Distributed Denial of Service (DDoS) attack is a major threat impeding service to legitimate requests on any network. Although the first DDoS attack was reported in 1996, the complexity and sophistication of these attacks has been ever increasing. A 2 TBps attack was reported in mid-August 2020 directed towards critical infrastructure, such as finance, amidst the COVID-19 pandemic. It is estimated that these attacks will double, reaching over 15 million, in the next 2 years. A number of mitigation schemes have been designed and developed since its inception but the increasing complexity demands advanced solutions based on emerging technologies. Blockchain has emerged as a promising and viable technology for DDoS mitigation. The inherent and fundamental characteristics of blockchain such as decentralization, internal and external trustless attitude, immutability, integrity, anonymity and verifiability have proven to be strong candidates, in tackling this deadly cyber threat. This survey discusses different approaches for DDoS mitigation using blockchain in varied domains to date. The paper aims at providing a comprehensive review, highlighting all necessary details, strengths, challenges and limitations of different approaches. It is intended to serve as a single platform to understand the mechanics of current approaches to enhance research and development in the DDoS mitigation domain.
The inverse degree index is a topological index first appeared as a conjuncture made by computer program Graffiti in 1988. In this work, we use transformations over graphs and characterize the inverse degree index for these transformed families of graphs. We established bonds for different families of
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-vertex connected graph with pendent paths of fixed length attached with fully connected vertices under the effect of transformations applied on these paths. Moreover, we computed exact values of the inverse degree index for regular graph specifically unicyclic graph.
Graph theory is a discrete branch of mathematics for designing and predicting a network. Some topological invariants are mathematical tools for the analysis of connection properties of a particular network. The Cellular Neural Network (CNN) is a computer paradigm in the field of machine learning and computer science. In this article we have given a close expression to dominating invariants computed by the dominating degree for a cellular neural network. Moreover, we have also presented a 3D comparison between dominating invariants and classical degree-based indices to show that, in some cases, dominating invariants give a better correlation on the cellular neural network as compared to classical indices.
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