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 –
As modern people, cyber threats are very common now in the world so, there is now need to use concrete security measures because the previous protective methods such as passwords, Pin, etc. , are easily hacked. This is the time where biometric security will surge which assist in the protection of data belonging to the user. But there will be some sort of privacy issues, data manipulation. That’s why this article is going to be devoted to the topic of integration of machine learning solutions to biometric security in the aspect of enhancement.
Therefore in general, machine learning has the capability to learn from the analyzed data and understand the patterns of complexity to brought immense benefits to biometric security. This article informs you about such techniques as how the fingerprint scanner, face recognition, and iris scanning work. And explain how the application of the machine learning uplifts the verity of these systems.
Also, regarding the topic of machine learning in this article, examples of real-life cases are described and it also tells about anomaly detection, how to extract features. Thus, amalgamation of machine learning with biometric security not only safeguards from Cyber Criminology but also formulates novel solutions for fresh problems. Thus this article also describes the current status, challenges and future trends in using machine learning with biometric security.
KEYWORDS: Cyber Threats, PIN, Vulnerable, Data manipulation, Machine Learning, Biometric Security, Privacy, Robust.
INTRODUCTION –
The emergent world is characterized by cyber threats that make it dangerous to operate an organization without the right security mechanisms. This is why traditional ways of securing accounts such as using passwords and Personal Identification Number (PINs) are sometimes problematic and not as secure as people would think because such accounts can easily be hacked by anyone with access to a computer system or device connected to the internet. This is where biometric security comes into play as it allows formulating a promising approach to the protection of user’s sensitive data. But like any other system it has its own problems, some of which include issues to do with privacy and also problems with manipulation of data or information. This paper aims to make a distinction on how AI solutions together with biometric security systems can be implemented to improve the security level.
Therefore, analyzing this article, it is possible to gain an understanding of the current situation with biometric security, the challenges encountered, as well as potential further advancements with the help of machine learning integration. In this paper, it will discuss how integrating these two approaches can improve cyber security in the modern society providing a higher level of protection and, therefore, create new opportunities in the field of ID verification and IT protection.
Potential of Machine Learning to Perceive Intensive Structures
Machine learning has enhanced many fields due to its ability of learning from a large data set to make a forecast or even make a decision without originally being pre-programmed to make that decision[1].These complications make the area of application most useful in biometric security since people’s characteristics are greatly diverse. Learning from Data
Machine learning algorithms can thus be defined in terms of one’s capacity to learn from data and thus enhance efficiency. This involves learning through large dataset; in a cycle where the algorithm updates the models and gradually improves on the errors that it generates concerning the forecasts that it is to provide. Regarding to biometric security, this imply that it is possible to train any kinds of ML models with abundant sets of biometric features (such as fingerprints, face images, iris scans) for identifying and differentiating users.
There are several issues classified in identifying the biometric data that dictate its nature, making it a very complicated formula that contains a lot of factors. Machine learning also proves effective in discovering the nuances that would be difficult for the human analysts to identify over this data. For example:
Fingerprint Recognition: So there is a possibility of training of the developed ML algorithms with the minutiae points (the ending of ridges, bifurcation and the other) and the patterns along with the variations which are prone.
Facial Recognition: Some of the facial attributes that could be identified and learnt by an ML model includes; the gap between the eyes, the shape of the, and the facial perimeter. CNN is a type of deep learning algorithm that is widely used to extract features from the facial images and to distinguish one face from the other with great efficiency.
Iris Scanning: ML models parse different features of the iris, the intensity of patterns, structures, and the texture to confirm people’s identities.
Adapting to Variability
There are diverse advantages practiced in machine learning where one of them is that it is well suited for new data or situations[2]. In many biometric systems, there is always a variability that is usually occasioned by other factors like variation of the physical environment, the aging process, and so on. It is possible to retrain the machine learning models with new data and keep the high performance at any stage of biometric traits or conditions changes.
Enhancing Robustness
Another way machine learning improves on biometric systems is on the issue of security specifically in matters related to spoofing or fraud. For example, when it comes to fingerprint detection, the difference between a real fingerprint and a spoof one made from certain material, the ML algorithms can be trained to detect the differences. This specificity is an added security to the systems of biometrics as a means of identification or verification.
Real-World Applications
The application of machine learning in biometric security extends to various real-world scenarios: The application of machine learning in biometric security extends to various real-world scenarios:
Anomaly Detection: Machine learning models integrate to learn how people in the society should behave, and they recognize aberrant behavior that in this case may be a sign of an attempt by the hackers to penetrate the system or hacks.
Feature Extraction: Feature selection can be done automatically using advanced machine learning methodologies and thus, does not require much of a human input and hence is more efficient[3].
The flow chart representing the process involved in how machine learning analyzes biometric security is shown in Figure 1.
Guard against Cyber Attacks
Machine learning will be protecting against many cyber attacks some of the most common attacks are shown below: The following are some of the common cyber attacks that machine learning will be preventing Some of the most common cyber attacks are as follows;
Finally, the integration of the approach of using ML with biometric security systems a lot raises its protection from various types of cyber threats. Here are some key ways in which machine learning contributes to this protection: The following are some of the large areas where machine learning has input in this protection:
Detection of Spoofing Attacks
Spoofing attacks therefore remains as the attacks whereby the attacker has taken the fake samples and submitted it to the Biometric systems or an actual photocopy of the Biometric samples[4]. It is here that the artificial neural networks for the differentiation of the fake and the actual biometric information are trained and it is through the use of these that such distinctions are made.
Techniques:
Liveness Detection: Real-time analysis is attainable in the ML depending on the characteristics of sample origin from the live body, such as the difference between alive and deceased like blood flow in fingerprints or faces’ movements[5].
Texture Analysis: Concerning the biometric like the fingerprint or recognition, ML can distinguish between the fake fingerprint/face or the real skin and fake skin or any other material.
Anomaly Detection
First of all, it is necessary to address the aspect of anomaly detection through which, the non-orthodox patterns which can point at a cyber threat will be identified. Such normal usage behaviour patterns are then integrated at the time of creating these machine learning models; and hence, the developed models can detect the kinds of activity that can be deemed as fraudulent.
Techniques:
Behavioral Analysis: Features are derived based on the user activity profile and some of them can be for instance; when the user opened the particular application in the particular device.
Adaptive Security Measures
Machine learning models are helpful when threats are dynamic because the model will be trained with newer data provided to it. This characteristic is especially valuable in ensuring security because the holders of malicious intent are constantly trying out new tricks.
Multi-Factor Authentication
Altogether biometric data, machine learning may also enhance MFA to add at least the one extra factor, such as passwords or behavioral patterns.
CONCLUSION –
So the combination of machine learning with biometric security will be a huge impact in the ongoing cyber attacks zone where traditional methods such as passwords, PIN are not enough to protect. So by leveraging it we can get the best use of it. There are many challenges which will be faced in biometric security alone which is protected by and addressed by machine learning. Through its power to analyze complex patterns it will be guarding against unauthorized access by using many techniques like anomaly detection, liveness detection, etc. If you explore real life studies and some case studies you will witness real power of machine learning. Particularly for verification and access control machine learning can adapt.
In conclusion, the combo of machine learning with biometric security not only protects against unauthorized access but also create new innovative solutions for new challenges faced. As cyber attacks increase it will also be crucial to safeguarding the data. So this article has provided overview of how to address new challenges, and methods involved to protect against it, and how it analyze complex patterns.
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Cite As
Mounish K.V.S. (2024) Cyber security and Biometrics: Machine Learning Solutions, Insigts2Techinfo, pp.1