Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.

An increasing number of people around the globe are becoming more reliant on information communications technology and digital infrastructure for seamless access to information, communication, and efficient service delivery. Nonetheless, this interconnectivity creates vulnerabilities and interdependencies which need to be managed at a national level. As such, securing the cyberspace is therefore an essential requirement for allowing the economic and social development of a nation and its people to fully benefit from the digital transformation. Securing the cyber space at a national level is a shared responsibility which requires a harmonised action for prevention, preparation, response, and incident recovery on the part of government authorities, the private sector, and civil society. This chapter evaluates national cybersecurity strategies of five African countries including Botswana, Rwanda, Mauritius, Ghana, and Nigeria using the national cybersecurity strategy guide as drafted by the International Telecommunications Union (ITU) alongside other stakeholders.

The effects of Coronavirus disease at a global level led the years 2020, 2021, and 2022. This global pandemic caused millions of organizations to shift to online workspaces to curb the rising numbers of contaminations. In doing this rapid change from physical to digital spaces, organizations were not adequately prepared for the aftermath. This included more sophisticated phishing attacks, directed scams, and taking advantage of poorly executed processes for the product supply chain. In this chapter, the authors will cover the events and the aftermath of security threats focusing on the onset of the pandemic. Request access from your librarian to read this chapter's full text.

Background The high rise in electronic devices in modern-day society has resulted in crimes in cyber-related crimes as criminals resort to hacking, illegal use of these devices. This is primarily due to perceived high rewards and low chances of being apprehended. The rise in cyber crimes poses a significant challenge to forensic investigators as now they have to process huge volumes of data from a variety of sources within a limited time. This results in investigators taking longer to process cases and in some instances missing links as they deal with data from a variety of sources. Findings In this paper, we provide a definition of big data forensics, and then we discuss the challenges associated with digital forensics investigations when dealing with big data. We provide details on how volume, variety, and velocity all pose a huge challenge in digital forensics investigations. We then discuss how a novel solution called Forensic Cloud Environment (FCE) leverages the power of Hadoop, HBase, and MapReduce to provide a solution for big data forensic challenges. Conclusion In conclusion, the fact that FCE provides an environment to store huge volumes of data from a variety of sources allows for an improved processing time of data. Hence, providing an environment for big data forensics for the future.