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"Computers Access control Evaluation."
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Increasing Accountability Through User-Interface Design Artifacts
Access-policy violations are a growing problem with substantial costs for organizations. Although training programs and sanctions have been suggested as a means of reducing these violations, evidence shows the problem persists. It is thus imperative to identify additional ways to reduce access-policy violations, especially for systems providing broad access to data. We use accountability theory to develop four user-interface (UI) design artifacts that raise users’ accountability perceptions within systems and in turn decrease access-policy violations. To test our model, we uniquely applied the scenario-based factorial survey method to various graphical manipulations of a records system containing sensitive information at a large organization with over 300 end users who use the system daily. We show that the UI design artifacts corresponding to four submanipulations of accountability can raise accountability and reduce access policy violation intentions. Our findings have several theoretical and practical implications for increasing accountability using UI design. Moreover, we are the first to extend the scenario-based factorial survey method to test design artifacts. This method provides the ability to use more design manipulations and to test with fewer users than is required in traditional experimentation and research on human–computer interaction. We also provide bootstrapping tests of mediation and moderation and demonstrate how to analyze fixed and random effects within the factorial survey method optimally.
Journal Article
HCAC-EHR: hybrid cryptographic access control for secure EHR retrieval in healthcare cloud
Technology that is perfect is free of vulnerability. Technological growth offers users online data storage and access to it from anywhere. Cloud computing is a model that provides data storage on a contract facility and a slew of different services. Today, online data relating to health is inevitably stored and managed. These health records comprise data that includes X-ray images, scanned images, therapy procedures, medical prescriptions, and patient information. Medical professionals use the stored health data for diagnosis, patients for their understanding, and government and insurance companies for further follow-up. Since multiple category of users want access to health data, data needs protection and to be stored with extreme security before being stored online in the form of electronic health records (EHRs) with proper access control mechanisms. To this end to provide secure cloud storage, we propose a novel scheme by implementing a hybrid cryptography algorithm in which we use Improved Key Generation Scheme of RSA (IKGSR) algorithm to encrypt health data and Blowfish algorithm for key encryption. We follow steganography-based access control for key sharing by means of substring indexing and keyword search mechanism to efficiently retrieve the encrypted data. We measure performance evaluation as well as the security of the proposed method and compare with existing hybrid method consider New York State Department of Health dataset. The results clearly confirm that our method provides better security and also retrieves data efficiently.
Journal Article
A Review of Blockchain’s Role in E-Commerce Transactions: Open Challenges, and Future Research Directions
The Internet’s expansion has changed how the services accessed and businesses operate. Blockchain is an innovative technology that emerged after the rise of the Internet. In addition, it maintains transactions on encrypted databases that are distributed among many computer networks, much like digital ledgers for online transactions. This technology has the potential to establish a decentralized marketplace for Internet retailers. Sensitive information, like customer data and financial statements, should be routinely transferred via e-commerce. As a result, the system becomes a prime target for cybercriminals seeking illegal access to data. As e-commerce increases, so does the frequency of hacker attacks that raise concerns about the safety of e-commerce platforms’ databases. Owing to the sensitivity of customer data, employee records, and customer records, organizations must ensure their protection. A data breach not only affects an enterprise’s financial performance but also erodes clients’ confidence in the platform. Currently, e-commerce businesses face numerous challenges, including the security of the e-commerce system, transparency and trust in its effectiveness. A solution to these issues is the application of blockchain technology in the e-commerce industry. Blockchain technology simplifies fraud detection and investigation by recording transactions and accompanying data. Blockchain technology enables transaction tracking by creating a detailed record of all the related data, which can assist in identifying and preventing fraud in the future. Using blockchain cryptocurrency will record the sender’s address, recipient’s address, amount transferred, and timestamp, which creates an immutable and transparent ledger of all transaction data.
Journal Article
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged between different communicating devices. The data should be communicated securely between the communicating devices and therefore, network security is one of the dominant research areas for the current network scenario. Intrusion detection systems (IDSs) are therefore widely used along with other security mechanisms such as firewall and access control. Many research ideas have been proposed pertaining to the IDS using machine learning (ML) techniques, deep learning (DL) techniques, and swarm and evolutionary algorithms (SWEVO). These methods have been tested on the datasets such as DARPA, KDD CUP 99, and NSL-KDD using network features to classify attack types. This paper surveys the intrusion detection problem by considering algorithms from areas such as ML, DL, and SWEVO. The survey is a representative research work carried out in the field of IDS from the year 2008 to 2020. The paper focuses on the methods that have incorporated feature selection in their models for performance evaluation. The paper also discusses the different datasets of IDS and a detailed description of recent dataset CIC IDS-2017. The paper presents applications of IDS with challenges and potential future research directions. The study presented, can serve as a pedestal for research communities and novice researchers in the field of network security for understanding and developing efficient IDS models.
