User Security Research Articles

ALVoyage – An AI Travel Planner

Abstract: The document discusses the ALVoyage AI travel planner, which integrates with Gemini and Weather APIs and utilizes Firebase authentication. ALVoyage offers personalized travel planning by leveraging AI technology to curate itineraries based on user preferences. It seamlessly integrates with the Gemini API to generate informative content and incorporates real-time weather data from the OpenWeatherMap API. Firebase authentication ensures user security and privacy. The document also highlights the benefits and key features of Gemini and OpenWeatherMap APIs. The methodology section outlines the system architecture and components of ALVoyage, including user authentication, UI/UX design, Gemini API integration, Weather API integration, and user reviews. The results and discussion section emphasizes the system's performance, user feedback, and the future scope of the project. The conclusion highlights the potential of AI-powered travel planning apps and the need for further research and development in the field.

A Survey on Graphical Password Strategy Authentication

In an ever-evolving digital realm, ensuring the security and reliability of software applications has become increasingly crucial. The continuous advancement of technology also brings about more complex cyber threats, emphasizing the urgent need to identify and address vulnerabilities. In response to this growing imperative, we introduce "Graphical Password Authenticator: A Novel Approach to Secure User Authentication." The Graphical Password Authenticator reimagines the conventional user authentication process by utilizing graphical elements and innovative techniques. It provides a secure and user-friendly alternative to traditional text-based passwords, offering enhanced protection against unauthorized access. By incorporating graphical images, patterns, or other visual cues, users can create unique and memorable authentication credentials that are difficult for malicious actors to compromise. This approach offers a more intuitive and engaging way for users to access their accounts while significantly reducing the risk of password-related security breaches. The Graphical Password Authenticator system ensures robust security through a combination of graphical recognition and encryption technologies. It is designed to be resistant to common attack methods such as brute force and keyloggers, providing a higher level of protection for user accounts and sensitive information. This novel approach to user authentication fosters a secure and user-centric environment, making it a promising solution for the ever-evolving digital landscape. The Graphical Password Authenticator is a groundbreaking step at the intersection of user authentication and innovation. By redefining the way users prove their identity through memorable graphical elements, this approach empowers users with a more secure and intuitive means of accessing their digital resources. This paradigm shift in authentication methods showcases the potential of technology to enhance user security and create a safer digital future, ultimately bridging the gap between user convenience and robust cybersecurity. Keywords: Graphical Password Authenticator, User Authentication, Cybersecurity, Digital Security, Encryption Technologies, User-Centric Approach, Brute Force Resistance, Keyloggers Protection.

Evaluation of System Access Security in The Implementation of Multi-Factor Authentication (MFA) in Educational Institutions

Information systems in educational institutions have the potential to be targeted by various parties, especially unauthorized parties, so they require a higher layer of security to protect sensitive data and secure user access. One solution that can be implemented is the implementation of Multi-Factor Authentication (MFA), a security approach that utilizes more than one authentication method to secure access to the system. MFA is a security method that requires more than one way to verify a user's identity when accessing a system, application, or service that aims to increase security by adding an additional layer of protection beyond using a single passphrase or password. This research aims to evaluate the effectiveness and impact of implementing MFA in the context of system access security in educational environments. The research methodology involves surveying system users, statistical analysis, and monitoring access activity to assess the extent to which MFA is successful in reducing security risks and protecting sensitive information. The research results show that the use of MFA in educational institutions can significantly increase system access security. Several obstacles that arise become challenges in solving problems regarding the use of MFA. Therefore, this article also examines recommendations for increasing the implementation of MFA in educational institutions. It is hoped that this research can contribute to the positive impact of MFA in improving system access security in the context of educational institutions by optimizing information technology in protecting data integrity and user access security.

