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Statistical Trend in Cyber Attacks and Security Measures
Published in Suhel Ahmad Khan, Rajeev Kumar, Omprakash Kaiwartya, Mohammad Faisal, Raees Ahmad Khan, Computational Intelligent Security in Wireless Communications, 2022
Shirisha Kakarla, Deekonda Narsinga Rao, Geeta Kakarla, Srilatha Gorla
Often the root cause of the data breaches is human negligence and due to the lack of expertise and availability of skilled staff to handle sensitive data and security procedures, which contribute to 30% of the overall data breaches [21]. The unawareness of the security policies, procedures to be enforced, and conducting and handling the incident response processes remain the main challenges faced by the data handler or the data owner, which threaten the confidentiality of the sensitive information.
Big Data in Cloud Computing - A Defense Mechanism
Published in Abid Hussain, Garima Tyagi, Sheng-Lung Peng, IoT and AI Technologies for Sustainable Living, 2023
N. Ramachandran, Salini Suresh, Sunitha, V. Suneetha, Neha Tiwari
Cybercriminals can access and transport sensitive data when the cloud service or a connected device is breached. This way of transporting cloud data electronically or physically is data leak. Data breaches in the cloud are occurring due to various aspects, for example, malicious attacks, insider threats, malicious insiders, compromised or stolen credentials, and misconfigurations.
Case Studies
Published in G. K. Awari, Sarvesh V. Warjurkar, Ethics in Information Technology, 2022
G. K. Awari, Sarvesh V. Warjurkar
A data breach occurs when sensitive, confidential, or otherwise protected data is accessed and/or disclosed without authorization. Data breaches may happen in any size of business, from tiny startups to large multinationals. Personal health information (PHI), personally identifiable information (PII), trade secrets, and other private information may be included.
More honour'd in the breach: predicting non-compliant behaviour through individual, situational and habitual factors
Published in Behaviour & Information Technology, 2022
Alex Leering, Lidwien van de Wijngaert, Shahrokh Nikou
In fact, most data breaches appear to be caused by employees (Abawajy 2014). A survey conducted by the Ponemon Institute in 2017 in over 419 organisations in eleven countries showed that human error, i.e. negligent employees or contractors, was the root cause of between 19% and 36% of all data breaches (Ponemon Institute 2017). The total cost of the average data breach amounted to US$ 3.62 million, while compliance failures increased the cost per individual compromised record of a data breach from US$ 141 to US$ 152, according to the Ponemon Institute (2017). In addition, information security breaches also have a negative impact on the reputation of the organisation or company involved (Safa and Ismail 2013). From a practical perspective, it is therefore important to understand why employees do not comply with information security policies.
Development of Large-Scale Farming Based on Explainable Machine Learning for a Sustainable Rural Economy: The Case of Cyber Risk Analysis to Prevent Costly Data Breaches
Published in Applied Artificial Intelligence, 2023
The motivation behind the proposed method of incorporating explainable machine learning techniques into large-scale farming for cyber risk analysis is driven by several key factors: Enhancing Cybersecurity: The primary motivation is to enhance cybersecurity in the agricultural industry. As the industry increasingly adopts advanced technologies and relies on digital systems, the risk of cyberattacks and data breaches becomes more significant. By integrating explainable machine learning techniques into risk analysis, the proposed method aims to identify and mitigate cyber threats effectively, thereby improving the overall cybersecurity posture of farms.Protecting Data and Reputation: Data breaches can have severe consequences, including financial losses, reputational damage, and legal liabilities. The proposed method seeks to protect the data for which organizations are responsible and maintain the confidentiality, availability, and integrity of that data. By identifying potential vulnerabilities and implementing appropriate measures, the method aims to safeguard sensitive information and prevent costly breaches that could impact the reputation and trust of the organization.Promoting Sustainable Rural Economy: A sustainable rural economy relies on the efficient and secure functioning of agricultural operations. By addressing cyber risks and enhancing cybersecurity practices, the proposed method aims to foster a sustainable rural economy. By reducing the potential financial losses and disruptions caused by cyber incidents, farmers and organizations can maintain operational continuity, preserve their economic viability, and contribute to the long-term sustainability of the agricultural sector.Leveraging Explainable Machine Learning: The use of explainable machine learning techniques is motivated by the need to understand and interpret the risk analysis results effectively. By incorporating techniques that provide transparent and interpretable insights, farmers and stakeholders can better comprehend the factors contributing to cyber risks and make informed decisions regarding risk mitigation strategies. Explainability also promotes trust and acceptance of the risk analysis process among users, making it more accessible and actionable.Adapting to Technological Advancements: The proposed method acknowledges the transformation of the agricultural industry through technological advancements, such as machine learning. By embracing these advancements and leveraging them for cyber risk analysis, the method aims to align risk management practices with the evolving digital landscape. It recognizes the need to adapt and utilize advanced tools and techniques to effectively address the emerging cyber threats that arise from increased connectivity and automation in large-scale farming.