Considering Quarantine in the SIRA Malware Propagation Model

Abstract

As the beginning of the 21 st century was marked by a strong development in data science and, consequently, in computer networks, models for designing preventive actions against intruding, data stealing, and destruction became mandatory. Following this line, several types of epidemiological models have been developed and improved, considering different operational approaches. e development of the research line using traditional SIR(Susceptible, Infected, Removed) model for data networks started in the 1990s. In 2005, an epidemiological compartmental model containing antidotal nodes, SIRA (Susceptible, Infected, Removed, Antidotal), was introduced to study how the antivirus policies affect the network reliability. e idea here is to study the consequence of quarantine actions in a network by modifying the SIRA model, introducing quarantine nodes generating the SIQRA (Susceptible, Infected, Quarantine, Removed, Antidotal) model. Analytical and numerical approaches result in parameter conditions for the existence and stability of disease-free and endemic equilibrium points for two different cases: saturation and nonsaturation of the quarantine population block. Based on these results, operational actions can be planned to improve the network reliability.

On the Time Shift Phenomena in Epidemic Models

“…In the literature, there are two basic approaches, one of which adds a compartment to the model as in Ref. [22] and the other adds a function β/(c + Q(t)), where Q(t) is a time-dependent rate as in Ref. [23] and has the effect of shortening the duration of the infection period.…”

A Novel Malware Classification Method Based on Crucial Behavior

“…e value [27] API call sequence Simple, vulnerable to reorder or irrelevant API calls Lee et al [28] API call sequence Hansen et al [29] API call sequence; arguments; frequency Amin [30,31] Opcode End-to-end learning D'Angelo et al [32] API call sequence-based image Park et al [34] Behavioral graph High dimensional features can bring more calculations Elhadi et al [11] API call graph Nikolopoulos and Polenakis [35] System call dependency graph Fredrikson et al [37] Optimally discriminative specification Simplified representation of behavior graphs Alam et al [40] Control flow graph-based feature Ding et al [41] API dependency graph 4 Mathematical Problems in Engineering 0x0000044c of Handle is used to connect the RegQuer-yValue on line 2. e details of API call graph construction are described in our previous work [43]. It is necessary to extract crucial behaviors from the API call graph for malware classification.…”

“…Moreover, the advent of new technologies has contributed to the increasing complexity of malware. Facing numerous and sophisticated malware variants, malware detection is urgently needed (e.g., see [3][4][5]).…”

Complex Behaviors of Epidemic Model with Nonlinear Rewiring Rate

“…We have investigated the qualitative behaviors of system (5). It is very interesting to apply the theoretical results to the problems with real data, as done in the paper [35]. It would also be important to consider the case where the rewiring rate is a saturated function of infected nodes and consider the time delay of adaptive rewiring.…”