Security Algorithms to Detect and Prevent Advanced Persistent Threats in Cloud Computing: A Systematic Review

Cloud computing simplifies and improves access to data and services for organizations and individuals. The number of users and data in cloud computing increases, making it more susceptible to targeted attacks. The cloud is a complex IT environment that makes it harder to detect advanced persistent threats (APTs). Continuous monitoring and defensive systems have proven efficient in identifying and blocking these attacks, although they have limits due to their reliance on known attack signatures. Thus, this paper proposes a security model for protecting cloud data from these attacks and overcoming the limitations of existing approaches. Our security model uses Deep Packet Inspection (DPI), AUSPEX, Domain-Generating Algorithm (DGA), and Pearson Correlation Coefficient (PCC) to detect and prevent APTs attacks. This model simplifies classification by identifying complex relationships between database features by intercepting and correlating operating system events with network events. Moreover, this paper reviews existing algorithms used to prevent APTs in cloud computing. Thereafter, we present the limitations of these existing algorithms. This comprehensive study serves as a guideline for everyone interested in developing security techniques for preventing APTs in cloud computing. Finally, the paper presents the conclusion and future work.