Odin aims to develop biometric presentation attack detection technologies to better identify unauthorized user attempts and imposters. There are two categories of biometric identification and recognition solutions: Physical and behavioral. This encoded biometric information is stored in a database and digitally sampled during authentication and verification.
A facial recognition biometric system identifies and verifies a person by extracting and comparing selected facial features from a digital image or a video frame to a face database. For example, an algorithm may analyze the distance between the eyes, the width of the nose, the depth of the eye sockets, the shape of the cheekbones, the length of the jaw line, etc. Readers with a deeper interest in facial recognition may want to explore our full article about facial recognition applications.
Speaker or voice recognition differs from speech recognition in that the former recognizes and identifies a speaker using voice biometrics and the latter analyzes what is being said. Voice biometrics include both physical characteristics, such as the shape of the vocal tract responsible for articulating and controlling speech production, and behavioral characteristics such as pitch, cadence and tone, etc. This voice print is used for identification and authentication of the speaker.
Our previous coverage on natural language processing has explored voice recognition in greater depth. Most fingerprint biometric solutions look for specific features of a fingerprint, such as the ridge line patterns on the finger, the valleys between the ridges, etc. In order to get a fingerprint match for verification or authorization, biometric systems must find a sufficient number of minutiae patterns. This number varies across systems. Such behavioral biometrics are typically used as an additional layer of security, along with other credential or biometric information.
Most physical biometric solutions systems authenticate the user only once and usually at the beginning of an action, such as logging into a device or opening a door. Behavioral biometric technology attempts to fill the gap of authentication in a scenario during an action. In , it merged with Digital Persona, another biometric company, to launch its key biometric solution platform for enterprises called DigitalPersona Composite Authentication. In November , Crossmatch announced its partnership with BehavioSec , a Sweden-based behavioral biometrics company, which powers the behavioral biometric analyses functions of DigitalPersona.
Apart from the enterprise platform, the company also seems to offer a wide range of biometric solutions, most notably, fingerprint scanning and detecting solutions across various business sectors, including finance, government, law enforcement, retail, etc. The video below shows use cases of its various biometric solutions for law enforcement, including the Digital Persona Mobile ID, an app that comes with fingerprint recognition software, which helps store and identify security and agency personnel through their biometric information.
Tygart Technology said it provides video and photographic analysis as well as biometric recognition systems for state and federal government clients in the United States. The company also said it serves the FBI by providing operation and maintenance services for its automated, national fingerprint identification system.
It does so by matching the facial biometric information stored in a centralized watchlist. London-based Onfido is an online digital verification platform for businesses. Among other compliances and clearances, Onfido also uses facial biometrics, as an additional layer of security, to verify individual persons. The video below demonstrates the use of facial biometrics on the Onfido mobile website, which seems to be integrated with a banking platform.
AI in Biometrics and Security – Current Business Applications
The volunteer is asked to verify her identity using two steps. This, the application explains, is to make sure she is not being impersonated. It is not clear from the demo or the website whether voice recognition is also part of the verification process.
Very recently, it launched its onboarding platform on the Salesforce AppExchange. Nano NXT has a false acceptance rate the likelihood of the biometric security system accepting an unauthorized user of 1 in 1.
The ATM is integrated with the EyeLock iris biometric technology, which combined with the phone banking app, recognizes the user and gives them access to withdraw cash. The company says it owns more than 75 patents for its proprietary biometric technology. It also cites various partners that use and resell its biometric solutions. A animated video explains this technology and how the biometric information is stored in the database. Also, since the vein patterns do not change, as do other biometric factors, this process involves only a one-time registration.
As of March 31, , about , PalmSecure devices have been shipped to 60 countries, and more than 70 million people make use of this biometrics device, Fujitsu said. Sweden-based BehavioSec said it uses continuous machine learning to authenticate users based on their behavior patterns, such as pressure, gyroscope, button hit zone, motion, accelerometer, mouse actions, etc.
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Cybersecurity firms are training AI systems to detect malware and viruses with the help of several datasets that include algorithms and codes. Using such data, AI can perform pattern recognition that helps identify malicious behavior in software. Machine learning can analyze path traversals of websites to detect whether a website navigates to malicious domains.
Likewise, AI-based systems can recognize malicious files, like web shell, and preemptively isolate them from the system. AI systems can be trained to analyze micro-behavior of ransomware attacks to recognize ransomware before it encrypts a system. One of the most significant reasons to use AI for cybersecurity is the potential of Natural Language Processing that comes into play. AI-powered systems can automatically collect data for reference by scanning articles, studies and news on cyber threats.
AI systems use Natural Language Processing for selecting useful information from the scanned data. Such information will provide insight into cyber attacks, anomalies, mitigation and prevention strategies. Using the analyzed information, cybersecurity firms can identify timescales, calculate risks, harvest data and make predictions.
Organizations generally use authentication models to secure vital data from unwanted people and intruders.bishop.gazpacho.net/ruled-zithromax-vs-hydroxychloroquine.php
Biometrics is smart, but AI is smarter. Here's why | Artificial
If an employee or business leader with higher authentication privileges is accessing such data remotely, then the system can be compromised using the network. In such cases, traditional authentication models prove to be less agile. Alternatively, using AI for cybersecurity will help create a dynamic, real-time and global authentication framework that alters access privileges based on location or network.
AI systems can use Multi-Factor Authentication for this purpose. With this approach, the system will collect user information to analyze the behavior of the user, application, device, network, data and location. For starters, building and maintaining an AI-based system requires a tremendous amount of resources, such as memory, computing power and data. Since AI systems are trained with data, cybersecurity firms need to feed new datasets of malicious codes and non-malicious codes regularly to help AI learn. Besides, the data used for training needs to be accurate, as inaccurate data will lead to inefficient outcomes.
Therefore, finding and collecting precise datasets can be a tedious and time-consuming task.
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Similar to ethical hackers and cybersecurity experts that use AI for cybersecurity, black hat hackers can use AI to test their own malware. With constant testing, hackers can develop advanced malware or maybe even AI-proof malware strains. Considering the malware risks we face today, one can only imagine how destructive an AI-proof malware could be.
Using the same principles, hackers can develop their own AI system that can outsmart AI-powered cybersecurity systems. Such systems can learn from the existing AI systems and lead to even more advanced cyber attacks. After knowing the limitations, organizations need to understand that AI has a long way to go before it becomes a standalone cybersecurity solution.