Using dynamic models to support inferences of insider threat risk

Sticha, Paul J.; Axelrad, Elise T.

doi:10.1007/s10588-016-9209-1

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…There is proof that fairness and justice are significant factors that affect the employees’ actions and reactions within enterprises (Farahmand and Spafford, 2013). It was identified by several researchers that disgruntled employees who believed that they were treated unfairly by their organisations were potential risks to the organisation (Nurse et al , 2014; Sokolowski et al , 2016; Sticha and Axelrad, 2016). For example, in some aviation positions in which employees were getting low wages, they were tempted to commit crimes of opportunity to augment their income (Loffi and Wallace, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Towards protecting organisations’ data by preventing data theft by malicious insiders

Al-Harrasi¹,

Shaikh

Al‐Badi³

2021

IJOA

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Purpose One of the most important Information Security (IS) concerns nowadays is data theft or data leakage. To mitigate this type of risk, organisations use a solid infrastructure and deploy multiple layers of security protection technology and protocols such as firewalls, VPNs and IPsec VPN. However, these technologies do not guarantee data protection, and especially from insiders. Insider threat is a critical risk that can cause harm to the organisation through data theft. The main purpose of this study was to investigate and identify the threats related to data theft caused by insiders in organisations and explore the efforts made by them to control data leakage. Design/methodology/approach The study proposed a conceptual model to protect organisations’ data by preventing data theft by malicious insiders. The researchers conducted a comprehensive literature review to achieve the objectives of this study. The collection of the data for this study is based on earlier studies conducted by several researchers from January 2011 to December 2020. All the selected literature is from journal articles, conference articles and conference proceedings using various databases. Findings The study revealed three main findings: first, the main risks inherent in data theft are financial fraud, intellectual property theft, and sabotage of IT infrastructure. Second, there are still some organisations that are not considering data theft by insiders as being a severe risk that should be well controlled. Lastly, the main factors motivating the insiders to perform data leakage activities are financial gain, lack of fairness and justice in the workplace, the psychology or characteristics of the insiders, new technologies, lack of education and awareness and lack of management tools for understanding insider threats. Originality/value The study provides a holistic view of data theft by insiders, focusing on the problem from an organisational point of view. Organisations can therefore take into consideration our recommendations to reduce the risks of data leakage by their employees.

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Section: Literature Reviewmentioning

confidence: 99%

Towards protecting organisations’ data by preventing data theft by malicious insiders

Al-Harrasi¹,

Shaikh

Al‐Badi³

2021

IJOA

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“…Recent work by Sticha et al (Sticha and Axelrad, 2016) introduced the integration of two modelling approaches (Bayesian belief networks and system dynamics models) each of which have different strengths in respect to addressing the problem of insider threats. Moreover, BN is an increasingly popular modelling technique in cyber-security fields such as forensic, risk management and smart grid security, (Chockalingam et al, 2017;Ross et al, 2017;Poolsappasit et al, 2012;Wadhawan et al, 2018).…”

Section: Modelling For Insider Threat Risk Predictionmentioning

confidence: 99%

“…On the other hand, in some cases that no evidence available "non-informative" or "reference" data appears, we manually set the conditional probabilities using a number of heuristics (Sticha and Axelrad, 2016). In these cases, we set the conditional probabilities based on our empirical experience.…”

Section: Stage 2: Estimated Conditional Probabilitiesmentioning

confidence: 99%

Insider Threat Risk Prediction based on Bayesian Network

Elmrabit

Yang

et al. 2020

Computers & Security

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Insider threat protection has received increasing attention in the last ten years due to the serious consequences of malicious insider threats. Moreover, data leaks and the sale of mass data have become much simpler to achieve, e.g., the dark web can allow malicious insiders to divulge confidential data whilst hiding their identities. In this paper, we propose a novel approach to predict the risk of malicious insider threats prior to a breach taking place. Firstly, we propose a new framework for insider threat risk prediction, drawing on technical, organisational and human factor perspectives. Secondly, we employ a Bayesian network to model and implement the proposed framework. Furthermore, this Bayesian network-based prediction model is evaluated in a range of challenging environments. The risk level predictions for each authorised users within the organisation are examined so that any insider threat risk can be identified. The proposed insider threat prediction model achieved better results when compared to the empirical judgments of security experts

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Modeling Interventions for Insider Threat

Osterritter

Carley

2020

Lecture Notes in Computer Science

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Using dynamic models to support inferences of insider threat risk

Cited by 12 publications

References 20 publications

Towards protecting organisations’ data by preventing data theft by malicious insiders

Towards protecting organisations’ data by preventing data theft by malicious insiders

Insider Threat Risk Prediction based on Bayesian Network

Modeling Interventions for Insider Threat

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