Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making realtime decisions that can effectively respond to sophisticated attacks. To support this, both researchers and practitioners need to be familiar with current methods of ensuring cybersecurity (CyberSec). In particular, the use of artificial intelligence for combating cybercrimes. However, there is lack of summaries on artificial intelligent methods for combating cybercrimes. To address this knowledge gap, this study sampled 131 articles from two main scholarly databases (ACM digital library and IEEE Xplore). Using a systematic mapping, the articles were analyzed using quantitative and qualitative methods. It was observed that artificial intelligent methods have made remarkable contributions to combating cybercrimes with significant improvement in intrusion detection systems. It was also observed that there is a reduction in computational complexity, model training times and false alarms. However, there is a significant skewness within the domain. Most studies have focused on intrusion detection and prevention systems, and the most dominant technique used was support vector machines. The findings also revealed that majority of the studies were published in two journal outlets. It is therefore suggested that to enhance research in artificial intelligence for CyberSec, researchers need to adopt newer techniques and also publish in other related outlets.

Purpose The purpose of this paper is to determine which factors influence information system security policy compliance. It examines how different norms influence compliance intention. Design/methodology/approach Based on relevant literature on information system security policy compliance, a research model was developed and validated. An online questionnaire was used to gather data from respondents and partial least square structural equation modelling (PLS-SEM) was used to analyse 432 responses received. Findings The results indicated that attitude towards information security compliance mediates the effects of personal norms on compliance intention. In addition, descriptive and subjective norms are significant predictors of personal norms. Originality/value Though advancement in technology has reached significant heights, it is still inadequate to guaranteed information systems’ security. Researchers have identified humans to be central in ensuring information security. To this effect, this study provides empirical evidence of the role of norms in influence information security behaviour.

<abstract> <p>Affective music composition systems are known to trigger emotions in humans. However, the design of such systems to stimulate users' emotions continues to be a challenge because, studies that aggregate existing literature in the domain to help advance research and knowledge is limited. This study presents a systematic literature review on affective algorithmic composition systems. Eighteen primary studies were selected from IEEE Xplore, ACM Digital Library, SpringerLink, PubMed, ScienceDirect, and Google Scholar databases following a systematic review protocol. The findings revealed that there is a lack of a unique definition that encapsulates the various types of affective algorithmic composition systems. Accordingly, a unique definition is provided. The findings also show that most affective algorithmic composition systems are designed for games to provide background music. The generative composition method was the most used compositional approach. Overall, there was rather a low amount of research in the domain. Possible reasons for these trends are the lack of a common definition for affective music composition systems and also the lack of detailed documentation of the design, implementation and evaluation of the existing systems.</p> </abstract>

Affective algorithmic composition systems are emotionally intelligent automatic music generation systems that explore the current emotions or mood of a listener and compose an affective music to alter the person's mood to a predetermined one. The fusion of affective algorithmic composition systems and smart spaces have been identified to be beneficial. For instance, studies have shown that they can be used for therapeutic purposes. Amidst these benefits, research on its related security and ethical issues is lacking. This chapter therefore seeks to provoke discussion on security and ethical implications of using affective algorithmic compositions systems in smart spaces. It presents issues such as impersonation, eavesdropping, data tempering, malicious codes, and denial-of-service attacks associated with affective algorithmic composition systems. It also discusses some ethical implications relating to intensions, harm, and possible conflicts that users of such systems may experience.