A Research on Graphical Password Strategy Authentication

In an ever-evolving digital realm, ensuring the security and reliability of software applications has become increasingly crucial. The continuous advancement of technology also brings about more complex cyber threats, emphasizing the urgent need to identify and address vulnerabilities. In response to this growing imperative, we introduce "Graphical Password Authenticator: A Novel Approach to Secure User Authentication." The Graphical Password Authenticator reimagines the conventional user authentication process by utilizing graphical elements and innovative techniques. It provides a secure and user-friendly alternative to traditional text-based passwords, offering enhanced protection against unauthorized access. By incorporating graphical images, patterns, or other visual cues, users can create unique and memorable authentication credentials that are difficult for malicious actors to compromise. This approach offers a more intuitive and engaging way for users to access their accounts while significantly reducing the risk of password-related security breaches. The Graphical Password Authenticator system ensures robust security through a combination of graphical recognition and encryption technologies. It is designed to be resistant to common attack methods such as brute force and keyloggers, providing a higher level of protection for user accounts and sensitive information. This novel approach to user authentication fosters a secure and user-centric environment, making it a promising solution for the ever-evolving digital landscape. The Graphical Password Authenticator is a groundbreaking step at the intersection of user authentication and innovation. By redefining the way users prove their identity through memorable graphical elements, this approach empowers users with a more secure and intuitive means of accessing their digital resources. This paradigm shift in authentication methods showcases the potential of technology to enhance user security and create a safer digital future, ultimately bridging the gap between user convenience and robust cybersecurity.

Verify and trust: A multidimensional survey of zero-trust security in the age of IoT

The zero-trust (ZT) model assumes that all users, devices, and network traffic should not considered as trusted until proven. The Zero-trust model emphasizes the importance of verifying and authenticating every user and device, and limiting access to resources based on the principle of least privilege. Under the principle of the zero-trust model, devices are granted access after they have been successfully presented with their authentication credentials and access rights based on different factors, such as user identity, device health, location, and behaviour. Access controls are then continuously evaluated and updated as user properties, locations and behaviour change. The zero-trust model can be applied in various domains (healthcare, manufacturing, financial services, government etc.) to provide a comprehensive approach to cybersecurity that helps organizations to reduce risk and protect critical assets. This paper aims to provide a comprehensive and in-depth analysis of the zero-trust model, its principles, and its applications, as well as to propose recommendations for organizations looking to adopt this approach. We explore the major components of the zero-trust framework and their integration across different practical domains. Finally, we provide insightful discussions on open research issues within the zero-trust model in terms of the security and privacy of users and devices. This paper should help researchers and practitioners understand the importance of a zero-trust framework and adopt the zero-trust model for effective security, privacy, and resilience of their networks.

HealthSquare: A Survey of Deep Learning Based Healthcare Platform

The multi-health diagnosis web application HealthSquare represents a pioneering endeavor in healthcare technology by integrating advanced AI models for comprehensive health analysis. The system comprises multiple components, including two image processing models for fracture detection from X-ray reports and brain tumor identification from MRI scans. Additionally, GCP’s Vertex AI is leveraged for blood & thyroid report analysis to predict & provide descriptive diagnosis. Moreover, the platform enables users to create accounts for secure storage of medical reports, akin to a specialized digital locker tailored specifically for healthcare documents. Furthermore, an AI-driven chat bot offers real-time health- related guidance and recommendations to users based on their queries. The integration of these components aims to redefine healthcare diagnostics by providing users with accessible, accurate, and personalized health analysis tools. Utilizing advanced image processing techniques, AI-driven analyses, and secure user accounts, the project strives to revolutionize healthcare by offering a comprehensive and user-centric approach to medical diagnosis and health management. Keywords: AI Integration, Fracture Detection, Brain Tumor Identification, Thyroid Detection, Blood Analysis, Secure Medical Report Storage, AI Chat Bot.

