In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

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Recently, using advanced cryptographic techniques to process, store, and share datasecurely in an untrusted cloud environment has drawn widespread attention from academicresearchers. In particular, Ciphertext‐Policy Attribute‐Based Encryption (CP‐ABE) is a promising,advanced type of encryption technique that resolves an open challenge to regulate fine‐grainedaccess control of sensitive data according to attributes, particularly for Internet of Things (IoT)applications. However, although this technique provides several critical functions such as dataconfidentiality and expressiveness, it faces some hurdles including revocation issues and lack ofmanaging a wide range of attributes. These two issues have been highlighted by many existingstudies due to their complexity which is hard to address without high computational cost affectingthe resource‐limited IoT devices. In this paper, unlike other survey papers, existing single andmultiauthority CP‐ABE schemes are reviewed with the main focus on their ability to address therevocation issues, the techniques used to manage the revocation, and comparisons among themaccording to a number of secure cloud storage criteria. Therefore, this is the first review paperanalysing the major issues of CP‐ABE in the IoT paradigm and explaining the existing approachesto addressing these issues.

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