Is Machine Learning Solving User Authentication Challenges in App Development?

Digital security is a greater concern in 2019 than ever before. Over the years, cybersecurity experts have come to a hard truth. They have recognized that password protection alone is inadequate for many applications. They need more sophisticated ways to authenticate users. Fortunately, machine learning is providing some new solutions to this challenge. It can evaluate a number of factors, including dynamic keystroke patterns of users.

Machine learning approaches to user authentication are proving to be especially useful for mobile applications. This is one of the many things that developers must think of when they are trying to decide how to build a social media app. There are too many mobile security risks these days including:

• The risk that mobile devices can be stolen and breached
• A growing number of password crackers give hackers access to mobile applications remotely
• Trojan software can infiltrate mobile apps and hijack the users’ account

Traditional authentication systems are inadequate for handling these threats. Machine learning is filling the void by offering more comprehensive protections.

How is machine learning improving the effectiveness of user authentication?

CSO Online reported that the same types of machine learning algorithms that have been used by financial services companies are now being employed by mobile application platforms. These algorithms can evaluate a number of different factors to distinguish authorized users from hackers that may have attempted to gain access to their accounts.

Previous attempts to thwart security breaches often lead to a number of false positives when looking for potential intruders. In other instances, they failed to notice legitimate security risks until it was too late.

One PayPal user told us about their experience back in 2011. They traveled from California to New England to see their family for the holidays. They attempted to transfer money to their bank account. PayPal recognized that they were using a different IP address, so the company locked their account for several days.

The user appreciated that PayPal was taking more careful steps to stop potential hackers. After all, passwords can be easily cracked with enough time and the right tools. The problem was that doing only one thing out of the ordinary raised red flags with PayPal security algorithms and caused a lot of inconveniences.

More modern security solutions rely on more sophisticated machine learning technology. They use a number of different behavioral factors for user verification. These include:

• The geographic location of the user
• The time of day that the user tends to log into their mobile accounts
• The ISP that the user tends to access the mobile application from
• Whether the user is logged into their Facebook or other social media, account that they use for verification
• How quickly they can enter their password
• Common keystroke dynamics, including mistakes they are likely to make (such as accidentally entering an additional character in their password and needing to hit the backspace key before submitting it)

It is unclear how many variables machine learning algorithms attempt to account for while improving their user authentication models. However, they are clearly much more effective today than they were just a few years ago. As they incorporate more variables into their algorithms, they will become increasingly more effective at deterring mobile security breaches.

Are there limits of machine learning for preventing mobile security breaches?

Machine learning is becoming increasingly effective at stopping hackers from gaining unauthorized access to mobile applications. However, there are some limitations that need to be accounted for.

Hackers might be able to eventually find ways to reverse engineer these user authentication algorithms. They could use a number of strategies to collect intelligence on their targets. This could include using keylogger malware applications to identify the times when users are most likely to access their accounts. By reviewing the timestamps of their activities on their mobile devices, they might be able to mimic them better. They could also try to use malware with geolocation technology to identify their location and most frequently used IP addresses. Hackers might find ways to spoof their victims’ IP addresses to fool the authentication technology. If that is not possible, they could still use a VPN to access an IP address in a similar vicinity.

Developers must also make sure that their machine learning algorithms don’t include variables that are nearly impossible to predict. Some variables could be entirely random, so using them for user authentication could lead to a number of false positives that could frustrate users by locking them out of their accounts.

Developers are going to need to take time to perfect their user authentication models. However, so far, they are proving to be highly effective.