Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
In a fog computing environment, lots of devices need to be authenticated in order to keep the platform being secured. To solve this\nproblem, we turn to blockchain techniques. Unlike the identification cryptographic scheme based on elliptic curves, the\nproposed 2-adic ring identity authentication scheme inherits the high verification efficiency and high key distribution of\nsequence ciphers of 2-adic ring theory, and this algorithm adds identity hiding function and trading node supervision\nfunction by design. The main designed application scenario of this solution is applicable to the consortium blockchain,\nand the master nodes are mutually trusting cooperative relations. The node transaction verification and block generation\nconsensus algorithm designed in this solution can be implemented in a set of algorithms, which has higher verification\nefficiency and easier to be deployed than other solutions. This scheme can be widely used in the fog computing environment....
Group activities on social networks are increasing rapidly with the development of mobile devices and IoT terminals, creating a\nhuge demand for group recommendation. However, group recommender systems are facing an important problem of privacy\nleakage on userâ??s historical data and preference. Existing solutions always pay attention to protect the historical data but ignore\nthe privacy of preference. In this paper, we design a privacy-preserving group recommendation scheme, consisting of a\npersonalized recommendation algorithm and a preference aggregation algorithm. With the carefully introduced local differential\nprivacy (LDP), our personalized recommendation algorithm can protect userâ??s historical data in each specific group. We also\npropose an Intra-group transfer Privacy-preserving Preference Aggregation algorithm (IntPPA). IntPPA protects each group\nmemberâ??s personal preference against either the untrusted servers or other users. It could also defend long-term observation\nattack. We also conduct several experiments to measure the privacy-preserving effect and usability of our scheme with some\nclosely related schemes. Experimental results on two datasets show the utility and privacy of our scheme and further illustrate its\nadvantages....
With the explosive growth of data generated by the Internet of Things (IoT) devices, the traditional cloud computing model by\ntransferring all data to the cloud for processing has gradually failed to meet the real-time requirement of IoT services due to high\nnetwork latency. Edge computing (EC) as a new computing paradigm shifts the data processing from the cloud to the edge nodes\n(ENs), greatly improving the Quality of Service (QoS) for those IoT applications with low-latency requirements. However,\ncompared to other endpoint devices such as smartphones or computers, distributed ENs are more vulnerable to attacks for\nrestricted computing resources and storage. In the context that security and privacy preservation have become urgent issues for\nEC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges. The powerful\nlearning ability of AI enables the system to identify malicious attacks more accurately and efficiently. Meanwhile, to a certain\nextent, transferring model parameters instead of raw data avoids privacy leakage. In this paper, a comprehensive survey of the\ncontribution of AI to the IoTsecurity in EC is presented. First, the research status and some basic definitions are introduced. Next,\nthe IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services\nwith AI is then presented. In the end, the open issues and challenges on the application of AI in IoT services based on EC\nare discussed....
With the development of communication systems, information securities remain one of the main concerns for the last few years. The\nsmart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios. Intruders are trying to attack\nthe network and capture the organizationâ??s important information for its own benefits. Intrusion detection is a way of identifying\nsecurity violations and examining unwanted occurrences in a computer network. Building an accurate and effective identification system\nfor intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication. In the\nproposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high\nsecurity of the network. A convolution neural network based approach is followed for the feature classification and malicious data\nidentification purposes. In the end, comparative results are generated after evaluating the performance of the proposed algorithm to\nother rival algorithms in the proposed field. These comparative algorithms were FGSM, JSMA, C&W, and ENM. After evaluating the\nperformance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different\nparametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores. These\nresults reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the\napplicability of the proposed algorithms in the network security field....
Components are the significant part of a system which plays an important role in the functionality of the system. Components\nare the reusable part of a system which are already tested, debugged, and experienced based on the previous practices. A new\nsystem is developed based on the reusable components, as reusability of components is recommended to save time, effort, and\nresources as such components are already made. Security of components is a significant constituent of the system to maintain\nthe existence of the component as well as the system to function smoothly. Component security can protect a component\nfrom illegal access and changing its contents. Considering the developments in information security, protecting the\ncomponents becomes a fundamental issue. In order to tackle such issues, a comprehensive study report is needed which can\nhelp practitioners to protect their system. The current study is an endeavor to report some of the existing studies regarding\ncomponent security evaluation based on multicriteria decision and machine learning algorithms in the popular\nsearching libraries....
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