The participation of ordinary devices in networking has created a world of connected devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every field. There are no definite protocols or standards for IoT communication, and most of the IoT devices have limited resources. Enabling a complete security measure for such devices is a challenging task, yet necessary. Many lightweight security solutions have surfaced lately for IoT. The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world. It is also hard to deploy any traditional security protocol on resource-constrained IoT devices. Softwaredefined networking introduces a centralized control in computer networks. SDN has a programmable approach towards networking that decouples control and data planes. An SDN-based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT. The proposed intrusion detection system does not burden the IoT devices with security profiles. The proposed work is executed on the simulated environment. The results of the simulation test are evaluated using various matrices and compared with other relevant methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Ulcerative colitis or Crohn's illness patients are in danger of colon cancer due to chronic inflammation, resulting from the reaction of the immune system to bacterial disease caused by genetic alterations in the colonic mucosa. Somatic cells gain genomic changes, such as TP53 that regulates MUC2 production and APC alterations linked with 𝛽-catenin and MUC1 contribution in the slight proliferation of cells. Mathematical modeling describes developmental modifications and uses the phrases to link parameter to curves of age-dependent incidence of epidemiological cancer. By using the long-lasting investigation of IBD patients to gather the genomic estimations for increasingly exact computations of IBD-explicit developmental parameters as initiation, birth, and death. Colon cancer genetic trajectory follows the structure of the composition of functions that leads to malignancies. Models of population level can be utilized to consolidate epidemiological information and in this manner describe malignant growth advancement in a population with IBD.

Most of the cancer growth models have described the exponential growth patterns at the very initial stage with low cell population density. Eventually, decreasing the tumor growth rate at higher cell population densities because of deficiency in resources such as space and nutrients. However, recent studies at clinical and preclinical investigations of cancer initiation or reappaearance showed a population dynamics evincing that the growth rate increases as cell number increases. Hence, showing behaviour analogous to cooperative mechanism in the ecosystem and ecological effect called Allee effect. Based on these observations with two arguments i.e. change in initial population and growth rate. In this paper, the novel mathematical model of tumor growth kinetics with Allee effect under fuzzy environment is proposed. In this model the Generalized Hukuhara derivative approach is utilized to solve the fuzzy differential equations. Moreover, it is showen that the change in initial value and growth rate affects the cell density with the Allee effect under the fuzzy environment. Finally the superiority of model has been showen with the help of numerical simulation.

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