Читать книгу Deep Learning Approaches to Cloud Security - Группа авторов - Страница 14

Biometric authentication is a security process that relies on the unique biological characteristics of a person in order to affirm their identity. Biometric verification frameworks compare biometric data with existing original datasets that are stored. Examples of biometric characteristics include iris, palm print, retina, fingerprint, face, and voice signature. In recent years, deep learning-based models have helped accomplish best in class results in machine vision, audio recognition, and natural language processing tasks. These models appear to be a characteristic fit for dealing with the everexpanding size of biometric acknowledgment issues, from phone verification to air terminal security frameworks. Thus, application of machine learning techniques to biometric security arrangements has become a trend [1].

Classification of Biometric Data:

 • Behavioral Biometrics: gestures, vocal recognition, handwritten texts, walking patterns, etc.

 • Physical Biometrics: fingerprints, iris, vein, facial recognition, DNA, etc.

Data science consultants can use machine learning’s capacity to mine, look, and examine huge datasets for improving the execution of security frameworks and their reliability.

In light of its exceptional capacity to recognize people, biometric innovation has quickly become a way to help forestall shams and discovered its place in today’s standard advancements. Consequently, it turns out to be more reliable than the customary validation frameworks that utilize passwords and documents for verification shown in Figure 1.1.

Figure 1.1 Biometric modalities [2].

Physical modalities like fingerprints, voice, faces, veins, iris, hand geometry, and tongue print are unique and provide robust advancements in the field of cyber security [2]. They are useful compared to names, ID numbers, passwords, etc. because they are extraordinary, hard to reproduce, and are more significantly and genuinely bound to the individual.

A computing model which gives on-demand services like information stockpiling, computer power, and infrastructure to associations in the IT industry is termed to be “cloud computing” [3]. Despite the fact that cloud offers a ton of advantages, it slacks in giving security which is an issue for most clients. Cloud clients are hesitant to put classified information up because of looming threats to security.