Stochastic modeling of self-evolving botnets with vulnerability discovery

Takanori, Kudo; Kimura, Tomotaka; Inoue, Yoshihisa; Aman, Hirohisa; Hirata, Ken–ichi

doi:10.1016/j.comcom.2018.04.010

Cited by 17 publications

(5 citation statements)

References 25 publications

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Section: Motivations and Related Workmentioning

confidence: 91%

“…A comprehensive study of the mathematical epidemiology containing several models is presented in Hethcote. 23 Other interesting models can be found in Paul and Mishra, 24 Ren et al, 25 Kudo et al, 26 and Wanget al 27 A computer worm is an intelligent virus that can automatically spread through vulnerable (or unprotected) computer applications. Some protection systems are incapable of detecting certain viruses, which are classified as polymorphic viruses, such as Stuxnet.…”

Section: Motivations and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Building epidemic models for living populations and computer networks

Kondakci¹,

Kondakci²

2021

Science Progress

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Accurate modeling of viral outbreaks in living populations and computer networks is a prominent research field. Many researchers are in search for simple and realistic models to manage preventive resources and implement effective measures against hazardous circumstances. The ongoing Covid-19 pandemic has revealed the fact about deficiencies in health resource planning of some countries having relatively high case count and death toll. A unique epidemic model incorporating stochastic processes and queuing theory is presented, which was evaluated by computer simulation using pre-processed data obtained from an urban clinic providing family health services. Covid-19 data from a local corona-center was used as the initial model parameters (e.g. [Formula: see text], infection rate, local population size, number of contacts with infected individuals, and recovery rate). A long–run trend analysis for 1 year was simulated. The results fit well to the current case data of the sample corona center. Effective preventive and reactive resource planning basically depends on accurately designed models, tools, and techniques needed for the prediction of feature threats, risks, and mitigation costs. In order to sufficiently analyze the transmission and recovery dynamics of epidemics it is important to choose concise mathematical models. Hence, a unique stochastic modeling approach tied to queueing theory and computer simulation has been chosen. The methods used here can also serve as a guidance for accurate modeling and classification of stages (or compartments) of epidemics in general.

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Section: Motivations and Related Workmentioning

confidence: 91%

Section: Motivations and Related Workmentioning

confidence: 99%

Building epidemic models for living populations and computer networks

Kondakci¹,

Kondakci²

2021

Science Progress

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“…Furthermore, the researchers in [19] exploited the evolutionary algorithm (EA) to generate malware automatically. Kudo et al [20], proposed botnets with adopted ML techniques for prediction of system vulnerabilities and malware selfgoverned evolution. Nevertheless, a search of the literature revealed few studies which address the combination of SI and other intelligence techniques in malware.…”

Section: Introductionmentioning

confidence: 99%

X-ware: a proof of concept malware utilizing artificial intelligence

Truong

Diep

Plucar

2022

IJECE

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<p>Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research literature techniques. This article investigates the possibility of combining artificial neural networks and swarm intelligence to generate a new type of malware. We successfully created a proof of concept malware named X-ware, which we tested against the Windows-based systems. Developing this proof of concept may allow us to identify this potential threat’s characteristics for developing mitigation methods in the future. Furthermore, a method for recording the virus’s behavior and propagation throughout a file system is presented. The proposed virus prototype acts as a swarm system with a neural network-integrated for operations. The virus’s behavioral data is recorded and shown under a complex network format to describe the behavior and communication of the swarm. This paper has demonstrated that malware strengthened with computational intelligence is a credible threat. We envisage that our study can be utilized to assist current and future security researchers to help in implementing more effective countermeasures</p>

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“…Deterministic models are usually global (that is, they suppose that all devices have the same characteristics and the contact topology is homogeneous) and, consequently, they are based on-deterministic-ordinary differential equations [11]). On the other hand, stochastic models can be also global (and based on stochastic differential equations), although the great majority follows the individual paradigm [12,13] and, consequently, takes into account particular characteristics of devices. All of them are compartmental models where the total population of devices is classified into different classes or compartments (depending on the epidemiological state).…”

Section: Introductionmentioning

confidence: 99%

Propagation of the Malware Used in APTs Based on Dynamic Bayesian Networks

2021

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Malware is becoming more and more sophisticated these days. Currently, the aim of some special specimens of malware is not to infect the largest number of devices as possible, but to reach a set of concrete devices (target devices). This type of malware is usually employed in association with advanced persistent threat (APT) campaigns. Although the great majority of scientific studies are devoted to the design of efficient algorithms to detect this kind of threat, the knowledge about its propagation is also interesting. In this article, a new stochastic computational model to simulate its propagation is proposed based on Bayesian networks. This model considers two characteristics of the devices: having efficient countermeasures, and the number of infectious devices in the neighborhood. Moreover, it takes into account four states: susceptible devices, damaged devices, infectious devices and recovered devices. In this way, the dynamic of the model is SIDR (susceptible–infectious–damaged–recovered). Contrary to what happens with global models, the proposed model takes into account both the individual characteristics of devices and the contact topology. Furthermore, the dynamics is governed by means of a (practically) unexplored technique in this field: Bayesian networks.

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Stochastic modeling of self-evolving botnets with vulnerability discovery

Cited by 17 publications

References 25 publications

Building epidemic models for living populations and computer networks

Building epidemic models for living populations and computer networks

X-ware: a proof of concept malware utilizing artificial intelligence

Propagation of the Malware Used in APTs Based on Dynamic Bayesian Networks

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