A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques

Abstract

Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.

Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

“…Since smart healthcare implements IoMT devices, APT attackers can exploit the weaknesses in smart devices, software, or network infrastructure to illegally gain access to steal sensitive patient healthcare information and compromise the integrity of medical data or medical devices, which can threaten the lives of patients [73]. Before attackers launch APTs, social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging are used for collecting the required data about the target, and a successful APT attack typically lasts for a long time [74]. Advanced persistent threats are difficult to detect and prevent since human behavioral variables leading to threats are not considered, there is no evident attack fingerprint, and adversaries conceal their identity while taking advantage of the weaknesses in the compromised system [70][74] [75].…”