Biblio

Deep Packet Inspection (DPI) is instrumental in investigating the presence of malicious activity in network traffic and most existing DPI tools work on unencrypted payloads. As the internet is moving towards fully encrypted data-transfer, there is a critical requirement for privacy-aware techniques to efficiently decrypt network payloads. Until recently, passive proxying using certain aspects of TLS 1.2 were used to perform decryption and further DPI analysis. With the introduction of TLS 1.3 standard that only supports protocols with Perfect Forward Secrecy (PFS), many such techniques will become ineffective. Several security solutions will be forced to adopt active proxying that will become a big-data problem considering the velocity and veracity of network traffic involved. We have developed an ABAC (Attribute Based Access Control) framework that efficiently supports existing DPI tools while respecting user's privacy requirements and organizational policies. It gives the user the ability to accept or decline access decision based on his privileges. Our solution evaluates various observed and derived attributes of network connections against user access privileges using policies described with semantic technologies. In this paper, we describe our framework and demonstrate the efficacy of our technique with the help of use-case scenarios to identify network connections that are candidates for Deep Packet Inspection. Since our technique makes selective identification of connections based on policies, both processing and memory load at the gateway will be reduced significantly.

Providing recommendations on social systems has been in the spotlight of both academics and industry for some time already. Social network giants like Facebook, LinkedIn, Myspace, etc., are eager to find the silver bullet of recommendation. These applications permit clients to shape a few certain social networks through their day-by-day social cooperative communications. In the meantime, today's online experience depends progressively on social association. One of the main concerns in social network is establishing a successful business plan to make more profit from the social network. Doing a business on every platform needs a good business plan with some important solutions such as advertise the products or services of other companies which would be a kind of marketing for those external businesses. In this study a philosophy of a system speaking to of a comprehensive structure of advertisement recommender system for social networks will be presented. The framework uses a semantic logic to provide the recommended products and this capability can differentiate the recommender part of the framework from classical recommender methods. Briefly, the framework proposed in this study has been designed in a form that can generate advertisement recommendations in a simplified and effective way for social network users.