Keep Calm and Carry on with Cybersecurity @Home: A Framework for Securing Homeworking IT Environment

For the first time in modern history, businesses had to suddenly facilitate homeworking for a large proportion, if not all, of their workforce because of COVID-19 pandemic. The fact that employees access sensitive corporate data from non-corporate networks opens the door wide for many cybersecurity risks that could result in data loss, breaches and consequently huge financial loss. Since the move was sudden, most businesses, especially small ones, did not have the time to assess their homeworking cybersecurity requirements. This paper aims to bridge this gap and propose a multi-layered framework that is focused on businesses' requirements to guide cybersecurity @home activities. The framework can be also beneficial for businesses that currently have homeworking cybersecurity policies to assess their compliance with the framework and enrich it.

Evaluation of Selected Stacked Ensemble Models for the Optimal Multi-class Cyber-Attacks Detection

The significant rise in the frequency and sophistication of cyber-attacks and their diversity necessitated various researchers to develop strong and effective approaches to address recurring cyber threat challenges. This study evaluated the performance of three selected meta-learning models for optimal multi-class detection of cyber-attacks using the University of New South Wales 2015 Network benchmark (UNSW-NB15) Intrusion Dataset. The results of this study show and confirm the ability of the three base models; Naive Bayes, C4.5 Decision Tree, and K-Nearest Neighbor for solving multi-class problems. It further affirms the knack of the duo of feature selection techniques and stacked ensemble learning to optimize ML models' performances. The stacking of the predictions of the information gain base models with Model Decision Tree meta-algorithm recorded the most improved and optimal cyber-attacks detection accuracy and Mattew's correlation Coefficient than the stacking with the Multiple Model Trees (MMT) and Multi Response Linear regression (MLR) Meta algorithms.

The Cyber Insurance Market in Israel

Cyberspace poses many challenges threats and risks which, with the development of technology, are increasingly affecting all of us in terms of the scope, frequency, damage, immediate effects and long-term consequences. All these impact many sectors. Cyber insurance is one of the tools for managing these risks. Indeed, the cyber insurance market is developing around the world, but in many countries, as well as in Israel, it has not yet reached its full potential. This study examined the cyber insurance market in Israel by analyzing the existence of a cyber insurance service among insurance companies and agencies. In addition, it examined the reference to this market by various experts in this field. All the above has been done with the aim of establishing the degree of maturity of the market in Israel. The findings of the study indicate that the cyber insurance market in Israel is not mature enough. Cyber insurance is not a popular product among companies to manage their cyber risks, mainly due to lack of information, knowledge, awareness among customers, insurance agencies, insurance companies, and government bodies. However, steps are being taken, mainly by Israel National Cyber Directorate (INCD), to empower insurance agents and their customers with relevant professional knowledge.

CyberOps: Situational Awareness in Cybersecurity Operations

Cybersecurity operations (CyberOps) is the use and application of cybersecurity capabilities to a domain, department, organisation or nation. It is fundamentally to protect digital investments, contribute to national economic wellbeing by providing a safe, secure and conducive environment to conduct business and to protect a nation’s critical national infrastructures and citizens welfare. In this paper, we investigate operational factors that influence situational awareness of CyberOps, specifically, the features that deals with understanding and comprehension of operational and human factors aspects and that helps with insights on human operator decision making (e.g., cognition, teamwork, knowledge, skills and abilities). The operational factors discussed in this paper range from tools, techniques, integration, architecture to automation, cognition, people, policy, process and procedures.