Socialisation of student youth in social networks

Social networks have become a part of the daily life of students, they influence the development of views, values and identity, which can have an impact on students’ social connections, so research is an urgent task that contributes to understanding this influence. The purpose of the study was to investigate the impact of social networks on various spheres of life of modern students. In order to investigate the issues of socialisation of young people in social networks, the study used generalisation and analytical method. As a result, the importance of developing strategies aimed at strengthening the positive impact of social networks on students was highlighted. It was revealed that changing social norms and creating an atmosphere of open and decent communication in an online environment can help improve students’ socialisation in the virtual world. It was noted that the protection of privacy and the development of critical thinking are important for ensuring the security and information competence of social network users. The importance of developing media literacy and skills in verifying the truthfulness of information in the process of socialisation of students was noted. Strategies that promote the development of virtual space and improve interaction in social networks were outlined. The paper analysed aspects of positive socialisation of students in the digital age that help them solve problems and cope with the challenges of the online environment. The results of scientific research can contribute to a better understanding of how social networks affect young people, and help researchers develop programmes and strategies aimed at supporting the positive aspects of this impact and reducing possible negative consequences

Adapting an artificial intelligence sexually transmitted diseases symptom checker tool for Mpox detection: the HeHealth experience.

Artificial Intelligence (AI) applications have shown promise in the management of pandemics. In response to the global Monkeypox (Mpox) outbreak, the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases (STD) to develop a digital screening test for symptomatic Mpox using AI. Before the global Mpox outbreak, the team developed a smartphone app (HeHealth) where app users can use a smartphone to photograph their own penises to screen for symptomatic STD. The AI model initially used 5000 cases and a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including syphilis, herpes simplex virus, and human papillomavirus. A total of about 22,000 users had downloaded the HeHealth app, and ~21,000 images were analysed using HeHealth AI technology. We then used formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool. A total of 1000 Mpox-related images had been used to train the Mpox symptom checker tool. Based on an internal validation, our digital symptom checker tool showed specificity of 87% and sensitivity of 90% for symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalisability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritising pragmatism, as well as the concept that 'big data' in fact is made up of 'small data'.

A Comprehensive Ad-Blocking and VPN Solution for Secure, Responsible and Ethical Online Browsing

A Comprehensive Ad-Blocking and VPN Solution for Secure, Responsible and Ethical Online Browsing

Meta-Learning for Multi-Family Android Malware Classification

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users’ devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious applications that exploit unknown vulnerabilities) classification problem. In this paper, we introduce an innovative meta -learning approach for m ulti-family A ndroid m alware c lassification named Meta-MAMC , which uses meta-learning technology to learn meta-knowledge (i.e. the similarities and differences among different malware families) of few-family samples and combines new sampling algorithms to solve the above challenges. Meta-MAMC integrates (i) the meta-knowledge contained within the dataset to guide models in learning to identify unknown malware, and (ii) more accurate and diverse tasks based on novel sampling strategies, as well as directly adapting meta-learning to a new few-sample and zero-sample task to classify families. We have evaluated Meta-MAMC on two popular datasets and a corpus of real-world Android applications. The results demonstrate its efficacy in accurately classifying malicious applications belonging to certain malware families, even achieving 100% classification in some families.

SMART SANITARY NAPKINS VENDING MACHINE

Our project, the Smart Sanitary Napkins vending machine, is an innovative solution that combines the functionalities of dispensing sanitary pads and medicines while incorporating advanced features for user convenience. Utilizing Arduino, LCD display, keypad, RFID technology, DC motor, and Node MCU, we've developed a robust system that enhances accessibility and usability. At its core, our vending machine boasts a user-friendly interface, featuring an LCD display and keypad for seamless interaction. Users can easily navigate through the menu to select and purchase either sanitary pads or medicines according to their specific needs. The integration of RFID technology ensures secure user authentication, granting access only to authorized individuals. The dispensing mechanism is driven by a precise DC motor, guaranteeing accurate and controlled distribution of sanitary pads and medicines. Additionally, we've incorporated a disposal unit to promote environmental sustainability by facilitating proper disposal of used sanitary pads. Keywords--- Arduino, LCD display, keypad, RFID technology, DC motor,

