Sensitive Information Research Articles

Phishing Website Detection

Abstract: Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Our findings show that the model based on the K means clustering is the most accurate of the other four techniques and outperforms other solutions in the literature.

A Fake Products Identification by Using Blockchain

Abstract: Counterfeiting of products is a significant challenge across various industries, leading to economic losses and risks to consumer safety. This paper proposes a novel approach to combat counterfeit products using blockchain technology. By leveraging the immutable and transparent nature of blockchain, a framework is devised to authenticate products throughout their lifecycle. The system utilizes unique identifiers assigned to each product during manufacturing, which are then recorded on a blockchain ledger. Through a decentralized network of nodes, stakeholders including manufacturers, distributors, retailers, and consumers can verify the authenticity of products in real-time. Smart contracts are employed to automate verification processes, ensuring integrity and efficiency. Additionally, the framework incorporates mechanisms for data privacy protection, ensuring sensitive information remains secure. Through empirical analysis and simulations, the effectiveness and scalability of the proposed solution are evaluated. Results demonstrate a significant reduction in counterfeit incidents and enhanced trust among stakeholders. This research contributes to the advancement of anti-counterfeiting measures, offering a robust solution to safeguard product authenticity in the digital age.

Detection of Phishing Websites by Using URL Analysis

Abstract: In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyse the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.

Ethical keylogger solution for monitoring user activities in cybersecurity networks

This paper presents the development of a software keylogger to monitor user activities within a cyber network, enhancing security and facilitating comprehensive cybersecurity analysis. Guided by the agile methodology, the work adopts a flexible and iterative approach to development, ensuring continuous collaboration, adaptation to evolving requirements, and swift response to feedback. Leveraging the versatility and efficiency of the Python programming language, this work transforms the traditional notion of a keylogger, which is a program that records keystrokes, into an ethical and legally compliant tool. By capturing and logging keystrokes, the system provides valuable insights into user behavior and potential security threats, enabling proactive measures to prevent cyber-attacks and data breaches. Prioritizing transparency and consent, the system design adheres to ethical standards and privacy regulations, ensuring the protection of sensitive information and user privacy. This technical solution offers cybersecurity professionals, system administrators, and organizations a valuable tool for network monitoring while showcasing responsible development practices and contributing to the advancement of ethical cybersecurity solutions.

Virtual Assistance for Banking System using AI and ML

Virtual assistance has emerged as a transformative technology in the realm of banking, offering an efficient and user-friendly means of engaging with financial services. This abstract explores the concept of a virtual assistant for the banking system, a sophisticated digital entity designed to facilitate and enhance customer interactions within the financial sector. A groundbreaking solution to empower blind individuals in navigating the complex landscape of banking operations. Rooted in the ethos of inclusivity, the project merges cutting-edge technologies with a user-friendly interface, ensuring a seamless and independent banking experience for visually impaired users. The frontend of the application is developed using Flutter, offering a responsive and visually appealing cross-platform interface that caters to a diverse range of user preferences. Meanwhile, the Python backend orchestrates the intricate ballet of banking operations, communicating securely with banking APIs to perform tasks such as balance inquiries, fund transfers, and transaction history retrieval. Central to the project's innovation is the integration of speech-to-text and text-to-speech functionalities. Users can interact with the application through spoken commands, which are accurately transcribed into text, and receive information through synthesized speech, providing a natural and conversational experience. A sophisticated chat box further facilitates communication, enabling users to seek assistance and guidance effortlessly. The text-based conversation within the chat box becomes a crucial element in ensuring a user-friendly and accessible interface. Security is paramount, with robust measures in place to safeguard sensitive information, employing encryption for data such as login credentials and transaction details

