No abstract

16 honey samples from Pakistan and two other countries were investigated for their physiochemical, biochemical, minerals, and antioxidant potential. Antioxidant activities of all honey samples were performed by using percentage inhibition of DPPH free radical, AEAC, and FRAP. 5-HMF and mineral contents were determined by HPLC and AAS, respectively. The obtained values of respective parameters, namely, pH, EC, TDS, total acidity, moisture, ash, color intensity, sugars, proline, and protein were in compliance with codex standard and recommendation of council directive by European Union. The total phenolics contents in acacia honey from Germany and jujube honey from Pakistan are similar to monofloral honey from Saudi Arabia and Yemen, respectively. The mineral contents in tested honey samples are comparable with honey from Brazil and Romania. Dark color honeys contained higher phenolic contents than light color ones and attributed to higher oxidation potential and have strong positive correlation with DPPH and FRAP.

Software Defined Network (SDN) is a flexible paradigm that provides support for a variety of data-intensive applications with real-world smart Internet of Things (IoT) devices. This emerging architecture updates with the managing ability and network control. Still, the benefits are challenging to achieve due to the presence of intruder flow into the network. The research topic of intrusion detection and prevention system (IDPS) has grasped the attention to reduce the effect of intruders. Distributed Denial of Service (DDoS) is a targeted attack that develops malicious traffic is flooded into a particular network device. These intruders also involve even with legitimate network devices, the authenticated device will be compromised to inject malicious traffic. In this paper, we investigate the involvement of intruders in three-Tier IDPS with regard to user validation, packet validation and flow validation. Not all the authentication users can be legitimate, since they are compromised, so that the major contribution is to identify all the compromised devices by knee analysis of the packets. Routers are the edge devices employed in first tier which is responsible to validate the IoT user with RFID tag and encrypted signature. Then the authenticated user"s packets are submitted into second tier with switches that validates the packets using type-II fuzzy filtering. Then the key features are extracted from packets and they are classified into normal, suspicious and malicious. The mismatched packets are analyzed in controllers which maintain two queues as suspicious and normal. Then suspicious queue packets are classified and predicted using deep learning method. The proposed work is experimented in OMNeT++ environment and the performances are evaluated in terms of intruder Detection Rate, Failure Rate, Delay, Throughput and Traffic Load.