PIN Codes Research Articles

Regional Crime Data Analysis and Insights Using Fb Prophet

Crime is one of the most dominate and formidable aspect of the society. Crimes are committed daily, which has made the lives of the common people restive. Therefore, preventing the crime from occurring can be achieved through a systematic approach by recognizing and evaluating the criminal trends specific to a certain location. Data analysis can assist us to explore former criminal incidents and help us analyze the patterns and hidden correlations which can be used for prevention of crime. Our system aims to analyze crime-prone regions by using the location and time of the crime. Data visualization is a powerful tool for investigating crime rates across the country. Heat maps can be useful for determining the hotspots of crime. The proposed system analyzes the data using the FBprophet model to forecast crime rates in different regions. The Facebook Prophet library, maintained by Facebook, provides forecasting of time series data. This application uses past data and web-scraped data from various sites in order to get detailed studies of various crimes in multiple places. It also enables you to get data about crimes in many places across India by using the pin codes. Government organizations and police departments can effectively utilize it to monitor crime events within a locality.

MORSE CODE-BASED AUTHENTICATION SYSTEM

To tackle analytically complex issues, data science is a multidisciplinary combination of data inference, algorithm development, and technology. Nearly every industry, including business, finance, healthcare, and education, uses data science to manage massive volumes of data. Practical uses include text prediction, image processing, identity recognition, audio processing for speech, stock movement, and cancer prediction. Given that the majority of individuals on the planet have issues with security and authentication, For those who use Morse Code for self-authentication, we can offer real-time eye tracing for password authentication. As we all know, the 21st century has seen significant support for advancements in authorization and authentication technology. Since the late 1990s, personal identification numbers, or PINs, have been extensively used for user authentication and security. These days, it is easy to crack PIN codes, so we would rather use a different strategy. On the other hand, PIN authentication using hands-off gaze-based PIN entry techniques offers a more secure password entry option because it leaves no physical traces behind. Key Words: Morse code, Face recognition, Pin Entry, Eye Blinks, Gaze based

Phish Catcher: Client-Side Defense against Web-Spoofing Attacks Using Machine Learning

Cybersecurity faces a significant challenge in safeguarding users' confidential information, such as passwords and PIN codes, from phishing attacks. These attacks, which employ various deceptive tactics like fake login pages, phishing emails, and click-jacking, aim to trick users into divulging sensitive data. Traditional security strategies often encounter issues of latency and accuracy in detecting these fraudulent activities. To address this challenge, we propose a client-side defense mechanism leveraging machine learning techniques to detect spoofed web pages and protect users from phishing attacks. In this work, we introduce Phish Catcher, a Google Chrome extension developed as a proof of concept for our machine learning-based approach. Phish Catcher utilizes a random forest classifier trained on four types of web features to classify URLs as suspicious or trustworthy. To evaluate the effectiveness of our extension, we conducted experiments on real web applications, testing 400 classified phishing URLs and 400 legitimate URLs. The results demonstrate a remarkable accuracy and precision of 98.5% for detecting spoofed web pages. Furthermore, we assessed the latency of Phish Catcher by measuring its response time over forty phished URLs. The average recorded response time was just 62.5 milliseconds, indicating minimal impact on user experience while providing robust protection against phishing attacks. Overall, our approach offers a highly accurate and efficient solution for detecting phishing attempts, thereby enhancing user security and mitigating the risks associated with online fraud.

WONK: keeping the edge in the era of artificial intelligence

Learning outcomes After completion of the case study, students will learn to use Lean Canvas to identify business opportunity. They will also learn the balancing of exploitation of profit-producing activities and exploring new opportunities according to the environmental dynamism. Case overview/synopsis WONK, a tutor discovery and booking app was launched by MyEdge in 2016 to search and book verified tutors in locations served by the company. Based on their requirements, parents and students could sort and book verified tutors in their area. Through the app, users could search for academic and hobby classes in the form of individual tuitions. The ease of use and the service offering made it a popular app with students enrolling every 6 min. Within a span of six years, WONK had provided services to thousands of students in 20+ countries and had 200,000+ tutors registered on their app from 15,000+ pin codes. Despite a plethora of Edtech companies in India, a different business model and services offered gave them an edge over other Edtech companies. To keep up with the customer needs, they were constantly making the upgrades to their technology and expanding their services. Vidhu Goyal, the founder of the company, was enjoying the progress when another development in the technology hit the world. With the launch of applications based on artificial intelligence, will it disrupt the business or not? Complexity academic level The case study is recommended to be taught in a 90-min class to Master of Business Administration students. The case study may be used in courses related to strategy, information systems management and entrepreneurship. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Authentication in a Hyperconnected World: Challenges, Opportunities and Approaches

