Confidentiality Research Articles

RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system.

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.

War crimes, sexual assault and medical confidentiality in Israel

Hamas militants have raped and mutilated the bodies of dozens of men and women in Israel during their attack and captivity in Gaza. The exact extent of these atrocities, however,...

A network security protection scheme for tax system based on elliptic curve cryptography

With the rapid development of modern information technology and network technology, the construction of tax informatization plays an important role in improving the level of tax management and work efficiency. However, in the centralized data processing mode, the establishment of large data center and the efficient, stable, and secure connection of server bandwidth have become key challenges. Moreover, the openness and interconnectivity of the tax system network pose various network security threats, seriously threatening the integrity and security of data. For the above issues, this article proposes a tax system network security protection scheme based on elliptic curve cryptography. This scheme combines hash function and elliptic curve cryptography to achieve user anonymity and secure generation of session keys, effectively ensuring the secure transmission of confidential information. The security and effectiveness of the scheme are verified through informal and formal security analysis based on security proof, BAN logic and AVISPA tool. And a comprehensive evaluation of the scheme is conducted, and the results show that the scheme significantly reduced the costs while providing security features.

Jurisprudential Rulings Relevant to the Use of Artificial Intelligence in the Medical Field

*This paper of research is sponsored and funded by Kuwait University, Research Project No. (HC03/23) Idea of the Research is to address the topic of using artificial intelligence in the medical field from the Islamic point of view. Its importance lies in focusing on the way Islamic Law keeps pace with latest issues that comply with the provisions and objectives of Islamic Law. Hence, the problematic deals with jurisprudential rulings relevant to the use of AI in the medical field and providing patients’ confidential data to AI software. This research aims to explain some jurisprudential rulings on using AI. Accordingly, extrapolatory, analytical, and comparative methods have been used. The main findings of the research are the following: learning AI is a collective duty provided that it agrees with the Objectives of Islamic Law that firmly stipulates non-disclosure of patients’ medical data except in a few cases to bring benefit or prevent harm. On this basis, practitioners can provide AI software with confidential patients’ information under certain conditions such as achieving public interest, obtaining permission and a written consent from the patient, revealing true information and preventing any kind of harm. In this regard, the researcher urges to keep pace with the latest AI developments and encourages more jurisprudential studies to promote research on emerging medical issues.

Research by the method of an extended systematical literature review E-SLR the problem of ensuring the security of personal data when using OSINT

Objective. The amount of personal data in open sources increases, which makes it possible for third parties to access it using open source intelligence (OSINT) methods, which can be used for malicious purposes. The aim of the work is to identify threats and existing methods and means of ensuring the security of a user's personal data and his reputation when using OSINT by intruders, as well as to identify the main problems in protecting user PD taking into account OSINT. Method. The study uses an extended method of systematic literature review (e-SLR), which is a systematic literature review (SLR) supplemented by responses from ChatGPT, GigaCHAT, YndexGPT neural networks. Result. 41 sources were received for the analysis of the problem, on the basis of which threats to personal data were identified: violation of the confidentiality of personal data and the operation of information systems, targeted attacks using social engineering, password disclosure, espionage; protection tools: data processing before publication, anonymization and depersonalization, limitation of personal data, selection of sites, protection using OSINT, creation of complex passwords, use of protection tools, organizational measures; problems in the development of protection tools: working with big data, unreliability of information and sources, labor-intensiveness of data analysis, technical limitations, bias, ethical and legal aspects. Conclusion. The results were used to develop models for protecting personal data in open sources, methods and means for detecting and preventing violations of their security.

Multiple levels of crypto-fiduciary security unforgeable stamp

The paper introduces the concept of a multi-level crypto-fiduciary security system, focusing on the development and implementation of an unforgeable stamp mechanism. This security architecture integrates cryptographic techniques with fiduciary principles to create a robust authentication framework that ensures data integrity, confidentiality, and trust in transactions. The unforgeable stamp serves as a cryptographic seal that is tamper-proof, traceable, and resistant to counterfeiting or duplication, providing enhanced protection against fraud and unauthorized access. The proposed system leverages multiple layers of encryption, key management, and blockchain technology to achieve a secure and verifiable method of transaction validation. This work aims to address critical security challenges in financial, governmental, and other sensitive applications, where trust and data authenticity are paramount.

