BIOCHEMICAL EVALUATION OF THREE MEDICINAL TAXA OF GENUS SESBANIA IN MAHARASHTRA

The genus Sesbania belongs to the family Leguminosae and its subfamily is Papilionoideae.There are four subgenera of which Sesbania and Agati are of agriculture value.  The seasonal variation  of  proteins  and  amino  acids  have  been  investigated  in  leaf , bark  and  wood  of  Sesbania rostrata , Sesbania exaltata and Sesbania sesban are the medicinal  plants in Maharashtra. Comparative  account  of  protein  content  of  leaves  of  three  tree  species  revealed  that  Sesbania exaltata were  rich  in  protein(  range from 3.34 to 3.81 mg / g dry wt .) than  Sesbania rostrata (  range from 3.60 to 3.72 mg / g dry wt .) and Sesbania sesban (  range from  2.31 to 2.55 mg / g dry wt .) . Amino  acids  content  of  leaves  of  three  tree  species  revealed  that  Sesbania exaltata were  rich  in  amino acid (  range from  2.47 to 2.67 mg / g dry wt .)  than  Sesbania rostrata (  range from  2.29 to 2.46 mg / g dry wt .) and Sesbania sesban (  range from  1.74 to 1.89 mg / g dry wt .). Key words: Protein , amino  acid , endangered  medicinal  tax

Edge-based blockchain enabled anomaly detection for insider attack prevention in Internet of Things

YesInternet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long-term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge-based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence-based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success.Petroleum Technology Development Fund(PTDF) Nigeria, Grant/Award Number:PTDF/ED/PHD/TYM/858/1