DoS Attack Detection Based on Deep Factorization Machine in SDN

Software-Defined Network(SDN)decouples the control plane of network devices from the data plane.While alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)attacks.While many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such attacks.To solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in SDN.Firstly,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection feature.Then,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS attacks.After training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is implemented.Simulation results show that our method can effectively detect DoS attacks in SDN.Compared with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values.

Experimental Validation and Application of a Phased Array Ultrasonic Testing Model on Sound Field Optimization

In safety dominant industries, nondestructive evaluation (NDE) is crucial in quality assurance and assessment. Phased array ultrasonic testing (PAUT) as one of the NDE methods is more promising compared with conventional ultrasonic testing (UT) method in terms of inspection speed and flexibility. To incorporate PAUT, the techniques should be qualified, which traditionally is performed by extensive physical experiments. However, with the development of numerical models simulating UT method, it is expected to complement or partly replace the experiments with the intention to reduce costs and operational uncertainties. The models should be validated to ensure its consistency to reality. This validation work can be done by comparing the model with other validated models or corresponding experiments. The purpose of current work focuses on the experimental validation of a numerical model, simSUNDT, developed by the Chalmers University of Technology. Validation is conducted by comparing different data presentations (A-, B- and C-scan) from experimental and simulated results with some well-defined artificial defects. Satisfactory correlations can be observed from the comparisons. After the validation, sound field optimization work aiming at retrieving maximized echo amplitude on a certain defect can be started using the model. This also reveals the flexibility of parametric studies using simulation models.