Performance Analysis of Honeypot with Petri Nets

Shi, Leyi; Li, Yang; Feng, Haijie

doi:10.3390/info9100245

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Numerical data modeling is a broad research area which involves a wide variety of novel artificial-intelligence and statistical tools. A few examples of recent contributions in this field are Petri nets to model honeypot [13], extreme learning machines in monthly precipitation time series forecasting [14], and recurrent neural networks to language modeling, emotion classification and polyphonic modeling [15]. Specifically to the problem treated in this paper, traditional artificial neural-network techniques to build up empirical models of GFR were proposed and tested in the scientific literature, although their performance appeared unsatisfactory or questionable [16,17].…”

Section: Introductionmentioning

confidence: 99%

Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method

Giles

Fiori

2019

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Statistical bivariate numerical modeling is a method to infer an empirical relationship between unpaired sets of data based on statistical distributions matching. In the present paper, a novel efficient numerical algorithm is proposed to perform bivariate numerical modeling. The algorithm is then applied to correlate glomerular filtration rate to serum creatinine concentration. Glomerular filtration rate is adopted in clinical nephrology as an indicator of kidney function and is relevant for assessing progression of renal disease. As direct measurement of glomerular filtration rate is highly impractical, there is considerable interest in developing numerical algorithms to estimate glomerular filtration rate from parameters which are easier to obtain, such as demographic and `bedside’ assays data.

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Section: Introductionmentioning

confidence: 99%

Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method

Giles

Fiori

2019

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“…The methodology used for formal method analysis of conpot is a CPN tool [21] which is used to model the Petri net of conpot honeypot and verify the correctness of it using state-space tool incorporated in CPN. CPN method is a combination of coloured Petri nets and Meta Language programming which is used for modelling and verification of the concurrent system.…”

Section: Methodsmentioning

confidence: 99%

“…The Coloured Petri Net [21] tool version 4.0.1 is used to model SCADA Honeypot models. The coloured Petri net tool enables modelling of complex and concurrent systems, and the coloured nets are the extended Petri nets that allow identifying tokens by "colors."…”

Section: Honeypot Model In Cpnmentioning

confidence: 99%

Industrial Control Systems Honeypot: A Formal Analysis of Conpot

Gokhale¹,

Dalvi²,

Siddavatam³

2021

IJCNIS

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Technologies used in ICS and Smart Grid are overlapping. The most discussed attacks on ICSs are Stuxnet and Black energy malware. The anatomy of these attacks not only pointed out that the security of ICS is of prime concern but also demanded to execute a proactive approach in practicing ICS security. Honeypot is used to implement defensive measures for security. The Honeynet group released Honeypot for ICS labelled as Conpot in 2013. Though the Conpot is low interactive Honeypot, it emulates processes of different cyber-physical systems, typically Smart Grid. In the literature, the effectiveness of Honeypot operations was studied by challenging limitations of the existing setup or proposing new variants. Similar approaches are followed for Conpot evaluation. However, none of the work addressed a formal verification method to verify the engagement of Honeypot, and this makes the presented work unique. For proposed work, Coloured Petri Net (CPN) tool is used for formal verification of Conpot. The variants of Conpot are modelled, including initial state model, deadlock state model and livelock model. Further evaluation of these models based on state space analysis results confirmed that Conpot could lure an attacker by engaging him in an infinite loop and thereby limiting the scope of the attacker from exploring and damaging the real-time systems or services. However, in the deadlock state, the attacker’s activity in the conpot will be restricted and will be unable to proceed further as the conpot model incorporates deadlock loop.

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“…In [31], C. Kiekintveld et al present a study about game theory methods used to deploy honeypots in an efficient manner, modelling the behaviour of the attacker and the defender. In [32], L. Shi et al investigate the performance of honeypots through Petri nets. In [33], W. Zhang et al present a honeynet composed of multiport honeypots for countering IoT attacks.…”

Section: Related Workmentioning

confidence: 99%

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Radoglou-Grammatikis

Sarigiannidis

Diamantoulakis

et al. 2024

IEEE Trans. Emerg. Topics Comput.

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The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultradense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e − Greedy and (b) Q − Learning. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.

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Performance Analysis of Honeypot with Petri Nets

Cited by 6 publications

References 22 publications

Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method

Glomerular Filtration Rate Estimation by a Novel Numerical Binning-Less Isotonic Statistical Bivariate Numerical Modeling Method

Industrial Control Systems Honeypot: A Formal Analysis of Conpot

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

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