Exploring on Cloud Security Landscapes:A Comprehensive Review

Nowadays, the massive growth of data and the adoption of the cloud-based architecture indicate that the secure storage of data is a very essential issue. Cloud storage is the solution to convenient, scalable and cost-effective storage, yet security issues persist, especially regarding the protection of sensitive information. The encryption plays a vital role in which data is made unreadable and only accessible by those with the proper authorization. This helps in data management and minimizes data leaks. Shared responsibility in cloud security entails collaboration between the operators and users through the encryption strategy and a number of security measures to protect the data, apps, and infrastructure. This paper deals with the different cryptographic techniques and technologies used for improving cloud security. It discusses the notion of Non-Deterministic Cryptographic Scheme (NCS), which is a secure technique that effectively utilizes randomness in cryptographic algorithms. Regarding the Intel architecture compatibility, SGX and blockchain technology implementation not only ensures data immutability, but it also strengthens the data against cyber threats. SGX produces secure compartments inside cloud platforms, while blockchain makes data integrity possible through decentralized and immutable ledger technology. The research gives an impression of the seaside and multi sided cloud security. Involving encryption, access control, data loss prevention, and intrusion detection systems. Technologies of cryptography like Group Key Based Attribute Encryption (GKBAE) and Modified Random Fibonacci Cryptography (MRFC) offers both data protection and reduced resource requirement in clouds. GKBAE enables fine-grained access control and attribute-based encryption that ensures high data security without any additional computation. In addition, the paper also talks about the privacy-preserving technology, and policy-oriented access control framework. Techniques like Linear Elliptical Curve Digital Signature (LECDS) that combine digital signatures with blockchain technologies can also be used to implement the above features. To sum up, the changing scenario of cloud security requires ceaseless innovation and application of the latest cryptographic methods. Through encryption, hardware level security features and distributed ledger technology implementation, data environment is more strengthened and vulnerabilities to hacking, data leakage, and unauthorized access are decreased. Making use of the modern security tools not only does the data safety but also is responsible for the trustworthiness and stability of the cloud computing infrastructure. Keywords— Cloud Security, Encryption, Data Protection, Non-Deterministic Cryptographic Scheme (NCS), Intel SGX, Blockchain, Group Key Based Attribute Encryption (GKBAE), Modified Random Fibonacci Cryptography (MRFC), Data Confidentiality.

Efficient Image Details Preservation of Image Processing Pipeline Based on Two-Stage Tone Mapping

Converting a camera’s RAW image to an RGB format for human perception involves utilizing an imaging pipeline, and a series of processing modules. Existing modules often result in varying degrees of original information loss, which can render the reverse imaging pipeline unable to recover the original RAW image information. To this end, this paper proposes a new, almost reversible image imaging pipeline. Thus, RGB images and RAW images can be effectively converted between each other. Considering the impact of original information loss, this paper introduces a two-stage tone mapping operation (TMO). In the first stage, the RAW image with a linear response is transformed into an RGB color image. In the second stage, color scale mapping corrects the dynamic range of the image suitable for human perception through linear stretching, and reduces the loss of sensitive information to the human eye during the integer process. effectively preserving the original image’s dynamic information. The DCRAW imaging pipeline addresses the problem of high light overflow by directly highlighting cuts. The proposed imaging pipeline constructs an independent highlight processing module, and preserves the highlighted information of the image. The experimental results demonstrate that the two-stage tone mapping operation embedded in the imaging processing pipeline provided in this article ensures that the image output is suitable for human visual system (HVS) perception and retains more original image information.

Development of a Phishing Detection System Using Support Vector Machine

Phishing represents a significant and escalating threat within the cyber domain, inflicting substantial financial losses on internet users annually. This illicit practice leverages both social engineering tactics and technological means to unlawfully obtain sensitive information from individuals online. Despite numerous studies and publications exploring various methodologies to combat phishing, the number of victims continues to surge due to the inefficiencies of current security measures. The inherently anonymous and unregulated nature of the internet further compounds its susceptibility to phishing attacks. While it's commonly believed that successful phishing endeavours involve the creation of replica messages or websites to deceive users, this notion has not undergone systematic examination to identify potential vulnerabilities. This paper endeavours to fill this gap by conducting a comprehensive evaluation of phishing, synthesizing diverse research perspectives and methodologies. It introduces an innovative classification method utilizing Support Vector Machine (SVM), achieving an impressive accuracy rate of 96.4% in detecting phishing attempts. By implementing this model to distinguish between phishing and legitimate URLs, the proposed solution offers a valuable tool for individuals and organizations to promptly identify and mitigate phishing threats. The findings of this study hold significant implications for bolstering internet security measures and enhancing user awareness in navigating potentially malicious online content.

