A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS

ABHD11-AS1: An Emerging Long Non-Coding RNA (lncRNA) with Clinical Significance in Human Malignancies

The aberrant expression of lncRNAs has been linked to the development and progression of different cancers. One such lncRNA is ABHD11 antisense RNA 1 (ABHD11-AS1), which has recently gained attention for its significant role in human malignancies. ABHD11-AS1 is highly expressed in gastric, lung, breast, colorectal, thyroid, pancreas, ovary, endometrium, cervix, and bladder cancers. Several reports highlighted the clinical significance of ABHD11-AS1 in prognosis, diagnosis, prediction of cancer progression stage, and treatment response. Significantly, the levels of ABHD11-AS1 in gastric juice had been exhibited as a clinical biomarker for the assessment of gastric cancer, while its serum levels have prognostic potential in thyroid cancers. The ABHD11-AS1 has been reported to exert oncogenic effects by sponging different microRNAs (miRNAs), altering signaling pathways such as PI3K/Akt, epigenetic mechanisms, and N6-methyladenosine (m6A) RNA modification. In contrast, the mouse homolog of AHD11-AS1 (Abhd11os) overexpression had exhibited neuroprotective effects against mutant huntingtin-induced toxicity. Considering the emerging research reports, the authors attempted in this first review on ABHD11-AS1 to summarize and highlight its oncogenic potential and clinical significance in different human cancers. Lastly, we underlined the necessity for future mechanistic studies to unravel the role of ABHD11-AS1 in tumor development, prognosis, progression, and targeted therapeutic approaches