Smartphones are used by 7.1 billion people but face significant security challenges. Behavioral biometrics, focusing on unique user behaviors, is emerging as a robust solution for better protection.
Updated 12 August 2024
Global Delivery Head at Appventurez
Smartphones have become an integral part of our lives with each passing day. For instance, 7.1 billion people worldwide use smartphones, with an average screen time of 6 hours 40 minutes. Each person selects a different method of authentication.
Recent smartphones have many privacy concerns, like data breaches or credentials theft, and many other concerns that must be resolved. Also, these issues are not limited to a few users but affect many smartphone users.
In 2023, about 8 million datasets were recorded to be exposed to the Internet worldwide. Most users don’t even know that their security has been breached and their credentials are exposed to the open Internet.
Smartphones have a massive amount of personal data, such as voice recordings, personal pictures, videos, digital prints, and other personal information. If any unauthorized person has access to them, that can even lead to security penetration.
About 53% of companies have occasionally faced a data breach, even when investing in security infrastructure. A small smartphone with minimum security will indeed have privacy concerns.
These smartphone security stats are concerning, and if the data is this personal, then different security methods should be used to preserve it, whether it is to secure it from a hacker or someone trying to impersonate the user.
There are some ways to access someone’s smartphone data without their knowledge. The main problem here is that smartphones don’t have very high-end security, which invites problems with them like:
People are afraid of data theft because many personal things, such as chats, calls, or even media, should not be in the public space.
It could be coined as the trial-and-error method in which people use different combinations through an automated program to find the right one.
A small malware helps the hacker access the system to spy on the passwords and the activity throughout the phone.
The hackers will use the lists of compromised user credentials to breach the smartphone’s security and access the data and media.
Modern smartphones use many screen-locking techniques to protect user data while enabling easy access. PINs, passwords, pattern locks, and biometric choices like fingerprint and face recognition are examples of standard techniques. These solutions accommodate user needs and preferences by providing varying security and usability.
Modern smartphones contain many types of screen lock authentication services:
This tech is quite common and used worldwide. It uses fingerprints to authenticate and allow access to phones and files. It is very convenient for the end-users and takes minimum time to authenticate.
This method of security is relatively easy to use. You just have to remember the PIN, pattern, or password. Then, you just need to enter a personalized password, and authorization is successful.
This is also the second most used authentication method. It creates facial points that combine to give a facial pattern, which is used to recognize the face again. It is another convenient method for authenticating with a minimum of time.
It is not a widely accepted method of authenticating because of its flaws. Many pointers demonstrate that it is inconvenient, as one voice sample is recorded for a specific keyword to analyze the pitch. People with similar voices can unlock each other’s phones.
This is a highly secure method of security. A camera scans the retina and iris of the eye, which is processed with high-end software. Only a specified person can open a phone. But the main problem is the hardware and camera; both should be high-end to scan the retina.
Many factors influence smartphone reliability. Even while screen locks such as face locks, pins, or fingerprints are considered the safest options, fraudsters can still take advantage of weaknesses in the system.
Only to a certain extent, fingerprint recognition, facial recognition, and complicated pattern locks make phone screen lock security largely dependable.
For instance, someone may imitate your fingerprint impressions to get around the security procedure. Moreover, brute force breakers can breach security by repeatedly entering pin or pattern combinations.
Smartphone security features offer advantages and disadvantages. While some features are trustworthy, some are not. There has been a lengthy and continuous discussion about smartphone screen lock concerns.
The trend leans towards highlighting the cons of smartphone security. Many incidents prove that recent smartphone security features are practical, but only to a certain extent. This limitation makes it easier for hackers to gain unauthorized access to users’ data.
Many organizations are currently working on behavioral biometric authentication as a solution to smartphone safety, and behavioral-based security will be the subsequent significant development in smartphone security.
Behavioral-based security research aims to create an algorithm to recognize the owner by examining touch locations and patterns particular to a smartphone user.
The program can continuously learn from and adjust to minor changes in the user’s behavior using machine learning techniques. This improves security and offers a smooth authentication process.