Study and Review of GupShup- A Chat Application

In the digital age, the demand for secure and private messaging platforms has surged, paralleling the exponential growth of online interactions. This research introduces Secure GupShup, a groundbreaking application designed to address the paramount concerns surrounding user privacy and data security. In an environment where information confidentiality is of utmost importance, Secure GupShup stands out by providing a feature-rich experience that includes private and public chat rooms, with a distinctive emphasis on security through the implementation of instant disappearing messages. The core innovation of Secure GupShup lies in its implementation of instant disappearing messages, a feature designed to augment user privacy. By ensuring that messages automatically vanish after a predetermined time, the application adds an extra layer of security, preventing unauthorized access to sensitive information. This not only grants users greater control over their conversations but also fosters an environment where information is transient, mitigating the risks associated with data retention. The research delves into the intricate architecture of Secure GupShup, exploring the amalgamation of cutting-edge encryption protocols and intuitive user interface design. The application's robust security measures, coupled with its user-friendly interface, position Secure GupShup as a formidable contender in the realm of secure communication applications. As the digital landscape continues to evolve, Secure GupShup signifies a pivotal step towards meeting the escalating demands for privacy and security in the realm of digital communication. The incorporation of end-to-end encryption protocols ensures that messages exchanged within Secure GupShup remain private and secure. Users can communicate confidently, knowing that their conversations are shielded from unauthorized access, providing a heightened level of privacy. The inclusion of disappearing messages adds an extra layer of security by automatically removing sensitive content after a designated time period. This feature not only protects user data from long-term exposure but also aligns with the evolving need for ephemeral communication in digital platforms. While acknowledging the current limitations, the proposed solutions aim to address these challenges and enhance the overall functionality of Secure GupShup. The outlined future work envisions continued improvements, embracing emerging technologies and user preferences to maintain Secure GupShup's position as a leading secure communication platform..

Privacy BERT-LSTM: a novel NLP algorithm for sensitive information detection in textual documents

Privacy BERT-LSTM: a novel NLP algorithm for sensitive information detection in textual documents

PDPHE: Personal Data Protection for Trans-Border Transmission Based on Homomorphic Encryption

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing.

Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance

The rapid proliferation of Internet of Things (IoT) devices across various industries has revolutionized the way we interact with technology. However, this widespread adoption has also brought about significant security challenges that must be addressed to ensure the integrity and confidentiality of data transmitted and processed by IoT systems. This survey paper delves into the diverse array of security threats faced by IoT devices and networks, ranging from data breaches and unauthorized access to physical tampering and denial-of-service attacks. By examining the vulnerabilities inherent in IoT ecosystems, we highlight the importance of implementing robust security measures to safeguard sensitive information and ensure the reliable operation of connected devices. Furthermore, we explore cutting-edge technologies such as blockchain, edge computing, and machine learning as potential solutions to enhance the security posture of IoT deployments. Through a comprehensive analysis of existing security frameworks and best practices, this paper aims to provide valuable insights for researchers, practitioners, and policymakers seeking to fortify the resilience of IoT systems in an increasingly interconnected world.

Comprehensive Person Profiling System

The proposed Comprehensive person profiling system aims to use innovative approach to revolutionizing identity verification in financial transactions, particularly within banks or other financial organizations. This project aims to replace traditional national ID usage with a more efficient and secure system. Leveraging advanced technologies such as biometrics, data analytics, and machine learning, the system ensures a comprehensive profiling of individuals, offering a robust alternative to conventional identification methods. By seamlessly integrating into financial institutions, this solution enhances inconveniences of the users’ doing transactions, security, minimizes identity fraud, and streamlines customer authentication processes. The Comprehensive Person Profiling System not only safeguards sensitive information but also enhances the overall efficiency and reliability of financial transactions, marking a significant leap towards a more secure and technologically advanced future in the realm of personal identification.

