The Mobile Ad-hoc Network (MANET) is a dynamic topology that provides a variety of executions in various disciplines. The most sticky topic in organizational fields was MANET protection. MANET is helpless against various threats that affect its usability and accessibility. The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organization's execution and reliability by dropping all approaching packages via the noxious node. The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node. Ad-hoc On-demand Distance Vector (AODV) is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center. We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits. The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology. We implement the suggested method's performance with the help of Network Simulator (NS)-3 simulation models. Furthermore, the proposed approach comes exceptionally near to the original AODV, absent black holes in terms of bandwidth, end-to-end latency, error rate, and delivery ratio.
In recent years mining of data from social media is attracting more attention due to the explosion in the growth of Big Data. In security, Big Data deals with collection of huge digital information for analyzing, visualizing and to draw the insights for the prediction & prevention of cyber attacks. The Big Data mined about an enterprise from the data cloud, if properly analyzed reveals the private information which is highly risky. Maintaining the privacy of users of social media is the major challenge with respect to the security issues. As the data is generally stored in a data cloud, a boundary of trust must be established between the social media users and the data bank owners. Hence there is requirement of developing an efficient protocol for sharing of data. To secure the sensitive information of the user, data mining can be used along with an effective algorithm. This paper proposes the technique of code inline parsing to make the data more secure from the attacks & cyber hacks along with the SQL injections such that the data on the social media is secured. The proposed method secures the platform of Big Data which protects the user’s sensitive information.
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