Factors Influencing Cybersecurity Risk Among Minority-Owned Small Businesses

Small businesses are increasingly becoming targets of cyberattacks. Minority-owned small businesses may face additional challenges when it comes to cybersecurity, due to factors such as limited resources and lack of awareness. Therefore, it is important to understand the specific factors that influence cybersecurity risk among minority-owned small businesses in order to develop effective strategies to protect them from cyber threats. This study aimed to identify the factors influencing cybersecurity risk among minority-owned small businesses. The variables examined were lack of resources, lack of awareness, use of outdated technology, limited training, and targeted attacks. A multiple regression analysis was conducted with a sample size of 252 minority-owned small businesses. The results showed that all of the variables were statistically significant in predicting cybersecurity risk. Lack of resources, lack of awareness, and use of outdated technology were found to be significant predictors of cybersecurity risk. Limited training and targeted attacks were also significant predictors. These findings suggest that minority-owned small businesses are vulnerable to cybersecurity risks due to a combination of factors, including limited resources, lack of awareness, outdated technology, and inadequate training. Therefore, it is important for small business owners to prioritize cybersecurity and invest in the necessary resources and training to protect their businesses from cyber threats

A Fuzzy-Based Brokering Service for Cloud Plan Selection

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user\u2019s desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users\u2019 desiderata, and hence to help users in the cloud service selection process

From Database to Cyber Security : Essays Dedicated to Sushil Jajodia on the Occasion of His 70th Birthday

This Festschrift is in honor of Sushil Jajodia, Professor in the George Mason University, USA, on the occasion of his 70th birthday. This book contains papers written in honor of Sushil Jajodia, of his vision and his achievements. Sushil has sustained a highly active research agenda spanning several important areas in computer security and privacy, and established himself as a leader in the security research community through unique scholarship and service. He has extraordinarily impacted the scientific and academic community, opening and pioneering new directions of research, and significantly influencing the research and development of security solutions worldwide. Also, his excellent record of research funding shows his commitment to sponsored research and the practical impact of his work. The research areas presented in this Festschrift include membrane computing, spiking neural networks, phylogenetic networks, ant colonies optimization, work bench for bio-computing, reaction systems, entropy of computation, rewriting systems, and insertion-deletion systems

Detection of advanced web bots by combining web logs with mouse behavioural biometrics

Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness: (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors’ mouse movements is more effective and robust toward detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches