Use of Behavioural Biometrics to Protect Against Mobile Malware

Mobile threat landscape is growing phenomenally. Some of the key challenges facing the mobile threat landscape include the rapid growth in the number of mobile devices in use, the increasing complexity of mobile operating systems, and the emergence of new attack vectors that take advantage of mobile device features such as GPS, NFC, and Bluetooth. Additionally, the increasing reliance on mobile devices for both personal and business use has made them a prime target for cyber criminals, who are constantly looking for new ways to exploit vulnerabilities and steal sensitive data.

Beginning in 2022, a number of fresh Android banking Trojans entered the threat scene. The current ones are also regularly adding game-changing features and enhancing their capabilities at the same time as the new ones. Threat data reveals a concerning trend: malware families that have the ability to commit On-Device Fraud (ODF) are becoming increasingly prevalent.

Rapid increase in the number of mobile apps has brought with it a corresponding increase in security risks. One such risk is the TeaBot Remote Access Trojan (RAT), which emerged at the beginning of 2021 and is designed to steal victims’ credentials and SMS messages. The TeaBot RAT is just one example of the many types of mobile malware that are constantly emerging, and it underscores the need for effective security measures to protect against these threats.

Mobile malware can take many forms and can be spread through a variety of methods, including through malicious app downloads, email attachments, and text messages. It is important for users to be aware of these threats and to take steps to protect their devices, such as keeping their mobile software up to date and being cautious when downloading apps or clicking on links. Users should also consider using security software, such as antivirus software, to help protect their devices from malware.

Potential solution to counter such challenges of advances in mobile malware is the use of behavioural biometrics. Behavioural biometrics involves the use of machine learning algorithms to analyze a user’s unique patterns of behaviour, such as their typing speed, screen touch patterns, and the way they hold and move their device. By analyzing these patterns, it is possible to create a unique “behavioural fingerprint” for each user, which can be used to verify their identity and detect anomalies that may indicate the presence of malware in the mobile device.

Behavioural fingerprints can be used in a variety of contexts, including in the field of cybersecurity. Behavioural fingerprints can be used in conjunction with other security measures, such as password protection, to provide a more robust defense against cyber threats. Behavioural biometrics has the potential to provide an additional layer of security for mobile devices, helping to protect against the latest threats and mitigate the risks posed by mobile malware. By leveraging the power of machine learning, it is possible to stay ahead of the curve and keep mobile devices secure in the face of constantly evolving threats.

There are several ways in which behavioural biometrics can be used to protect against mobile malware. One approach is to use behavioural biometrics as a means of verifying the identity of users when they access sensitive data or systems. For example, if a user’s behavioural fingerprint does not match the expected patterns of behaviour, this could indicate that the user’s device has been compromised by malware. In this case, the system could take appropriate action, such as denying access to sensitive data or triggering an alert to notify security personnel.

Another approach is to use behavioural biometrics to monitor the behaviour of users in real-time, looking for anomalies that may indicate the presence of malware. For example, if a user’s device begins exhibiting unusual patterns of behaviour, such as unusually high levels of network traffic or unusual input events, this could be a sign of malware activity. In this case, the system could take appropriate action, such as blocking access to sensitive data or triggering an alert to notify security personnel.

The use of behavioural biometrics can be an effective way to protect against mobile malware and keep mobile devices secure. By leveraging the power of machine learning, it is possible to stay ahead of the curve and anticipate new threats as they emerge, helping to mitigate the risks posed by mobile malware and keep devices secure.