Flow Based Analysis of Advanced Persistent Threats Detecting Targeted Attacks in Cloud Computing

Cloud computing provides industry, government, and academic users' convenient and cost-effective access to distributed services and shared data via the Internet. Due to its distribution of diverse users and aggregation of immense data, cloud computing has increasingly been the focus of targeted attacks. Meta-analysis of industry studies and retrospective research involving cloud service providers reveal that cloud computing is demonstrably vulnerable to a particular type of targeted attack, Advanced Persistent Threats (APTs). APTs have proven to be difficult to detect and defend against in cloud based infocommunication systems. The prevalent use of polymorphic malware and en-crypted covert communication channels make it difficult for existing packet inspecting and signature based security technologies such as; firewalls, intrusion detection sensors, and anti-virus systems to detect APTs. In this paper, we examine the application of an alternative security approach which applies an algorithm derived from flow based monitoring to successfully detect APTs. Results indicate that statistical modeling of APT communications can successfully develop deterministic characteristics for detection is a more effective and efficient way to protect against APTs.