Rice tungro is a viral disease seriously affecting rice production in South and Southeast Asia. Tungro is caused by the simultaneous infection in rice of Rice tungro bacilliform virus (RTBV), a double-stranded DNA virus and Rice tungro spherical virus (RTSV), a single-stranded RNA virus. To apply the concept of RNA-interference (RNAi) for the control of RTBV infection, transgenic rice plants expressing DNA encoding ORF IV of RTBV, both in sense as well as in anti-sense orientation, resulting in the formation of double-stranded (ds) RNA, were raised. RNA blot analysis of two representative lines indicated specific degradation of the transgene transcripts and the accumulation of small molecular weight RNA, a hallmark for RNA-interference. In the two transgenic lines expressing ds-RNA, different resistance responses were observed against RTBV. In one of the above lines (RTBV-O-Ds1), there was an initial rapid buildup of RTBV levels following inoculation, comparable to that of untransformed controls, followed by a sharp reduction, resulting in approximately 50-fold lower viral titers, whereas the untransformed controls maintained high levels of the virus till 40 days post-inoculation (dpi). In RTBV-O-Ds2, RTBV DNA levels gradually rose from an initial low to almost 60% levels of the control by 40 dpi. Line RTBV-O-Ds1 showed symptoms of tungro similar to the untransformed control lines, whereas line RTBV-O-Ds2 showed extremely mild symptoms.

No abstract

No abstract

IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.

No abstract