Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short Term Memory and Autoencoder

Currently, the wide spreading of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspections (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the above methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmision requirements to many Internet users using SDN capability and the potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that does not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.Comment: 27 page

First PCR Confirmed anthrax outbreaks in Ethiopia-Amhara region, 2018-2019.

BackgroundAnthrax is a disease that affects humans and animals. In Ethiopia, anthrax is a reportable disease and assumed to be endemic, although laboratory confirmation has not been routinely performed until recently. We describe the findings from the investigation of two outbreaks in Amhara region.MethodsFollowing reports of suspected outbreaks in Wag Hamra zone (Outbreak 1) and South Gondar zone (Outbreak 2), multi-sectoral teams involving both animal and public health officials were deployed to investigate and establish control programs. A suspect case was defined as: sudden death with rapid bloating or bleeding from orifice(s) with unclotted blood (animals); and signs compatible with cutaneous, ingestion, or inhalation anthrax ≤7 days after exposure to a suspect animal (humans). Suspect human cases were interviewed using a standard questionnaire. Samples were collected from humans with suspected anthrax (Outbreak 1 and Outbreak 2) as well as dried meat of suspect animal cases (Outbreak 2). A case was confirmed if a positive test was returned using real-time polymerase chain reaction (qPCR).ResultsIn Outbreak 1, a total of 49 cows died due to suspected anthrax and 22 humans developed symptoms consistent with cutaneous anthrax (40% attack rate), two of whom died due to suspected ingestion anthrax. Three people were confirmed to have anthrax by qPCR. In Outbreak 2, anthrax was suspected to have caused the deaths of two livestock animals and one human. Subsequent investigation revealed 18 suspected cases of cutaneous anthrax in humans (27% attack rate). None of the 12 human samples collected tested positive, however, a swab taken from the dried meat of one animal case (goat) was positive by qPCR.ConclusionWe report the first qPCR-confirmed outbreaks of anthrax in Ethiopia. Both outbreaks were controlled through active case finding, carcass management, ring vaccination of livestock, training of health professionals and outreach with livestock owners. Human and animal health authorities should work together using a One Health approach to improve case reporting and vaccine coverage