Combining Pretty Good Privacy and Role-Based Access Control Technologies for Access Protection to Confidential Data

Combining Pretty Good Privacy and Role-Based Access Control Technologies for Access Protection to Confidential Data

Efficient security interface for high-performance Ceph storage systems

Ceph portrays a resilient clustered storage solution with supporting object, block, and file storage capabilities with no single point of failure. Despite these qualifications, data confidentiality defines a concern in the system, as authentication and access control are the only data protection security services in Ceph. CephArmor was proposed as a third-party security interface to protect data confidentiality by adding an extra protection layer to data at rest. Despite the added layer, the initial design of the API needed to be more efficient in addressing security and performance simultaneously. In this study, we propose a new architectural design to address the associated issues with the preliminary prototype. Comprehensive performance and security analysis verify the improvement of the proposed method compared to the initial approach. The benchmark result has indicated a 37% improvement on average in IOPS, elapsed time, and bandwidth for the write benchmark compared to the initial model.

SIMULASI JARINGAN VPN ROUTER DENGAN METODE LINK STATE ROUTING PROTOCOL DI SMK KORPRI MAJALENGKA

At this time the progress of information technology is growing so rapidly. The internet is a product of advances in information technology that can be accessed by anyone and anywhere. The increasingly sophisticated information technology helps a lot in human life today, but not a few also have a negative impact on the individual and group levels. Therefore, it is necessary to have a data and information security system. SMK Korpri Majalengka is an education provider institution in Majalengka Regency that has not carried out a data communication security system that is not optimal, such as confidential data that can still be accessed by just anyone. The agency needs a local network that has a wide range, cannot be accessed by just anyone, but only people who have access rights are connected to the local network so that agency security can be maintained properly. To solve this problem, a Virtual Private Network (VPN) network is needed. Therefore, the author intends to answer the above problems by simulating the "VPN Router Network with the Link State Routing Protocol Method" with the aim of maintaining the data and information security system at SMK Korpri Majalengka Regency.

Cyber Fraud, Causes and Its Effect on Money Deposit Banks

The study examined cyber fraud, causes and its effect on money deposit banks (DMB) in Nigeria: a study of Zenith Bank. The main objective is to examine electronic frauds, causes and its challenges in the banking sector. The study is hinged on two hypotheses; 49 branches; sample of 557 staff. Structured questionnaire was used to capture the response of the bank staff and it was analyzed using statistical package for social science version 27 (SPSS). Descriptive statistics and multiple regression were employed in the data analysis. Among the findings were accounting fraud by bank staff, identity theft, money laundering, phishing, pharming, computer virus, lack of oversight function,lack of standards, financial loss, absence of national central control, lack of credible infrastructure, porous internet, lack of national functional database, and inadequate awareness by bank customers were the challenges militating the effort towards curbing the cyber fraud in the banking system in Nigeria. The findings also revealed that cybercrime reduced productivity; vulnerability of banks Information and Communication Technology (ICT) systems and security audit. The study recommends adequate antivirus and antimalware software technology and the use of multi-factor authentication, use of biometrics, automatic logout are the solutions identified to curb cybercrimes in banks. The study concluded that the place of security in the cyber space of banks in Nigeria cannot be oversemphasied, therefore necessary measures should be put in place towards mitigating cybercrime in Zenith Bank. Equally, Zenith Bank should employ stringent measures to monitor staff activities especially in the confidentiality of customer information, cyber security audit should be done on regular basis, sensitization of bank customers, and Multifactor authentication, biometrics and automatic log out, and strong firewall should be adopted by Zenith Bank.

БЕЗОПАСНОСТЬ ИНФОРМАЦИОННОЙ СФЕРЫ В УСЛОВИЯХ ГЛОБАЛИЗАЦИИ

The article examines the problematic issues of ensuring personal information security, which are an important aspect in the modern world, where information plays a key role. The article specifies the components of personal information security, in particular, the protection of confidential data, personal information and information related to a person from unauthorized access, use, disclosure, violation, modification, verification or destruction.

Hybridized Data Encoding based Encryption and Diffie Hellman Decryption for Security Enhancement

Information security is extremely important because the modification and interception of the data can lead to loss of data integrity, confidentiality, and availability. The encryption process is a critical component of classic standard encryption techniques such as advanced encryption, standard block ciphers, and data encryption standards. The traditional algorithm requires increased power consumption and memory. Therefore, there is a need to develop an efficient cryptographic operation to promote better security. The encryption process is performed using the Hybridized Data Encoding (HDE) model. Here, the optimal keys can be selected using the Chaotic Coati optimization algorithm (ChCoA). The decryption process can be performed using the Enhanced Diffie Hellman algorithm (EDHA). The resulting achievements will include effective encryption and decryption algorithms that together strengthen data security, facilitate secure data exchange, and provide superior protection against data vulnerabilities. By implementing the proposed work in the Matlab working platform, the performances in terms of encryption time, decryption time, processing time and throughput are analyzed. The proposed model achieved a throughput of 249.1093 (kb/s) and a processing time of 0.55 (sec).