Authentication and integrity are the prerequisites for trustworthy and secure communication. Without unambiguous knowledge of who is being interacted with, no confidential content can be exchanged, no (remote) access to systems and equipment can be granted, and no trust can be established. This situation is further exacerbated by an increasing interconnection and globalization towards a hyperconnected world. (Communication) Participants are no longer necessarily in close physical and social proximity and do not need to know each other, but can have their source/destination anywhere in the world.
 An authentication process is used to verify that someone -whether human or machine-, is in fact who she claims to be: The process thus includes a validation step to evaluate an assertion. However, systems differ in terms of their requirements, for instance with regard to the authentication options available, the time period required for re-authentication and the frequency of re-authentication, as well as the level of security to be achieved with authentication. The latter particularly with regard to the cost/benefit ratio of the application. Additionally, there are efforts to finally abolish traditional passwords, passphrases and pin codes and render them obsolete.
 In this work, technologies and methods for authentication beyond passwords and trustworthy authentication will be examined, particularly with regard to future communication infrastructures such as Beyond 5G and Sixth Generation (6G) wireless systems. Thereby, the impact of Artificial Intelligence (AI) methods, but also the relevance to Quantum Key Distribution (QKD) and Post Quantum Cryptography, as well as the use of 6G-enabling technologies like Reconfigurable Intelligent Surfaces (RISs), Wireless Optical Communication (WOC) and Physical Layer Security (PhySec), for example as additional factors of a multi-factor authentication process, will be considered, along with Body Area Networks (BANs) and the integration of the human body relying on biometrics. The various concepts are compared with regard to their requirements, limitations and possible applications in order to provide the user with an orientation as to which authentication method is conceivable and useful in which specific scenarios.

Phish Catcher: Client-Side Defense against Web-Spoofing Attacks Using Machine Learning

Cybersecurity faces a significant challenge in safeguarding users' confidential information, such as passwords and PIN codes, from phishing attacks. These attacks, which employ various deceptive tactics like fake login pages, phishing emails, and click-jacking, aim to trick users into divulging sensitive data. Traditional security strategies often encounter issues of latency and accuracy in detecting these fraudulent activities. To address this challenge, we propose a client-side defense mechanism leveraging machine learning techniques to detect spoofed web pages and protect users from phishing attacks. In this work, we introduce Phish Catcher, a Google Chrome extension developed as a proof of concept for our machine learning-based approach. Phish Catcher utilizes a random forest classifier trained on four types of web features to classify URLs as suspicious or trustworthy. To evaluate the effectiveness of our extension, we conducted experiments on real web applications, testing 400 classified phishing URLs and 400 legitimate URLs. The results demonstrate a remarkable accuracy and precision of 98.5% for detecting spoofed web pages. Furthermore, we assessed the latency of Phish Catcher by measuring its response time over forty phished URLs. The average recorded response time was just 62.5 milliseconds, indicating minimal impact on user experience while providing robust protection against phishing attacks. Overall, our approach offers a highly accurate and efficient solution for detecting phishing attempts, thereby enhancing user security and mitigating the risks associated with online fraud.

Sound-based Two-factor Authentication: Vulnerabilities and Redesign

Reducing the level of user effort involved in traditional two-factor authentication (TFA) constitutes an important research topic. An interesting representative approach, Sound-Proof , leverages ambient sounds to detect the proximity between the second-factor device (phone) and the login terminal (browser), and it eliminates the need for the user to transfer PIN codes. In this article, we identify a weakness of the Sound-Proof system that makes it completely vulnerable to passive “environment guessing” and active “environment manipulating” remote attackers and proximity attackers. Addressing these security issues, we propose Listening-Watch , a new TFA mechanism based on a wearable device (watch/bracelet) and active browser-generated random speech sounds. As the user attempts to log in, the browser populates a short random code encoded into speech, and the login succeeds if the watch’s audio recording contains this code (decoded using speech recognition ) and is similar enough to the browser’s audio recording. The remote attacker, who has guessed/manipulated the user’s environment, will be defeated, since authentication success relies upon the presence of the random code in watch’s recordings. The proximity attacker will also be defeated unless it is extremely close (<50 cm) to the watch, since the wearable microphones are usually designed to capture only nearby sounds (e.g., voice commands).

