Smartphone Malware and Its Propagation Modeling: A Survey

Abstract

Smartphones are pervasively used in society, and have been both the target and victim of malware writers. Motivated by the significant threat that presents to legitimate users, we survey the current smartphone malware status and their propagation models. The content of this paper is presented in two parts. In the first part, we review the short history of mobile malware evolution since 2004, and then list the classes of mobile malware and their infection vectors. At the end of the first part, we enumerate the possible damage caused by smartphone malware. In the second part, we focus on smartphone malware propagation modeling. In order to understand the propagation behavior of smartphone malware, we recall generic epidemic models as a foundation for further exploration. We then extensively survey the smartphone malware propagation models. At the end of this paper, we highlight issues of the current smartphone malware propagation models and discuss possible future trends based on our understanding of this topic.

Internet of Things Malware : A Survey

“…Zolkipli et al [41] offers seventeen classes of classification based on form of existence. Peng at al [42] in their survey of smartphone malware highlights five classes of malware based on a matrix on propagation factors, existence form and risk. Based on review of malware literature, classification of malware in IoT will follow the classical classification.…”

“…Virus: Is a type of malware that gain access to the device or software without user knowledge and duplicates itself or commits the programmed malicious task. Viruses cannot exist on their own therefore require a carrier to propagate [42] which makes it hard to survive in sensors and actuators. b. Worms: It gain access to systems without owner's permission and operates stealthily with capability to duplicate into thousands of copies.…”

“…Rey classifies the models as either Deterministic or Stochastic; Continuous or Discrete ; (global or individual models. Peng et al [42] groups epidemic models as deterministic, stochastic and spatial temporal. This work adopts a mix of Rey and Peng et al classification and extends the subcategories based on recent works.…”

“…In human epidemiology the model has been applied in maladies such as chickenpox, measles and mumps [42]. The parameters for SIR model are formulated as follows; β is the infection rate: probability of contagion after contact per unit time.…”

PRoFIT: Modelo forense-IoT con integración de requisitos de privacidad

“…Aunque este es un escenario hipotético y el malware, así como la aplicación deals4U, son ficticios, no es descabellado que ataques de este tipo pudieran producirse (o se estén produciendo) sin que el usuario lo perciba [13].…”