Secured energy data transaction for prosumers under diverse cyberattack scenarios

Due to the increasing use of renewable energy sources and the advancement of smart grid technology, bilateral energy transactions between prosumers have attracted significant interest as a potential solution for efficient and decentralized energy distribution. Prosumers can establish direct energy exchanges by utilizing internet of things (IoT) technologies and arrangements with smart metering capabilities, eliminating the need for middlemen and allowing for more effective use of renewable energy sources. However, these direct energy exchanges between prosumers can be susceptible to cyber-threats, which hinder secure and effective energy transactions while protecting privacy. To enable safe and seamless energy transactions among prosumers and the grid, the cyber-security of IoT devices should be of paramount significance as a possible solution. Therefore, this paper focuses on securing the energy transactions among prosumers facilitated by smart meters. It aims to address potential threats against data integrity, confidentiality, and availability from the prosumers’ point of view and develop a comprehensive framework for securing energy transactions based on artificial intelligence (AI). The proposed structured roadmap not only identifies compromised trading data but also prevents prosumers from reacting to it by replacing the contaminated as well as missing trading data. A comparative analysis on AI-based algorithms indicates that decision tree (DT) outperforms support vector machine (SVM) and multi-layer perceptron (MLP) for the proposed framework to profile the corrupted trading data identification and categorization in order to provide effective outcomes. Additionally, the proposed framework adopts a deep learning (DL)-based model for the replacement of compromised trading data. All the numerical analyses, along with extensive simulation results, justify, the efficacy of the proposed framework.

Revolutionizing Cloud Security: A Novel Framework for Enhanced Data Protection in Transmission and Migration

This research introduces a novel security framework specifically tailored to enhance data protection during cloud transmission and migration. Our study addresses critical gaps in existing security models by proposing a multi-dimensional system that incorporates advanced encryption techniques, dynamic access control, and continuous security auditing. Notably, this framework excels in ensuring cloud data integrity, confidentiality, and availability—core aspects often compromised under conventional methods. Comparative analysis with existing models in simulated cloud environments reveals that our framework significantly enhances threat detection accuracy, response speed, and resource management efficiency. The findings highlight the system’s capability to reduce security vulnerabilities while optimizing operational overhead, presenting a substantial improvement over traditional security solutions. This innovative approach, marked by improved scalability and flexibility, is poised to revolutionize cloud data security practices across various industries, prompting further research into robust cloud computing security methodologies.

Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

Real-time phishing URL detection framework using knowledge distilled ELECTRA

The rise of cyber threats, particularly URL-based phishing attacks, has tarnished the digital age despite its unparalleled access to information. These attacks often deceive users into disclosing confidential information by redirecting them to fraudulent websites. Existing browser-based methods, predominantly relying on blacklist approaches, have failed to effectively detect phishing attacks. To counteract this issue, we propose a novel system that integrates a deep learning model with a user-centric Chrome browser extension to detect and alert users about potential phishing URLs instantly. Our approach introduces a Knowledge Distilled ELECTRA model for URL detection and achieves remarkable performance metrics of 99.74% accuracy and a 99.43% F1-score on a diverse dataset of 450,176 URLs. Coupled with the browser extension, our system provides real-time feedback, empowering users to make informed decisions about the websites they visit. Additionally, we incorporate a user feedback loop for continuous model enhancement. This work sets a precedent by offering a seamless, robust, and efficient solution to mitigate phishing threats for internet users.

Provenance-based APT campaigns detection via masked graph representation learning

Advanced Persistent Threats (APTs) are well-planned, persistent, and highly stealthy cyberattacks designed to steal confidential information or disrupt specific target systems. Recent studies have used system audit logs to construct provenance graphs that describe system interactions to detect potentially malicious activities. Although they are effective, they still suffer from problems such as the need for a priori knowledge, lack of attack data, and high computational overhead that limit their application. In this paper, we propose a self-supervised learning-based APT detection model, APT-MGL, which learns the embedded representations of nodes through a graph mask self-encoder and transforms the detection problem into an outlier detection problem for malicious nodes. APT-MGL characterizes the behavior of nodes based on node type, action, and interaction frequency, and fuses the features through a multi-head self-attention mechanism. Then the node embedding is obtained by combining graph features and structural information using masked graph representation learning. Finally, the unsupervised outlier detection method is used to analyze the computed embeddings and obtain the final detection results. The experimental results show that APT-MGL outperforms existing monitoring models and achieves a small overhead.