Beyond Digital Protection(ism)

Abstract What values and conceptualizations of data are being advanced through new data protection regulations in Asia? This article answers this question with a comparative analysis of data protection regulations in China, India, and South Korea. The article identifies four key dimensions of divergence: conceptualizations of personal and sensitive information, cross-border data transfer restrictions, state exceptions, and considerations for platforms. This leads to three conclusions: local notions of “privacy” are animated by complex domestic priorities and geopolitics, data protection regulations contribute to platform governance, and local exceptions to data protection regulations reveal diverse political strategies to manage citizen-state relations.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server’s computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Fostering Privacy in Collaborative Data Sharing via Auto-encoder Latent Space Embedding

Securing privacy in machine learning via collaborative data sharing is essential for organizations seeking to harness collective data while upholding confidentiality. This becomes especially vital when protecting sensitive information across the entire machine learning pipeline, from model training to inference. This paper presents an innovative framework utilizing Representation Learning via autoencoders to generate privacy-preserving embedded data. As a result, organizations can distribute these representations, enhancing the performance of machine learning models in situations where multiple data sources converge for a unified predictive task downstream.

PPG-Hear: A Practical Eavesdropping Attack with Photoplethysmography Sensors

Photoplethysmography (PPG) sensors have become integral components of wearable and portable health devices in the current technological landscape. These sensors offer easy access to heart rate and blood oxygenation, facilitating continuous long-term health monitoring in clinic and non-clinic environments. While people understand that health-related information provided by PPG is private, no existing research has demonstrated that PPG sensors are dangerous devices capable of obtaining sensitive information other than health-related data. This work introduces PPG-Hear, a novel side-channel attack that exploits PPG sensors to intercept nearby audio information covertly. Specifically, PPG-Hear exploits low-frequency PPG measurements to discern and reconstruct human speech emitted from proximate speakers. This technology allows attackers to eavesdrop on sensitive conversations (e.g., audio passwords, business decisions, or intellectual properties) without being noticed. To achieve this non-trivial attack on commodity PPG-enabled devices, we employ differentiation and filtering techniques to mitigate the impact of temperature drift and user-specific gestures. We develop the first PPG-based speech reconstructor, which can identify speech patterns in the PPG spectrogram and establish the correlation between PPG and speech spectrograms. By integrating a MiniRocket-based classifier with a PixelGAN model, PPG-Hear can reconstruct human speech using low-sampling-rate PPG measurements. Through an array of real-world experiments, encompassing common eavesdropping scenarios such as surrounding speakers and the device's own speakers, we show that PPG-Hear can achieve remarkable accuracy of 90% for recognizing human speech, surpassing the current state-of-the-art side-channel eavesdropping attacks using motion sensors operating at equivalent sampling rates (i.e., 50Hz to 500Hz).

Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment

Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Surrogate information transfer and fusion in high-dimensional expensive optimization problems

The Kriging surrogate model is less frequently employed in high-dimensional expensive problems than is the radial basis function (RBF) model. This discrepancy is attributed to the challenge of hyperparameter tuning within the Kriging covariance function, which leads to relatively worse predictive performance. However, the Kriging model still plays a crucial role in providing the predicted uncertainty for new point infill that cannot be replaced by the RBF model. To leverage the advantages of both models, a surrogate information transfer and fusion algorithm is presented. Surrogate information transfer introduces global sensitivity information from the constructed RBF model to the hyperparameter optimization of the Kriging model, reducing the dimensions of hyperparameter tuning and improving its approximation performance. Surrogate information fusion combines the RBF and Kriging models, with the RBF model providing more accurate predictions of unknown points for exploitation, while the Kriging model provides predicted uncertainty for swarm updating and new point infilling to ensure exploration of the solution space. Compared with several state-of-the-art algorithms, the proposed algorithm is evaluated on eight benchmark problems and a in real-world optimization case. The experimental results demonstrate the effectiveness of the surrogate information transfer and fusion methods and the significant superiority of the proposed algorithm over the compared algorithms.