Appventurez is one of the leading technology companies actively working on machine learning, ensuring the safe integration of AI/ML into mobile applications and software to protect against attackers and malware.
Many recent advancements have been made in screen lock authentication processes, but the trending pointer is behavior-based security. Much research and development have gone into it, and it could be the next big thing in the security industry.
The purpose of developing behavioral biometric security is to create an algorithm that recognizes the owner by learning the touch points used by the smartphone user.
The tech industry is moving toward behavioral patterns that are unique to each person rather than depending on any physical aspect of biometrics. In this case, there wouldn’t be any need for security passwords or biometrics, but the device analyzes the behavioral patterns and gives authorization accordingly.
Let’s explore behavior-based security in the tech industry, which has been called behavioral biometrics.
These authentication services are based on phone-using patterns. Every smartphone user has a unique style of using it, and every time someone tries to open the phone, the behavioral touchpoints are used to authenticate the user.
But what is unique about behavioral biometrics?
Usually, smartphones use physical biometrics to authenticate, but it is different in behavioral biometrics. It doesn’t need physical biometrics, but the authentication is based on digital touchpoints and the user’s behavioral pattern.
In more detail, the user’s behavioral pattern includes digital touchpoints, the amount of time spent on the app, and even keystroke dynamics. Behavioral patterns include many more things that make them more secure than the usual biometric authorization.
Understanding behavioral biometrics includes many things, like how it works and how user behavior is analyzed for further authentication. Many questions and concerns about behavioral biometrics still need to be answered or noticed.
The central question is how this behavior of biometric recognition works.
Behavioral Biometrics involves a complex analysis of every digital touchpoint to authenticate the user. Many machine-learning companies are interested in this complex authentication process. Behavioral biometrics is undoubtedly different from traditional biometrics because, in behavioral biometrics, the whole authorization relies on the system, not on any physical parameters.
If the authentication is based on the system’s decision, then what is the system’s working mechanism? Here is the working of the system:
The user behavior is collected in the form of data and later analyzed. The whole machine learning system includes many factors that may be considered as data like:
The analyzed behavior is stored as data. After a few device uses, machine learning records the behavior metrics. The analyzed behavior is established in the algorithm for recognition next time.
Authenticating the user in behavioral biometrics is a continuous process. While the user is on the device, the authorization takes place. The whole process makes the user experience more secure.
The device is locked when an abnormality in the behavioral pattern is detected. The AI/ML model confirms it with the stored metrics and data. Then if the data doesn’t match the device is locked. So that only the authorized user can open the device through a unique behavioral pattern.
Behavioral biometrics have been a magnificent authorization solution in different fields. The tech industry is trying to adopt this new way of authentication. Behavioral biometrics is a continuous authorization. Any unrecognized pattern between two consecutive uses would lock the device instantly. Many different fields are using it in various ways:
It can authenticate the customer and suggest products according to their needs or purchase history. With behavioral authentication, fraudsters can be easily identified, and fraud prevention is easier.
Behavioral biometrics can make financial transactions much safer. If the behavioral pattern differs from the data, creating a transaction through the device would be impossible.
Accessing sensitive data during remote work is a big concern. With behavioral pattern recognition, anyone other than the authorized person can not access the data, even from the authorized device.
Behavioral biometrics is the future, but is it 100% safe? The answer is undoubtedly no because many experiments confirm some loopholes in the system.
A few loopholes are minor and will be updated over time, but a few are significant and require much attention and time. When the password is not physical but a set of data the system calculates to authenticate the user, there are many chances to authenticate the wrong person.
For instance, if someone recorded a user’s behavioral pattern in a data set and then used it to bypass behavioral authentication, it’s a matter of concern. However, with time, all the factors considered can be improved.
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Global Delivery Head at Appventurez
Ashish governs the process of software delivery operations. He ensures the end product attains the highest remarks in qualitative analysis and is streamlined to the clientele’s objectives. He has over a decade of experience as an iOS developer and teams mentorship.
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