Compromised user credentials detection in a digital enterprise using behavioral analytics

Shah, Saleh; Shah, Babar; Amin, Adnan; Al-Obeidat, Feras; Chow, Francis; Moreira, Fernando; Anwar, Sajid

doi:10.1016/j.future.2018.09.064

Cited by 18 publications

(9 citation statements)

References 22 publications

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“…In this work, we use precision, recall, F-measure, false-positive rate, and EDP evaluation metrics in order to evaluate the proposed trust model's performance. These evaluation metrics are well-known metrics for validating the results [67,68].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Privacy‐preserving blockchain‐based authentication and trust management in VANETs

Ahmed

Mukathe

2022

IET Networks

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Vehicular Ad Hoc Networks (VANETs) are characterized by high mobility of nodes and volatility, which make privacy, trust management, and security challenging issues in VANETs' design. In such networks, data can be exposed to a variety of attacks, the most dangerous is false information dissemination, which threatens the safety and efficiency of transportation systems. False emergency messages can be injected by inside attackers to announce fake incidents such as traffic accidents, resulting in a false information attack. As the data in VANET is based on events, any trust mechanism must first identify the true events. To address these security challenges, a blockchain-based authentication scheme and trust management model are proposed for VANETs. Using the authentication scheme, vehicles are enabled to send messages anonymously to the roadside units (RSUs) and the identity privacy of vehicles is protected. Besides, the proposed trust management model is designed to detect and deal with false information by evaluating the trustworthiness of vehicles and data. Using the trust model, when vehicles report an incident to the nearest RSU, the RSU is able to verify whether or not the incident took place. This mechanism ensures that RSUs send only verified event notifications. Finally, RSUs participate in updating the trust values of vehicles and store these values in the blockchain. The efficiency of the proposed authentication scheme is validated through analysis while the trust model is validated through simulations. The results obtained show that the proposed authentication scheme and the trust model provide better performance than other state-of-the-art models where malicious vehicles can be identified efficiently and RSUs are enabled to broadcast only legitimate events.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Privacy‐preserving blockchain‐based authentication and trust management in VANETs

Ahmed

Mukathe

2022

IET Networks

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“…digital transformation and cloud computing) (Han and Trimi, 2022; Llopis-Albert et al. , 2021; Shah et al. , 2019), competition law (antitrust) that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies (Lundqvist, 2017; Sivinski et al.…”

Section: Discussion Implications and Future Research Challengesmentioning

confidence: 99%

“…There are also some important associated concepts that can transversally add value for many phases of the competitive intelligence cycle with big data: some disruptive technologies (e.g. digital transformation and cloud computing) (Han and Trimi, 2022;Llopis-Albert et al, 2021;Shah et al, 2019), competition law (antitrust) that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies (Lundqvist, 2017;Sivinski et al, 2017;Wasastjerna, 2019), privacy concerns (Hajli et al, 2021;Montes et al, 2019) and data science (Kayabay et al, 2022;Provost and Fawcett, 2013).…”

Section: What Are the Main Implications Of Big Data For Firms' Compet...mentioning

confidence: 99%

One decade of big data for firms' competitiveness: insights and a conceptual model from bibliometrics

Tchuente

Haddadi

2023

JEIM

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PurposeUsing analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.Design/methodology/approachIn this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.FindingsBased on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.Research limitations/implicationsThe findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.Practical implicationsThis study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.Social implicationsThis study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.Originality/valueThe authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

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“…In this work, state-of-the-art evaluation measures are utilized for assessing the proposed model's performance, such as precision, recall, overall accuracy, and communication overhead. These metrics are well-known evaluation measures to validate the obtained results [22].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

et al. 2020

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Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

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Compromised user credentials detection in a digital enterprise using behavioral analytics

Cited by 18 publications

References 22 publications

Privacy‐preserving blockchain‐based authentication and trust management in VANETs

Privacy‐preserving blockchain‐based authentication and trust management in VANETs

One decade of big data for firms' competitiveness: insights and a conceptual model from bibliometrics

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

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