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Threats, Security and Safety of Cyber-Physical Systems in Construction Industry
Published in Salah Wesam Alaloul, Cyber-Physical Systems in the Construction Sector, 2022
Khalid Mhmoud Alzubi, Wesam Salah Alaloul, Abdul Hannan Qureshi
Due to the massive amount of information and data that is shared in open channels and publicly in the construction industry, encryption is a need for in construction CPSs. Encryption provides the ability to securely transfer data through these networks by avoiding sensitive information to be observed by malicious attackers and unauthorised access as well as hiding the original data. Also shared keys are a technique used within the context of encryption to provide the right keys to decode the encrypted information to the involved persons. In this way, information between stakeholders, sensors, and systems will have the right keys to decode encrypted data.
Cyber Threats to Farming Automation
Published in Utku Kose, V. B. Surya Prasath, M. Rubaiyat Hossain Mondal, Prajoy Podder, Subrato Bharati, Artificial Intelligence and Smart Agriculture Technology, 2022
Confidentiality means that sensitive information should be available only to legitimate individuals and systems. A wide variety of techniques and tools are used by attackers to get access to the sensitive data of users. One of the earliest suggested approaches for preventing this is with the use of encryption techniques to preserve user data so that even if the attacker gains access to one's data, it cannot be deciphered. The Advanced Encryption Standard (AES) and Data Encryption Standard (DES) are seen as the leading encryption standards in the market (Figure 13.2).
Natural Language Processing in Data Analytics
Published in Jay Liebowitz, Data Analytics and AI, 2020
Though unstructured data has become a tremendous source of untapped value for businesses, it doesn’t easily lend itself to older models of data storage and analytical processes. The following introduces two of the many challenges around unstructured data today. Quality. The value of data requires a high-quality data source. Unstructured data comes from various sources, in various formats, and with various uses. For example, comments gathered through online forums or social media sites may have issues of reliability in that some comments are simply not facts but made-up information by users. The data may also present inconsistency during a certain time which makes it hard to be trusted and used. Business goals should be set before data is collected and analyzed, but the amount of data that is gathered does not necessarily guarantee the relevancy or completeness. It may only be relevant to one but not all aspects of the goal. The deficiency of one component may impact the use, accuracy, and integrity of the data. As unstructured data, readability is a persisting issue. Transforming it to a clean and usable format is not a standardized process and always presents as the first challenge in analyzing it.Volume. Large volumes of data continue to be generated every second, and it offers different kinds of value to different people in different ways. Building an infrastructure that is able to continuously scale to the ever-growing data has become a challenge faced by many organizations today. For many businesses, due to the lack of techniques and tools for managing and analyzing the amount of data they collect, the data is simply lying around and consuming storage capacity without adding any value. And even worse, if no management watches over what’s stored, the organizations may end up losing track of what data they have and what’s in the data. Some data may contain sensitive information such as credit card information, social security numbers, or other personally identifiable information. Without encryption, data lays bare and vulnerable, which will raise data privacy and security issues, such as identity theft, financial resources theft, and fraud, and therefore will present huge risks to organizations.
Subspace-based aggregation for enhancing utility, information measures, and cluster identification in privacy preserved data mining on high-dimensional continuous data
Published in International Journal of Computers and Applications, 2022
Shashidhar Virupaksha, D. Venkatesulu
Data mining involves exploration of large data to extract knowledge which may be patterns or rules. In the last few decades, organizations have been using it effectively. Privacy becomes a major consideration when data are sent for mining. In various countries, governments have passed legislations to ensure the privacy of data. In US legislations such as total information awareness program ADVICE (Analysis, Dissemination, Visualization, Insight and Semantic Enhancement) have been passed. Other policies such as HIPPA (Health Insurance Portability and Accountability Act) and E.U. data protection directive make it mandatory that data released for data mining has to be protected from loss of sensitive information [1,2]. Hence in the last two decades, the field of privacy preservation in data mining (PPDM) has emerged which anonymizes data for data mining. Aggregation is a popular privacy preservation technique that protects sensitive information from identity disclosure or attributes disclosure ([3,4] and PPPCA [5]).
Phishing: message appraisal and the exploration of fear and self-confidence
Published in Behaviour & Information Technology, 2020
Perceived susceptibility and perceived threat cause fear and motivate action (Green and Witte 2006). When the perceived threat is elevated, fear is also elevated (Witte 1992; Witte 1998; Witte and Allen 2000). Future harms are what constitute the threat (not present danger) (Lazarus 1966) as is the case with phishing attacks. Phishing attacks are an indication of anticipation that sensitive information will be gathered and identity theft or access to funds will be gained. When harm is seen as highly threatening and perceived as occurring in the near future, the threat has the highest impact (Lazarus 1966). Individuals can foresee a threat and have an anticipatory reaction such as if they determine a threat is severe, they will have an increased fear arousal (Bandura 1977b). As noted by Connor and Norman (1995), when both threat susceptibility and severity are high, fear arousal will be engaged. Thus, hypotheses 6 and 7 state: H6: Perceived threat severity will have a positive influence on fear arousal.H7: Perceived threat susceptibility will have a positive influence on fear arousal.
Trust aware cryptographic role based access control scheme for secure cloud data storage
Published in Automatika, 2023
K. Roslin Dayana, P. Shobha Rani
In [29], virtual resource management approaches are presented for a cloud environment. These methodologies involve building a RBAC policy, which lowers the possibility of data being exposed to unauthorized parties. These approaches contribute to maintaining the confidentiality of sensitive information. In multi-tenant data centres, the idea of sensitivity is utilized when discussing the degree to which individual tenants share their data with one another. It is generally accepted that data centres with low levels of information sharing have low levels of sensitivity, whereas data centres with high levels of information sharing are generally accepted to have high levels of sensitivity.