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.

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Current literature suggests that engineering activities of software engineering and software startup engineering differ. Thus, there is the need to elicit competencies specific for software startup engineering. This paper proposes a model that provides the various types of competencies and their respective relevance at the various stages of software startup evolution.