Cyber forensics framework for big data analytics in IoT environment using machine learning

Chhabra, Gurpal Singh; Singh, Varinder; Singh, Maninder

doi:10.1007/s11042-018-6338-1

Cited by 49 publications

(18 citation statements)

References 38 publications

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“…The performance of the proposed approach is obtained by comparing the proposed with the existing methods like generalized forensic framework, 27 Hadoop‐based analytic framework, 28 Semantic web‐based framework, 30 Adam‐based Deep stacked autoencoder, 37 SFO‐based Deep stacked autoencoder, and Jaya‐based Deep stacked autoencoder.…”

Section: Resultsmentioning

confidence: 99%

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Cyber forensic framework for big data analytics using Sunflower Jaya optimization‐based Deep stacked autoencoder

Venugopal¹,

Sathianesan

Rengaswamy

2021

Int J Numerical Modelling

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The skills of forensic analysts are at risk to process the increasing data in the Internet of Things‐based environment platforms. However, the technical issues like anti‐forensics, variety of traffic formats, steganography or encrypted data, and real‐time live investigation degrades the performance of the cyber forensic framework. Therefore, an effective method named Sunflower Jaya Optimization‐based Deep stacked autoencoder (SFJO‐based Deep stacked autoencoder) is proposed to perform the cyber forensic framework. The finite element model of Sunflower optimization is integrated with the control parameters of Jaya optimization to solve the issues in the cyber forensic framework. The proposed SFJO‐based Deep stacked autoencoder uses the pollination and the peculiar behaviors to enable the cyber forensic framework based on the error value in the big data analytics model. Accordingly, the solution with the minimal value of error is accepted as the best optimal solution by computing the orientation vector. However, the proposed model is illustrated based on the unconstrained benchmark function, which in turn results in the fitness function to reveal the best candidate solution. The proposed SFJO‐based Deep stacked autoencoder attained better performance using metrics like precision, sensitivity, and specificity with the values of 0.9053, 0.8865, and 0.8839 using dataset‐1.

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Section: Resultsmentioning

confidence: 99%

“…Various existing cyber forensic framework methods are surveyed along with their merits and demerits. Chhabra et al 27 developed a generalized forensic framework for extraction, analysis, and traffic translation of traffic features. It effectively predicts the reliability and authenticity of the evidence.…”

Section: Literature Surveymentioning

confidence: 99%

Cyber forensic framework for big data analytics using Sunflower Jaya optimization‐based Deep stacked autoencoder

Venugopal¹,

Sathianesan

Rengaswamy

2021

Int J Numerical Modelling

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“…The suggested work was evaluated with the Bot-IoT and UINSW-NB15 datasets. With a focus on big data and IoT, Chhabra et al (2020) presented a cyber forensic framework for big data analytics in an IoT environment using machine learning. Furthermore, the authors mentioned different publicly available datasets for machine-learning models.…”

Section: Intrusion Detection Systems With a Focus On Iotmentioning

confidence: 99%

Cyber risk and cybersecurity: a systematic review of data availability

Cremer

Sheehan

Fortmann

et al. 2022

Geneva Pap Risk Insur Issues Pract

118

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Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

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“…This piece of work can be used to enhance IoT forensics especially in cases of compromised IoT devices through botnets, however, as it is an intrusion detection mechanism, it remains to be a forensics readiness process. A forensic framework is proposed by [58] for big data in IoT environments for precision and sensitivity. The framework employs a Machine Learning (ML) approach using the Google's MapReduce as the basis for understanding traffic, extracting, and analysing the data.…”

Section: ) Other Iot Forensic Processesmentioning

confidence: 99%