When people use social networks, they often fall prey to a clickbait scam. The scammer attempts to create a striking headline that attracts the majority of users and attaches a link. The user follows the link and can be redirected to a fraudulent resource where the user easily loses personal data. To solve this problem, a Blockchain-enabled deep recurrent neural network (BDRNN) is proposed to detect the nature safe and malicious clickbait from the contents. The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. The analysis of clickbait and source rating phase helps to analyze different sources to detect the clickbait and also rating the content-sources. To achieve the clickbait analysis and source rating, the detection of blacklisted/white-listed source and source rating check algorithms are introduced. The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. The multi-layered clickbait detection is main phase of the proposed BDRNN that consists of three models: content-to-vector model (layer-1), deep neural network model(layer-2), and Blockchainenabled malicious content detection model (layer-3). These models collectively detect the malicious and safe clickbait from the contents. The extensive experiments are conducted to determine the effectiveness of the proposed BDRNN model and compared with the existing state-of-the-art neural network models designed for clickbait detection, and the result demonstrates that the proposed BDRNN model outperforms the counterparts from the, accuracy, link detection, memory usage, analogous perspectives, and attacker's successful content capturing rate.
In this work we study some methods of storing data in the cloud for a given time using secret sharing technology. Such problems are especially relevant in the context of the rapid development of the internet of things (IoT). Chips, smart cards and other physically small devices, as a rule, have significant memory limitations, so there is a need to use cloud storage as an auxiliary tool for secure data storage. We present method for secure storing data in distributed servers, provided that servers remote from each other do not collude with each other. In the paper we also consider two diferent methods for storing data using various cryptographic solutions, such as Shamir's secret sharing method, El Gamal scheme, diffie-hellman key distribution protocol.
We study new methods of secure cloud processing of big data when solving applied computationally-complex problems with secret parameters. This is one of the topical issues of secure client-server communication. As part of our research work, we model the client-server interactions: we give specific definitions of such concepts as “solvable by the protocol”, “secure protocol”, “correct protocol”, as well as actualize the well-known concepts-“active attacks” and “passive attacks”. First, we will outline the theory and methods of secure outsourcing for various abstract equations with secret parameters, and then present the results of using these methods in solving applied problems with secret parameters, arising from the modeling of economic processes. Many economic tasks involve processing a large set of economic indicators. Therefore, we are considering a typical economic problem that can only be solved on very powerful computers.
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