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.

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation

Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.

UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the literature.In this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for MCS.The main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data samples.Finally,we conducted extensive experiments to evaluate the UAV-ITD scheme.The results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

Predictive Model of Live Shopping Interest Degree Based on Eye Movement Characteristics and Deep Factorization Machine

In the live broadcast process,eye movement characteristics can reflect people’s attention to the product.However,the existing interest degree predictive model research does not consider the eye movement characteristics.In order to obtain the users’interest in the product more effectively,we will consider the key eye movement indicators.We first collect eye movement characteristics based on the self-developed data processing algorithm fast discriminative model prediction for tracking(FDIMP),and then we add data dimensions to the original data set through information filling.In addition,we apply the deep factorization machine(DeepFM)architecture to simultaneously learn the combination of low-level and high-level features.In order to effectively learn important features and emphasize relatively important features,the multi-head attention mechanism is applied in the interest model.The experimental results on the public data set Criteo show that,compared with the original DeepFM algorithm,the area under curve(AUC)value was improved by up to 9.32%.