How AI and Machine Learning Are Helping Companies Prevent and Defeat Cybersecurity Attacks
Each year, the number and complexity of cybersecurity threats increase, exposing sensitive consumer data to unscrupulous hackers. According to CSOOnline.com, the 2017 worldwide cost of ransomware attacks like WannaCry exceeds $5 billion. However, with advances in artificial intelligence, analysts can use machine learning to move the cybersecurity industry toward predictive offense strategies, making it easier to prevent, and even defeat cyber attacks.
Predictive Capabilities
Machine learning is the process of training and testing data against an algorithm so that a system can better learn to predict a known outcome, given certain inputs. Security analysts can use large data samples, or big data, from previous attacks to determine if an event has qualities that meet the definition of a malicious or benign attack.
This information is then applied to events happening in real time. Helping cybersecurity specialists assess whether or not an attack is likely to take place. However, failure to prevent an attack can actually help improve a threat detection model, since there's generally a positive correlation between the amount of training data used and the effectiveness of the model itself.Companies that have yet to analyze their security-related data can use machine learning techniques to assess which characteristics constitute a certain event classification or threat level. In 2017, Symantec identified 100 new malware families, a 36 percent global increase over previous years.
Adaptability
Many companies are now handling terabytes or even petabytes of data on a daily basis. This enormous scale of data, combined with the increased sophistication of attacks, presents a real problem for the security of any company. In addition, traditional malware detection strategies increasingly fail against new attack methods.
The Defense Advanced Research Projects Agency (DARPA), part of the U.S. Department of Defense, currently uses machine learning to reverse engineer malware, conduct social network analysis on malicious code and quickly categorize malware threat levels, in real-world time. Machine learning techniques are also being applied to detect internal threats via user account activity monitoring so that corrupted accounts can be weeded out before they can do damage. Deep learning, a type of machine learning modeled after the human mind, can help security teams analyze mountains of real-time data in record time.
The steady increase in cybersecurity attacks necessitates a new offensive strategy, across industries. Applying AI and machine learning solutions can significantly improve the safety and security of businesses everywhere and help eliminate the next big threat before it even happens.
