VPN IPSEC &amp;amp; SSL technology Security and management point of view

Lakbabi, Abdelmajid; Orhanou, Ghizlane; Hajji, Saïd El

doi:10.1109/ngns.2012.6656108

Cited by 12 publications

(4 citation statements)

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“…Lakbabi et al [20] presented the differences between protocols strengths and weaknesses from a security and management perspective of IPSEC and SSL VPN technologies. They have briefed the general overview of all the layer 2 VPN technologies which have got no encryption mechanism, and so, IPSEC and SSL VPN has been the topic of discussion in this paper.…”

Section: Firewallmentioning

confidence: 99%

Secure Remote Access IPSEC Virtual Private Network to University Network System

Sharma

2021

j. of Comput. sci. res.

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With the popularity of the Internet and improvement of information technology, digital information sharing increasingly becomes the trend. More and More universities pay attention to the digital campus, and the construction of digital library has become the focus of digital campus. A set of manageable, authenticated and secure solutions are needed for remote access to make the campus network be a transit point for the outside users. Remote Access IPSEC Virtual Private Network gives the solution of remote access to e-library resources, networks resources and so on very safely through a public network. It establishes a safe and stable tunnel which encrypts the data passing through it with robust secured algorithms. It is to establish a virtual private network in Internet, so that the two long-distance network users can transmit data to each other in a dedicated network channel. Using this technology, multi-network campus can communicate securely in the unreliable public internet.

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Section: Firewallmentioning

confidence: 99%

Secure Remote Access IPSEC Virtual Private Network to University Network System

Sharma

2021

j. of Comput. sci. res.

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“…Upon of receiving of the malicious request, the receiver might lose the data or suffer from long queuing time which may lead to receiver complete fail. is made to classify the data according to the nature of connection [13]. The attack prevention model is working according to the following steps.…”

Section: Attack Repellingmentioning

confidence: 99%

Enhancement of Virtual Private Network Security Using Machine Learning Technologies

Nagy¹,

Wali²

2021

JPS

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Network security concerns and approaches are expanded in the recent days due to the network developments and network user’s expansion. In this paper, virtual private network is suggested for network security after fulfilment of other network performance metrics such as throughput and time delay. Furthermore, in order to prevent any malicious activities with those connections under one virtual private network, artificial intelligence attack predictor is implemented. Using of various machine learning algorithms such as Naïve Bays and Random Forest, attacks can be predicted and then blocked. Another approach is used for attacks prediction called as Artificial Neural Network which represents a deep learning technology to predict any attach by learning the attacks behaviors during the training stage.

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“…The information transmitted between the two communicating sites through the encrypted tunnel cannot be read by anyone else because the system relies on several factors to secure the service provider's private network and the public network through which the remote user connects. There are three main network protocols for using VPN tunnels, namely IPsec, the Point-to-Point Tunneling Protocol (PPTP) and Layer Two (2) Tunneling Protocol (L2TP) [199,201,202]. Using VPNs, users' online activities (visited websites, for instance) are protected against disclosures to third parties.…”

Section: End-to-end Secure Communicationsmentioning

confidence: 99%

Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey

Kaaniche

Laurent

Belguith

2020

Journal of Network and Computer Applications

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Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users' physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users' privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent -economical and ethical -purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions.

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VPN IPSEC & SSL technology Security and management point of view

Cited by 12 publications

References 0 publications

Secure Remote Access IPSEC Virtual Private Network to University Network System

Secure Remote Access IPSEC Virtual Private Network to University Network System

Enhancement of Virtual Private Network Security Using Machine Learning Technologies

Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey

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