Efficient and secure privacy protection scheme and consensus mechanism in MEC enabled e-commerce consortium blockchain

The application of blockchain technology to the field of e-commerce has solved many dilemmas, such as low transparency of transactions, hidden risks of data security and high payment costs. Mobile edge computing(MEC) can provide computational power for blockchain, and can meet the demand for high real-time and low latency in e-commerce transaction systems. However, there are still some constraints in the MEC enabled e-commerce consortium blockchain, such as the leakage of user privacy information, low security of consensus algorithm and other security issues. In this paper, we propose a secure transaction model suitable for MEC enabled e-commerce consortium blockchain, aiming to ensure the efficiency of system transaction processing while improving the security of users’ privacy information and transaction data. The model adopts the lightweight Paillier encryption algorithm to protect the security of user privacy information and transaction data to prevent the leakage of user privacy information, and optimizes the security of leader election phase of Raft consensus algorithm by introducing the shamir secret sharing protocol to improve the anti-Byzantine failure capabilities of Raft consensus algorithm. The effectiveness of the scheme proposed in this paper is demonstrated by experimental simulations.

A Usable Encryption Solution for File-Based Geospatial Data within a Database File System

Developing a security solution for spatial files within today’s enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in “data silos” of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution’s usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS.

Active Chat Monitoring and Suspicious Chat Detection Over Internet

In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat monitoring techniques for the detection of suspicious behavior over the internet. By leveraging advancements in natural language processing (NLP), machine learning, and data analytics, this study aims to explore effective methodologies for real-time monitoring and analysis of chat conversations to identify potentially harmful content. The paper will delve into various approaches for detecting suspicious patterns, including keyword analysis, sentiment analysis, and anomaly detection algorithms. Additionally, considerations for privacy, ethics, and legal implications surrounding chat monitoring will be discussed. Ultimately, this research endeavors to contribute to the enhancement of online safety measures by providing insights into the design and implementation of proactive monitoring systems capable of identifying and mitigating risks in virtual communication spaces. Keywords— Active Chat Monitoring, cybersecurity, user experience, digital era, security, user-controlled, effectiveness, evolving threat, Chat content analysis, Threat intelligence.

Secure Text Transfer Using Cloud Environment

Cloud computing has become an integral part of the digital world, offering cost effective and flexible features for data storage, processing, and transfer. However, security concerns have become a major challenge with the increased reliance on cloud computing. In particular, secure text transfer in the cloud is a crucial aspect of cloud computing, as it involves the transmission of sensitive information. This research paper proposes a cloud-based secure text transfer mechanism that addresses the security challenges of text transfer in the cloud environment. The suggested technique guarantees the confidentiality, integrity, and availability of data during transmission, and provides a high level of security for cloud users. We present a comprehensive analysis of the proposed mechanism and evaluate its performance against various security metrics. The results of our research demonstrate the effectiveness of the proposed mechanism in providing secure text transfer in the cloud environment. Keywords: Cloud Computing, Secure Text Transfer, Confidentiality, Integrity, Availability, Security, Data Transmission, Cloud Users, Security Metrics.

Communication network security situation analysis based on time series data mining technology

AbstractCommunication network security situation analysis prevents unauthorized users from accessing and stealing sensitive information. Network security analysis aims to monitor, discover, and eradicate security flaws by carefully examining the network’s architecture, data, and traffic to ensure safety. In time series data mining analysis by cyber terrorism, specialists must pay attention to cyber security, which involves identifying the elements contributing to long-term trends or systemic patterns via pattern-matching algorithms and other types of inferential processing on large datasets. The challenging characteristics of communication network security situation analysis are data loss, security breaches, hacking, and viruses. Hence, in this research, attention mechanism-based convolutional neural network-enabled practical byzantine fault tolerant (AMBCNN-PBFT) has been designed to improve communication network security situation analysis in time series data mining. AMBCNN-PBFT helps to increase communication network security usage and support the expansion during the evaluation system by optimizing the time series data mining. AMBCNN-PBFT effectively predicts the rise in the communication network, associated with faster times series benefits data mining approach. The study concludes that the AMBCNN-PBFT efficiently indicates and validates the communication network security in time series data mining during the evaluation system. The experimental analysis of AMBCNN-PBFT outperforms the data mining time series in terms of accuracy, efficiency, performance, and prediction.

