Neural Network Based Detection Model Research Articles

Detection of Distributed Denial of Service (DDoS) attacks using convolutional neural networks

The rapid evolution of the Internet has brought tremendous benefits to the world at large. To effectively leverage on the importance of the internet, there is the need for a secured and reliable network. But currently, there are lots of network attacks against network infrastructures. One of such attack is the Distributed Denial of Service (DDoS) attacks which is an attempt by hackers to deny authorized users access internet service availability using many attack machines. In this paper, a Convolutional Neural Network based detection model is proposed to proffer solution to the challenges of DDoS attacks. The dataset for the modelling was sourced from the KDD Cup-99 Dataset. The evaluation of the experiment conducted was based on three standard metrics of accuracy, sensitivity and specificity. The experimental results showed that the developed model had an accuracy of 99.72%, specificity of 99.69% and sensitivity of 99.71%. Furthermore, the performance of the model was compared with other existing traditional learning models, the results indicated that the model presented in this work performed significantly better.

Contact-free wheat mildew detection with commodity wifi

Mildew is recognized as one of the most critical causes of the damages in food storage. Due to the complex operation and high cost, many advanced detection instruments cannot be widely deployed, while the detection of grain mildew are mostly carried out manually (i.e., inspection by experts) nowadays with very low efficiency. To address this problem, we present a non-destructive, non-intrusive, and low-cost mildew detection system for stored wheat implemented with off-the-shelf WiFi devices, which represents a new application of the Internet of Things (IoT) for smart agriculture. In this paper, we introduce the impact of wheat mildew in stored food, and demonstrate that it is viable to utilize WiFi Channel State Information (CSI) amplitude for detection of mildew in stored wheat. Next, we propose the MiFi system, which comprises sensing of WiFi CSI data, data preprocessing, a radial basis function (RBF) neural network-based detection model, and mildew detection. We conduct extensive experiments to validate the performance of the proposed MiFi system using real grain samples. The results show that MiFi achieves an average detection accuracy of over 90% in both line-of-sight (LOS) and non-line-of-sign (NLOS) scenarios, as well as a comparable detection performance as manual detection by an expert.