Journal Article
A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
Wi-Fi is a popular wireless technology and is continuously extended to keep pace with requirements such as high throughput, real-time communication, dense networks, or resource and energy efficiency. The IEEE 802.11ax standard, also known as Wi-Fi 6, promises to provide data rates of up to almost 10 Gb/s, lower energy consumption, and higher reliability. Its capabilities go far beyond Wi-Fi 5 (802.11ac) and novel technical concepts have been introduced for this purpose. As such, the Wi-Fi 6 standard includes Multi-User Orthogonal Frequency Division Multiple Access (MU OFDMA), Multi-User Multiple-Input Multiple-Output (MU MIMO), new mechanisms for Spatial Reuse (SR), new mechanisms for power saving, higher-order modulation, and additional minor improvements. In this paper, we provide a survey of Wi-Fi 6. Initially, we provide a compact technological summary of Wi-Fi 5 and its predecessors. Then, we discuss the potential application domains of Wi-Fi 6, which are enabled through its novel features. Subsequently, we explain these features and review the related works in these areas. Finally, performance evaluation tools for Wi-Fi 6 and future roadmaps are discussed.
Journal Article
Preparing Wi-Fi 7 for Healthcare Internet-of-Things
The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual’s health. The continuous monitoring of an individual’s health for disease diagnosis and early detection is an important application of H-IoT. Ambient assisted living (AAL) entails monitoring a patient’s health to ensure their well-being. However, ensuring a limit on transmission delays is an essential requirement of such monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be, that is compliant with the latency requirements for healthcare applications. The scheduler allocates the channel resources for UL transmission taking into consideration the traffic class or access category. The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT applications. Additionally, the performance in terms of fairness and throughput is also superior to state-of-the-art schedulers.
Journal Article
Why deep-learning AIs are so easy to fool
Artificial-intelligence researchers are trying to fix the flaws of neural networks.
Artificial-intelligence researchers are trying to fix the flaws of neural networks.
Journal Article
Comparing virtual reality, desktop-based 3D, and 2D versions of a category learning experiment
Virtual reality (VR) has seen increasing application in cognitive psychology in recent years. There is some debate about the impact of VR on both learning outcomes and on patterns of information access behaviors. In this study we compare performance on a category learning task between three groups: one presented with three-dimensional (3D) stimuli while immersed in the HTC Vive VR system (n = 26), another presented with the same 3D stimuli while using a flat-screen desktop computer (n = 26), and a third presented with a two-dimensional projection of the stimuli on a desktop computer while their eye movements were tracked (n = 8). In the VR and 3D conditions, features of the object to be categorized had to be revealed by rotating the object. In the eye tracking control condition (2D), all object features were visible, and participants’ gaze was tracked as they examined each feature. Over 240 trials we measured accuracy, reaction times, attentional optimization, time spent on feedback, fixation durations, and fixation counts for each participant as they learned to correctly categorize the stimuli. In the VR condition, participants had increased fixation counts compared to the 3D and 2D conditions. Reaction times for the 2D condition were significantly faster and fixation durations were lower compared to the VR and 3D conditions. We found no significant differences in learning accuracy between the VR, 3D, and 2D conditions. We discuss implications for both researchers interested in using VR to study cognition, and VR developers hoping to use non-VR research to guide their designs and applications.
Journal Article
A Systematic Review and Meta-analysis of Interventions to Decrease Cyberbullying Perpetration and Victimization
Evidence suggests that cyberbullying among school-age children is related to problem behaviors and other adverse school performance constructs. As a result, numerous school-based programs have been developed and implemented to decrease cyberbullying perpetration and victimization. Given the extensive literature and variation in program effectiveness, we conducted a comprehensive systematic review and meta-analysis of programs to decrease cyberbullying perpetration and victimization. Our review included published and unpublished literature, utilized modern, transparent, and reproducible methods, and examined confirmatory and exploratory moderating factors. A total of 50 studies and 320 effect sizes spanning 45,371 participants met the review protocol criteria. Results indicated that programs significantly reduced cyberbullying perpetration (g = −0.18, SE = 0.05, 95% CI [−0.28, −0.09]) and victimization (g = −0.13, SE = 0.04, 95% CI [−0.21, −0.05]). Moderator analyses, however, yielded only a few statistically significant findings. We interpret these findings and provide implications for future cyberbullying prevention policy and practice.
Journal Article