Network Security in Big Data: Tools and Techniques

Verma, Pushpak; Chandra, Tej Bahadur; Dwivedi, Ashutosh

doi:10.1007/978-81-322-2755-7_26

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…So that from security point of view the 5 Vs of Big Data demands ultra-fast response times from security and privacy solutions/products [20].…”

Section: Big Data For Security and Privacymentioning

confidence: 99%

A comparative study of recent advances in Big data Security and Privacy

Kourid¹,

Chikhi²,

Hong³

2017

AJMAHS

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Big data is coming with new challenges in security; involve the three aspects of security: (confidentiality, availability, integrity) and privacy. These chal-lenges are due to the characteristics 5V of data in Big data: velocity, variety, volume, value, and veracity. And depend on several level of security: network, data, applica-tion, and authentication. Furthermore, big data is also promising security. The huge amount of data provides a more security information like data logs. Moreover, big da-ta analysis can be applied to security. Many theories for big data security are pro-posed in literature, covering the different aspects of security and privacy. Recently, different schemes and frameworks are introduced to reach high level of security in big data, based on different security theories. In this paper, we discuss different challeng-es in big data security and privacy, and we introduce recent security theories and works in this filed. A comparative study of latest advances in big data security and privacy is presented.

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“…So that from security point of view the 5 Vs of Big Data demands ultra-fast response times from security and privacy solutions/products [20].…”

Section: Big Data For Security and Privacymentioning

confidence: 99%

A comparative study of recent advances in Big data Security and Privacy

Kourid¹,

Chikhi²,

Hong³

2017

AJMAHS

View full text Add to dashboard Cite

show abstract

A Comparative Study of Recent Advances in Big Data for Security and Privacy

Kourid

Chikhi

2017

Networking Communication and Data Knowledge Engineering

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Vanet FDIA Solutions using Blockchain Based IPFS-Trust Management System with ML SVR Model

Grover

Prasad

2023

Preprint

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The Internet of Vehicles (IoV) is the next phase in the evolution of vehicular ad hoc networks (VANETs).Multiple types of Smart Networks exists in our surrounding.i.e., Wireless Sensor Networks (WSNs), Crowd Sensing Networks (CSNs), and Internet of Vehicles, etc A VANET is a collection of mobile nodes (vehicles) that share data through ad hoc on-demand connections. Vehicle Tracking is one of the uses of IOV(Internet of Vehicles) and Vehicle Security is one of the major issues for all vehicle owners. On a vehicle, there are various on-board sensors that sense a vehicle’s motion and the surrounding environment. On-board sensors can also warn drivers about approaching vehicles, speeding, and slippery road conditions. The main aim of the paper is to provide solutions for False Data Injection Attack by Integration of Blockchain Based IPFS-Trust Management System with ML SVR Regression Model. Due to Network Assaults and Threats under Vanet System, the safety of the drivers is under stake and Critical. A rogue node can send out erroneous messages, causing unavoidable scenarios. We first filter the received data from Vehicles creating false traffic jam warning messages using the Machine learning SVR Regression Model where data is created and split into train and test data. We used Machine learning supervised algorithm to find whether the vehicle is a legitimate vehicle or an attacker vehicle and the result is validated using the parameters like Accuracy, Loss Rate, Precision, Recall, and F-Test Score. Algorithm Implementation results show that the FDIA attack strategy achieves a better performance than the without using ML algorithm of SVR Regression Model based attack strategy in Predicting the Vanet Security. Also, we studied the various ways to mitigate the impact of false data injection into the network through a compromised node. Users can access the system through DApp, an Ethereum-distributed application, and manage their vehicle data.

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Network Security in Big Data: Tools and Techniques

Cited by 3 publications

References 2 publications

A comparative study of recent advances in Big data Security and Privacy

A comparative study of recent advances in Big data Security and Privacy

A Comparative Study of Recent Advances in Big Data for Security and Privacy

Vanet FDIA Solutions using Blockchain Based IPFS-Trust Management System with ML SVR Model

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