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Bahaa, A., Abdelaziz, A., Sayed, A., Elfangary, L., & Fahmy, H. (2021). Monitoring real time security attacks for iot systems using devsecops: A systematic literature review. Information (Switzerland), 12(4), 1-23. [154]. https://doi.org/10.3390/info12040154 In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring. publishersversion published

Renoprotectors are highly demanded due to environmental nephrotoxic factors. P. longifolia leaves extract alleviating effect was assessed in nephritic-induced rats by whole body shot dose of γ-radiation. Many biomarkers were detected using several assays. Renohistopathological examinations were performed. Moreover, the extract phytoconstituents were identified using spectroscopic analysis. In-vitro anti-inflammatory activity of some compounds was examined using histamine release assay. Post-irradiation treatment with the extract significantly ameliorated all elevated biomarker levels. Creatinine and urea were adjusted, TGF-β/Smad signaling was suppressed causing down-regulation to microRNA-21. Nitric oxide, reactive oxygen species, glutathione and kidney injury molecule-1 were normalized in comparison with the γ-irradiated group. The renohistopathological analysis was consistent with the biochemical study. Phytochemical analysis resulted in the isolation of two diterpenoids (γ-methoxybutenolide clerodane diterpene and 16(R/S)-hydroxycleroda-3,13-dien-16,15-olide-2-one), aporphine alkaloid (anonaine) and flavonol (kaempferol-3-O-rutinoside). The latter two showed moderate anti-histaminic activities. Our results indicated that P. longifolia reduced oxidative stress and nephropathy in rats due to its anti-inflammatory principles.