although cybercrime will not go away, machine learning is providing much more powerful solutions to help organizations fend off criminal threats.