Advancing the deployment and information management of direct air capture: A solution enabled by integrating consortium blockchain system

Direct air capture (DAC) is a critical and emerging Negative Emissions Technology (NET) that directly removes CO2 from the atmosphere, significantly contributing to climate change. However, the deployment and management of large-scale DAC faces challenges such as collections and analysis of energy consumption data, intricate device and system management, emission prediction and operation strategy, precise carbon footprint tracking, etc. This paper proposes the integration of blockchain technology with DAC systems to address these challenges, utilizing blockchain's inherent properties of immutability, security, and transparency. The implementation strategy includes the development of a DAC consortium blockchain system, leveraging a consensus mechanism11Consensus mechanism: The programming and process used in blockchain systems to achieve distributed agreement about the ledger's state or the state of a data set. (Source: Investopedia), ECDSA encryption22Elliptic Curve Digital Signature Algorithm (ECDSA): In cryptography, the Elliptic Curve Digital Signature Algorithm (ECDSA) offers a variant of the Digital Signature Algorithm (DSA) which uses elliptic-curve cryptography. As with elliptic-curve cryptography in general, the bit size of the private key believed to be needed for ECDSA is about twice the size of the security level, in bits. (Source: Wikipedia), IoT33Internet of things (IoT): describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communication networks. (Source: Wikipedia) integration, and digital signatures. Preliminary modeling of the proposed system suggests potential improvements in operational efficiency and a reduction in data inaccuracies. The proposed system underscores the system's ability to streamline identity verification, improve data collection accuracy, and facilitate secure, confidential information sharing among DAC stakeholders. By enhancing the efficiency and reliability of DAC operations, this approach supports the scalable and effective deployment of NETs in the global effort to combat climate change. Future research will focus on empirical validation through pilot projects and simulations to further substantiate these claims.

A traceable and revocable decentralized attribute-based encryption scheme with fully hidden access policy for cloud-based smart healthcare

Smart healthcare is an emerging technology for enabling interaction between patients and medical personnel, medical institutions, and medical devices utilizing advanced Internet of Things (IoT) technologies. It has attracted significant attention from researchers because of the convenience of storing and sharing electronic medical records (EMRs) in the cloud. Given that a patient’s EMR contains sensitive individual information, it must be encrypted before uploading it to the cloud. As a countermeasure for data confidentiality and fine-grained access control, the Ciphertext Policy Attribute-Based Encryption (CP-ABE) technique is proposed, which helps manipulate private personal data without explicit authorization. However, most CP-ABE schemes use a centralized mechanism which may lead to performance bottlenecks and single-point-of-failure issues. They will also be at risk of key abuse and privacy breaches in smart healthcare applications. To this end, in this paper, we investigate a traceable and revocable decentralized attribute-based encryption scheme with a fully hidden access policy (TR-HP-DABE). Firstly, to overcome the issues of user privacy leakage and single-point-of-failure, a fully hidden access policy is established for multiple attribute authorities. Secondly, to prevent key abuse, the proposed TR-HP-DABE can achieve the tracking and revocation of malicious users by using Key Encryption Key (KEK) trees and updating the partial ciphertext. Furthermore, the online/offline encryption and verifiable outsourced decryption are applied to improve its efficiency in practical smart healthcare. According to our analysis, the security and traceability of TR-HP-DABE can be proved. Finally, the performance evaluation of TR-HP-DABE is more effective than some existing typical ones.

Implementing a Secure Cloud-Based System to Safeguard Sensitive Medical Data for Healthcare

In most developed countries, the medical healthcare system is experiencing rapid development from the stage of clinical information to the stage of information dissemination. In all of these countries, it is undeniable that the Internet of Medical Things (IoMT) technologies have contributed in order to develop information medical healthcare. In reality, the development of smart medical healthcare has been hindered by the protection of medical privacy, according to research and acceptance. This is especially true as telecommunications systems continue to expand and wireless sensor networks (WSN) develop, as well as ways to penetrate those checks that have become increasingly difficult. In the smart healthcare system, protecting users’ information remains an outstanding issue. IoMT features and the protection of privacy and security have led to the development of an extended privacy homomorphism algorithm based on scrambling matrixes, an encryption algorithm enhanced by Modified RSA (mRSA), and a method of encrypted data compression that ensures data confidentiality. For the above purpose, we built a prototype system on a demo temporary domain using both hardware and software. According to the results, the proposed scheme protects E-healthcare from potential threats by providing stakeholders with a secure interface and preventing unauthorized users from accessing the mCloud, thus ensuring privacy. E-healthcare services based on cloud technology are protected by the proposed scheme because it is simple and robust.