Module lattice based post quantum secure blockchain empowered vehicle to vehicle communication in the internet of vehicles

The importance of vehicle-to-vehicle (V2V) communication is increasing due to high rate of accidents and advancements in information exchange system. The V2V communication facilitates vehicles to exchange real-time informations about their surrounding, such as nearby vehicle, pedestrian, and road hazard. These type of informations are very helpful to detect potential collision, warn driver of imminent danger, and take advanced measure to avoid accident. Both security and privacy of user participating in the vehicular networking system depends upon reliable data transmission system. The suggested approaches must have minimal processing complexity and be unaffected by latency problems due to the scalability of V2V connection. This paper contains module lattice based quantum safe blockchain empowered authentication framework for autonomous vehicle communication system. This framework uses blockchain that is always contains multiple nodes, then we need an aggregation technique to provide authenticity to the system. The proposed framework provides authenticity to the system based on two assumptions, (1) Module Learning With Error and (2) Module Short Integer Solution. The proposed framework is quantum safe, provides high latency and throughput.

PellucidAttachment: Protecting Users From Attacks via E-Mail Attachments

Malicious email attachments are a common and successful attack vector on today's Internet. Sophisticated at- tackers can craft highly-targeted attachments, using publicly available information about potential victims to create convincing documents that contain hidden malicious payloads. Users who open these attachments using vulnerable applications are at a high risk of infection. Unfortunately, current mitigations are unreliable, relying either on fallible malware detection techniques or user education. In this work, we propose adopting a default policy of isolated attachment rendering. Emails bearing attachments are transpar- ently rewritten (in a sandboxed virtual machine environment) to contain static renderings of the attachments. Users who wish to obtain the original attachment are explicitly warned of the dan- gers of doing so – akin to TLS warnings as used in web browsers before being allowed to access the requested documents. We implement this technique in a system we call PellucidAttachment. We further report on an extensive user study that measures the usability and effectiveness of PellucidAttachment in shielding users from attacks. Our evaluation shows that adopting email attachment security indicators and an isolation-by-default policy results in a significant increase in user security, while maintaining the usability of email attachments.

Social Media and Social Interaction: Adversities and Advantages with Special Reference to Cyber Bullying

The present research entitled, “Social media and social interaction: Adversities and advantages with special reference to cyber bullying” investigates the effects of social media on social interaction among individuals in contemporary society with the rise of social media platforms such as whatsapp, facebook, instagram, and snapchat etc., There has been a significant shift in interaction and communication. The present research investigates the positive and negative effects of social media on interpersonal relationship, communication pattern and social behaviour. A descriptive as well as analytical design with a semi-structured questionnaire is utilised to understand the influence of social media usage on social interaction and communications at Manzini town in Eswatini. The respondents using social media expresses the positive impact of using social media like development of social contacts, increased IQ level and encouraging worldwide interaction with people and on the other hand those who highlighted negative impact view social media as waste of time, money and limits face-to-face interaction and communication skills, furthering cyber bullying and addiction. The challenge against privacy and security of the social media user leads to harassment or cyberbullying. The respondents recognise the effectiveness of intervention aimed at promoting safety and responsible social media. The present research provides a valuable insight into the benefits and drawbacks of social media encouraging researchers and policy makers to conduct further studies in developing strategies to mitigate the risks and promote healthy social media use.

Development design of an IoT-based smart home monitoring system with security features

<div style="text-align: start;" align="center"><p>A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry.</p></div>