Orgatma – the 3PL challenge

Learning outcomes This case study would help students in understanding the dynamics of logistics and logistics vendor roles and contributions to overall business operations. The case study covers real-time information for applying the theoretical knowledge students gain related to the selection of logistics vendor. It would help students to understand and evaluate the dynamics of a new start-up related to cost, profits and dependency; understand and analyze the importance of third-party logistics (3PL) service providers in the supply chain; become aware of the key performance indicators (KPIs) important in the selection of logistics vendor; and develop and create measures for selecting logistics vendors on the basis of KPIs. Case overview/synopsis This case study was about an innovative start-up operating in the field of organic edible oils. The company catered to end consumers with its indigenous technology and processes. The innovative and healthy products were appreciated by the consumers, as was reflected in the surging demand figures. With the increasing popularity of organic products, the orders were surging. At the same time, issues such as damaged product delivery, increased cost per delivery of small packages and failure to deliver because of unserved pin codes by their logistics partners were being faced by the company. The case discusses the dilemma faced by the protagonist regarding the selection of the right 3PL partner. The case study is suitable for teaching courses in operations and logistics, supply chain management and entrepreneurship-related courses. Complexity academic level This case study is appropriate for postgraduate courses in entrepreneurship, operations management, logistics and supply chain management and general management. Supplementary materials Teaching notes are available for educators only. Subject code CSS9: Operations and logistics.

Deep learning-based models’ application to generating a cryptographic key from a face image

Generating cryptographic keys, such as passwords or pin codes, involves utilizing specialized algorithms that rely on complex mathematical transformations. These keys necessitate secure storage measures and complex distribution and processing mechanisms, which often incur substantial costs. However, an alternative approach emerges, proposing the generation of cryptographic keys based on the user's biometric data. Since one can generate keys "on the fly," there is no longer a requirement for key storage or distribution. These generated keys, derived from biometric information, can be effectively employed for biometric authentication, offering numerous advantages. Additionally, this alternative approach unlocks new possibilities for constructing information infrastructure. By utilizing biometric keys, the initiation of cryptographic algorithms like encryption and digital signatures becomes more streamlined and less burdensome in storing and processing procedures. This paper explores biometric key generation technologies, focusing on applying deep learning models. In particular, we employ convolutional neural networks to extract significant biometric features from human face images as the foundation for subsequent key generation processes. A comprehensive analysis involves extensive experimentation with various deep-learning models. We achieve remarkable results by optimizing the algorithm's parameters, with the False Reject Rate (FRR) and False Acceptance Rate (FAR) approximately equal and less than 10%. With code-based cryptographic extractors’ post-quantum level of security, we ensure the continued protection and integrity of sensitive information within the cryptographic framework.

ONLINE BANKING SECURITY WITH REAL TIME FACE RECOGNITION APPROACH

Online banking has become an essential part of modern banking. Many attacks are successful in accessing social network accounts since the current password-based authentication paradigms are not efficient and robust enough as well as vulnerable to automated attacks. The simplest alternative is complementing the single factor (password-based) authentication process with additional identification elements, such as one-time PIN codes, generated by the user’s own device (e.g., the smartphone) or received via SMS. To improve the security of online banking transactions, real-time face recognition technology can be used as a biometric authentication technique. This technology provides a reliable and convenient way of verifying the identity of customers in real-time. The aim of this project is to develop an online banking system that uses real-time face recognition technology for customer authentication. The system will be designed to provide a secure and user-friendly interface that allows customers to carry out banking transactions such as funds transfer, bill payments, and balance inquiries. The system will incorporate a Grassmann Learning algorithm that is capable of capturing and analyzing a customer's facial features in real-time. The algorithm will compare the facial features of the customer with those in the bank's database to verify the customer's identity. Second level verification based on OTP verification in reverse order. The system will provide a secure and user-friendly interface for customers to carry out banking transactions in real-time. In this proposed net banking application notifications are sending to the user regarding banking interface access and amount transaction.

Brightness Based Password Safeguard System with Face Recognition in ATM

the importance of security in the authentication process as well as the increase in threat level posed by such malware has attracted many researchers to the field. Many attacks are successful in accessing social network accounts since the current password-based authentication paradigms are not efficient and robust enough as well as vulnerable to automated attacks. The traditional two-factor authentication mechanisms are not applicable to online social networks because physical token or biometric data cannot be easily used to log into users’ profiles. The simplest alternative is complementing the single factor (password-based) authentication process with additional identification elements, such as one-time PIN codes, generated by the user’s own device (e.g. the smart phone) or received via SMS. Proposed a brightness based authentication mechanism (i.e., Bright Pass) capable of enhancing the security of identity confirmation PIN codes without asking the user to memorize an additional secret value or to solve a complex cognitive task. This method introduces a new input value that is changed at every usage combining a something you know element (i.e., the PIN) with an interface element that cannot be captured by spyware, i.e., a bright or dark circle displayed on the phone screen to tell the user when to digit the correct PIN digit and when to digit a fake one. It prevents the malware from correctly inserting the PIN code, thereby disallowing the possibility to perform critical operations without the user’s agreement. Proposed work also focuses on implementing secure face recognition approach for user authentication. This approach will enhance the performance of ATM system

