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Social Media, Data Privacy, and the Internet of People, Things and Services in the Workplace
Published in Claire A. Simmers, Murugan Anandarajan, The Internet of People, Things and Services, 2018
Kimberly W. O’Connor, Gordon B. Schmidt
Collecting data from wearable technology presents many potential risks for both employee and employer. For example, researchers at Symantec, a California information-management company, recently confirmed that hackers can easily track the location of many health monitors. Low-cost wearables are also often not encrypted (Austen, 2015). Encryption provides a layer of cybersecurity whereby data are translated into secret codes that require a password in order to decrypt the text (Beal, n.d.). Without encryption, data from wearables, such as the user’s name, address, telephone number, and date of birth, are highly susceptible to hackers. Yet even if a particular wearable is encrypted, the smart device that links to it may not be. Again, this is a weak point that hackers can target, just as they did when Fitbit user accounts were hacked in 2016 (Austen, 2015; Ahmed, 2016).
Smart Grid Technologies
Published in Stuart Borlase, Smart Grids, 2017
Cybersecurity is a term that relates to technologies, processes, and measures taken to protect data, communications networks, information technologies, and computing systems against unauthorized access or attack. One of the most problematic elements of cybersecurity is the quickly and constantly evolving nature of security risks. The traditional approach has been to focus most resources on the most crucial system components and protect against the biggest known threats, which necessitated leaving some less important system components undefended and some less dangerous risks not protected against. Such an approach is insufficient in the current environment. To deal with the current environment, advisory organizations are promoting a more proactive and adaptive approach. The National Institute of Standards and Technology (NIST) in the United States, for example, recently issued updated guidelines in its risk assessment framework that recommended a shift toward continuous monitoring and real-time assessments [1].
Cybersecurity Incident Response in the Enterprise
Published in Mohiuddin Ahmed, Nour Moustafa, Abu Barkat, Paul Haskell-Dowland, Next-Generation Enterprise Security and Governance, 2022
Nickson M. Karie, Leslie F. Sikos
Many types of cybersecurity incidents exist, the common ones include: Malware attacks (e.g., ransomware), illegal encryption of data, phishing attacks delivered mostly via emails, insider threats (e.g., theft of data and trade secrets), unauthorized access to systems or data, illegal deletion or corruption of data, privilege escalation attacks, password attacks, eavesdropping attacks, man-in-the-middle (MITM) attacks, brute-force and dictionary network attacks, denial-of-service (DoS) attacks leading to disruption of services, AI-powered attacks, web application attacks, cyber-fraud or theft (e.g., illegal financial transfer), advanced persistent threat (APT), side-channel attacks, and botnet attacks.
Evaluating Industry 4.0 technology and sustainable development goals – a social perspective
Published in International Journal of Production Research, 2023
Chunguang Bai, Hua Zhou, Joseph Sarkis
Cybersecurity refers to protecting information from being stolen, compromised or attacked, especially through the use of preventative methods. This I4.0T provides people with a safe working environment. As an example, the application of 5G mobile communication technology will help governments to better locate rare animals, plants or marine life in nature reserves, collect biodiversity information and better protect the natural ecological environment (Zhu et al. 2021). Big Data and Analytics () and Cloud Computing () have an interactive relationship. Big Data and Analytics can store and process a large amount of data collected from the information collection I4.0T and can cooperate with Cloud Computing to clean and compute data. These data can greatly support Responsible consumption and production () located at level 4 of the hierarchical structure.
A Review on Application of GANs in Cybersecurity Domain
Published in IETE Technical Review, 2022
Cybersecurity is a set of rules and procedures used to safeguard the cyber-space which includes hardware, software, private networks and data, from cyber attacks. The different types of attacks are Phishing, Man in Middle, Denial of Service (DOS), and use of malware/virus-based software. There is a tremendous increase in cyber threats in the last few years due to the rise in inter-connected Internet of Things (IoT) and the potential and massive volumes of data generated by devices, sensors, applications, and websites used in cloud-based services. The security systems are unable to detect and prevent hacks that are now becoming progressively complicated and highly mischievous. The deep learning methods are widely used in the cybersecurity domain [1, 2] to tackle these difficulties.