The increase in cyber-attacks impacts the performance of organizations in the industrial sector, exploiting the vulnerabilities of networked machines. The increasing digitization and technologies present in the context of Industry 4.0 have led to a rise in investments in innovation and automation. However, there are risks associated with this digital transformation, particularly regarding cyber security. Targeted cyber-attacks are constantly changing and improving their attack strategies, with a focus on applying artificial intelligence in the execution process. Artificial Intelligence-based cyber-attacks can be used in conjunction with conventional technologies, generating exponential damage in organizations in Industry 4.0. The increasing reliance on networked information technology has increased the cyber-attack surface. In this sense, studies aiming at understanding the actions of cyber criminals, to develop knowledge for cyber security measures, are essential. This paper presents a systematic literature research to identify publications of artificial intelligence-based cyber-attacks and to analyze them for deriving cyber security measures. The goal of this study is to make use of literature analysis to explore the impact of this new threat, aiming to provide the research community with insights to develop defenses against potential future threats. The results can be used to guide the analysis of cyber-attacks supported by artificial intelligence.

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As interações humanas virtuais têm sido ampliadas com o uso crescente da Internet e redes sociais, elevando os riscos de ameaças cibernéticas de Engenharia Social. O uso de Bots nesses ataques permite escalabilidade na exploração da confiança dos usuários, provocando riscos de segurança. Poucos são os trabalhos com foco nas ações automatizadas de Engenharia Social com o uso de Bots. Este artigo apresenta uma verificação dos controles de uma rede social profissional quanto à identificação e bloqueio desses ataques automatizados, utilizando um Bot de prova de conceito. A análise e discussão dos resultados permite demonstrar as vulnerabilidades de segurança presentes nas redes profissionais que podem ser exploradas para construção da relação de confiança do usuário com um Bot malicioso.