By: KV Sai Mounish, Department of computer science and technology, Student of computer science and technology, Madanapalle Institute Of Technology and Science, 517325, Angallu , Andhra Pradesh.
ABSTRACT –
Today the demand of biometric security and its application is increased with machine learning. Various ML algorithms are able to determine our behavior attributes such as voices, fingerprints,
Iris, etc. Because of their continuous learning and evolving which makes it more protective to security risks. However there are certain limitations such as data privacy, high computational needs, weak vulnerabilities, etc. So this article focuses on advantage and disadvantage of machine learning in biometric security and suggests any future development and uses of biometric security integrated with machine learning.
KEYWORDS – Biometric security, Machine learning, Vulnerabilities, High computation.
INTRODUCTION –
Biometric security solutions have become an essential defense mechanism in the changing field of cyber security, using distinct behavioral traits to authenticate people. Even if they work well, certain biometric systems have limitations with regard to precision, flexibility, and resilience to advanced weak vulnerabilities. This is where machine learning (ML) enters the picture, providing fixes to improve the dependability and efficiency of biometric security systems.
So machine learning is a branch in artificial intelligence which helps with zero dependence on humans or minute dependence. Machine learning techniques can handle enormous amount of data which will improve accuracy and reduces overfitting to provide extra protection.[1]. The combination of this ML techniques has made unique changes in biometric security like facial, iris recognition.
The article mainly focuses on how different ML techniques will help biometric security more advance and safer, even secure It will be used to inspect the underlying features such as accuracy, scalability, and fitting. Not only that but also tells about negatives involved in biometric security like data privacy, algorithm biases and computation power. It will clarify the concept of machine learning in biometric security and how it would defend against attacks with real life examples.
Process involved in biometric security is explained in Figure 1.
Advantage of Biometric Security
Accurate Identifying in some features like fingerprints, eye scans, will be giving more accuracy when it is compared to convention methods and it will minimize risks such as data manipulation, stolen credentials, since these are unique they are not easy to manipulate.
Continuous improvement is shown tremendous growth in branch of AI and provides most prominent security and also offers scalable and effective solution like no need to remember they are linked to every single individual who will be difficult to outsiders or intruders to interfere in their access so that they cannot forge enough. [2].
Enhanced Security for example if we take apple id it will use face id for facial recognition, and siri for voice recognition and works as a assistant which will help in accurate identification and recognition of faces, etc.
Real-Time Processing and Scalability the efficiency and scalability of biometric security systems are improved by machine learning. Mobile devices, access control systems, and large-scale public security implementations are just a few examples of applications that can benefit from machine learning (ML) models because they can handle and process enormous amounts of biometric data in real-time. Deploying biometric technologies in high-user-traffic scenarios requires the systems to be scalable and retain high performance[3].
There are many other advantages which include enhanced security which will make them resilient to complex security attacks and can detect changes in certain behavioral patterns.
We will explore further regarding disadvantage involved.
Data Privacy Issue
Coming to data breaches there will be misuse of biometric data the possibility of data leaks is one of the biggest privacy issues with biometric security solutions. Biometric information, such fingerprints, face features, and iris patterns, is fundamentally personal and cannot be altered in the event of a breach, unlike passwords or PINs. Theft of biometric information might result in serious and permanent privacy violations. Cybercriminals who have access to biometric databases may utilise the data for fraudulent schemes, identity theft, and other nefarious purposes. To reduce these dangers, it is essential to guarantee the transmission and storage of
data[4].
Abuse of Biometric Information another major privacy risk is the exploitation of biometric data by both authorized and unauthorized groups[5]. Businesses that gather biometric data may use it for reasons other than those for which it was originally disclosed or intended[6].
The demand and various aspects of biometric security is shown in Table 1.
ASPECT | DESCRIPTION |
Demand | Increasing demand for biometric security |
Applications | ML algorithms determine various attributes such as voices, fingerprint, and iris patterns |
Advantages | continuous learning, evolving to enhance security against risks |
Disadvantage | data privacy concerns, high computational needs ,potential vulnerabilities |
Table 1: Aspects and description
CONCLUSION –
In conclusion, the world today is seeing a real rise in biometric security, especially with machine learning stepping up to the plate. Sure, there are impressive ML algorithms that can recognize our unique traits like voices and fingerprints, along with our irises. It’s incredible how these systems learn and adapt, making them pretty strong against security threats. But, let’s remember there are some bumps on the road. Issues like data privacy and their hunger for computational power can’t be ignored. Plus, weaker spots in the systems need attention too. This article has explored both the benefits & drawbacks of combining machine learning with biometric security. Looking ahead, there’s a bright future for developments in this field. With thoughtful improvements, we can fully harness the potential of this tech to keep our data safe.
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Cite As
Mounish K.V.S (2024) Machine Learning in Biometric Security Systems, Insights2Techinfo, pp.1