RWD10 Measuring Patient Satisfaction From Patient Reported Outcomes (PRO) Application Embedded in the Hospital's Treatment Pathway in Finland

Patient Reported Outcomes (PROs) are valuable in monitoring patients’ real-time wellbeing, but the data collection is only viable if patients feel satisfied to actively answer the digital PRO questionnaires. Patient satisfaction on the applications’ technical solution is evident to the response rate of this appreciated real-world data (RWD). It can be especially challenging to use digital solutions embedded in the hospital’s treatment pathway, when they require strong authentication, pin codes and patients ID. The important patient privacy aspect can lead to possible challenges in the collection of PRO data from less digitally capable patients. The objective of this study was to understand how satisfied the patients are with the current solution.

Quantum Pin Codes

We introduce quantum pin codes: a class of quantum CSS codes. Quantum pin codes are a generalization of quantum color codes and Reed-Muller codes and share a lot of their structure and properties. Pin codes have gauge operators, an unfolding procedure and their stabilizers form so-called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> -orthogonal spaces meaning that the joint overlap between any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> stabilizer elements is always even. This last feature makes them interesting for devising magic-state distillation protocols, for instance by using puncturing techniques. We study examples of these codes and their properties.

Importance of Data Biometric in the Organisational Culture

Biometric data is a technology that users or workers use and saves a lot of time. This technology helps people to keep things, data, and information more safe. There are several types of biometric such as fingerprints scan, iris scan, palm scan, face scans and many more. Users no longer need to remember passwords or pin codes because of this technology. Users just have to scan their fingers or palm, face or eyes to unlock devices and this system is way more secure than any other systems. Though it is a high cost system, organisations can trust on this system because no one can easily hack this technology as every person has different characteristics. Passwords, pin codes, pattern locks can be easily hacked but hacking someone’s fingerprints or iris scan is quite impossible. Still hackers can hack this technology sometimes. Mainly voice commands get hacked easily because anyone can record a user's voice without consent. In that case, users have to be more careful about personal things and should not share single information with anyone

Mobile behavioral biometrics for passive authentication

• We present a first benchmark of HuMIdb, a publicly available HCI database. • We evaluate touchscreen and background sensor data for mobile passive authentication. • We train a recurrent neural network with triplet loss for each individual modality. • A multimodal system is achieved through the fusion of modalities at score level. • Our results show the suitability of behavioral biometrics for mobile authentication. Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, 1 1 https://github.com/BiDAlab/HuMIdb one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval.

PROPOSAL AND SUGGESTIONS FOR KICK STARTING ECONOMIC ACTIVITY POST COVID 19 &amp; CREATING BALANCED DEVELOPMENT AND EMPLOYMENT OPPORTUNITIES ACROSS THE NATION&amp; AVOIDINGLARGE SCALE MIGRATION OF LABOUR IN INDIA - A RESEARCH PAPER LINKING INFRASTRUCTURE&amp; BALANCED ECONOMIC DEVELOPMENT

THIS PAPER FOCUSSES ON how we should quickly utilise the highways and other infrastructure created for the economic growth of the nation post covid 19, and get a proper return on THE investment already DONE In these AREAS.IT ALSO EXAMINES WHETHER BEYOND A POINT, Does creation of an extensive HIGHWAY / infrastructure network drive the economy AND IF it createS LONG TERM EMPLOYMENT, or are there better ways of doing the same. Items that are discussed more specifically are as under: 1. Leveraging current /existing developmentin the form of infrastructureto kick start further growth on an all India basis. 2. Promoting decongestion of cities from a health perspective&amp; reduce migration across India. 3. Creating location based tax benefits to spur development and create new demand.to overcome the economic slowdown ,by using the already well done highway infrastructure 4. Creation of a modified tax administration system based on PIN Codes and tracking infrastructure growth and returns through PIN codes. The paper takes the liberty of proposing multiple but closely related issues as listed above that should be possibly considered by the Government for further improvement and mainly swift improvement of our country.

DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding.

