SBI Model for the Detection of Advanced Persistent Threat Based on Strange Behavior of Using Credential Dumping Technique

This study investigated the shift from the manual approach of processing data to the digitized method making organizational data prone to attack by cybercriminals. The latest threat Advanced Persistent Threats (APT) was originated by the United States Air Force in 2006 by Colonel Greg Rattray. APT is constantly ravaging industries and governments, which causes severe damages including data loss, espionage, sabotage, leak, or forceful pay of ransom money to the attackers. This study introduces a new model built on Adversarial Tactics Techniques and Common Knowledge (ATT&CK) matrix for detecting APT attack. This is to identify the APT on the first potential victim when the attackers use credential dumping technique. Strange Behavior Inspection Model incorporating several models investigates and monitors APT behavioral features in the CPU, RAM, windows registry, and file systems proposed to detect APT Attack at the first potential victim machine. The Strange Behavior Inspection (SBI) Model proposed in this paper is designed to detect the attack before being developed to more advanced phases. The results of this study are presented at four levels:1- random access memory, 2-central processing unit, 3- windows registry, and 4- file systems. This study proposes a unique model as evidence to detect APT attacks before any other techniques are used. The proposed model reduces the detection time from nine-months to 2.7 minutes.