The onboarding of IoT devices by authorized users constitutes both a challenge and a necessity in a world, where the number of IoT devices and the tampering attacks against them continuously increase. Commonly used onboarding techniques today include the use of QR codes, pin codes, or serial numbers. These techniques typically do not protect against unauthorized device access-a QR code is physically printed on the device, while a pin code may be included in the device packaging. As a result, any entity that has physical access to a device can onboard it onto their network and, potentially, tamper it (e.g., install malware on the device). To address this problem, in this paper, we present a framework, called Deep Learning-based Watermarking for authorized IoT onboarding (DLWIoT), featuring a robust and fully automated image watermarking scheme based on deep neural networks. DLWIoT embeds user credentials into carrier images (e.g., QR codes printed on IoT devices), thus enables IoT onboarding only by authorized users. Our experimental results demonstrate the feasibility of DLWIoT, indicating that authorized users can onboard IoT devices with DLWIoT within 2.5-3sec.

DATA EXTRACTION FROM PASSWORD PROTECTED MOBILE PHONE BY USING CHIP-OFF METHOD –A FORENSIC CASE STUDY

The current digital era is full of digital devices and to ensure the safety of their data, users utilize the protective armor of passwords using the fingerprints lock, face lock, pin codes and password locks. The trial-and-error method possesses an infinite possibility of passwords in patterns/ pin locks for unlocking the devices. The levels of security ensure that only a limited number of trials are possible before any device blocks itself or makes unlocking harder. In these circumstances, especially when a victim is a deceased person (e. g. an abetment in suicide case), the password is practically impossible to attain through the authorities. However, the data can be retrieved with the unique and distinctive method of Chip-off analysis. In this study, a password protected mobile phone was retrieved from a deceased individual and only the analysis of his/her mobile could lead to investigative insights for catching the perpetrator. The Chip-off method has several difficulties and a password/ pin protected device increases the difficulties for analysis several folds. This study highlights the significance of chip-off analysis in achieving accurate importing and extraction of maximum data along with the use of the hardware/software, MSAB, XRY and other software.

Оценка риска нарушения кибербезопасности в коммерческом банке (на примере атак на банкоматы “Brute force” и “Black box”)

Abstract. During the XX–XXI century there was a development of technologies, which resulted in the creation of a global financial system that allows you to quickly make money transactions in opposite points of the Earth. The progress of digital transformation of society and, in particular, financial and economic systems leads to the complication of the problems of information security of competing entities. By focusing on scientific research, we can achieve success in these areas. The purpose of the study: to increase the level of security of banking services for individuals and legal entities in accordance with the recommendations of information security standards by analyzing the risk of information security violations in electronic banking technologies (on the example of the “Brute force” and “Black box” attacks). Research methods: empirical methods of scientific knowledge (observation, measurement, experiment), theoretical methods (analysis, synthesis, induction, deduction, abstraction, formalization), graphical interpretation of information, probability theory methods and computer programming. The result of the study: standards for effective management of information security management at the enterprise are considered. The advantage of social engineering methods over the “Brute force” method of PIN codes is shown quantitatively. The time characteristics of its commission and protective measures against attacks of the “Black box” type are analyzed. A method for improving the effectiveness of the response and protection of ATMS from attacks of the “Black box” type is proposed.

On Optimality of CSS Codes for Transversal T

In order to perform universal fault-tolerant quantum computation, one needs to implement a logical non-Clifford gate. Consequently, it is important to understand codes that implement such gates transversally. In this paper, we adopt an algebraic approach to characterize all stabilizer codes for which transversal $T$ and $T^{-1}$ gates preserve the codespace. Our Heisenberg perspective reduces this to a finite geometry problem that translates to the design of certain classical codes. We prove three corollaries: (a) For any non-degenerate $[[ n,k,d ]]$ stabilizer code supporting a physical transversal $T$, there exists an $[[ n,k,d ]]$ CSS code with the same property; (b) Triorthogonal codes are the most general CSS codes that realize logical transversal $T$ via physical transversal $T$; (c) Triorthogonality is necessary for physical transversal $T$ on a CSS code to realize the logical identity. The main tool we use is a recent efficient characterization of certain diagonal gates in the Clifford hierarchy (arXiv:1902.04022). We refer to these gates as Quadratic Form Diagonal (QFD) gates. Our framework generalizes all existing code constructions that realize logical gates via transversal $T$. We provide several examples and briefly discuss connections to decreasing monomial codes, pin codes, generalized triorthogonality and quasitransversality. We partially extend these results towards characterizing all stabilizer codes that support transversal $\pi/2^{\ell}$ $Z$-rotations. In particular, using Ax's theorem on residue weights of polynomials, we provide an alternate characterization of logical gates induced by transversal $\pi/2^{\ell}$ $Z$-rotations on a family of quantum Reed-Muller codes. We also briefly discuss a general approach to analyze QFD gates that might lead to a characterization of all stabilizer codes that support any given physical transversal $1$- or $2$-local diagonal gate.