# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 5 Conclusions and Further Work
\begin{tabular}{l l} \(X\), \(Y\) & Two different feature vector categories. \\ \(p_{1}(x)\), \(p_{2}(y)\) & Respective marginal probability distribution functions of \(x\) and \(y\). \\ \(p(x,y)\) & Joint probability distribution function of \(x\) and \(y\). \\ \(W\) & Correlation coefficient of features and categories. \\ \(F\) & A feature set. \\ \(S\) & A feature subset of \(F\). \\ \(c\) & Target category. \\ \(R\) & Redundancy coefficient between two features. \\ \(F^{{}^{\prime}}_{AlexNet}\), \(F^{{}^{\prime}}_{VGG16}\), \(F^{{}^{\prime}}_{LSTM}\) & Feature vectors selected from \(F_{AlexNet}\), \(F_{VGG16}\), \(F_{LSTM}\) by mRMR \\  & algorithm respectively. \\ \(F^{{}^{\prime}}_{1^{\prime}}\), \(F^{{}^{\prime}}_{2^{\prime}}\), \(\ldots\), \(F^{{}^{\prime}}_{1000}\) & 100 specific feature vectors selected from each neural network. \\ \(F_{Fusion}\) & Fused vector after the filtering of the three channels. \\ \(f_{Known}\) & Set of known features in the feature database. \\ \(f^{1}_{K^{\prime}}\), \(f^{2}_{K^{\prime}}\), \(\ldots\), \(f^{n}_{K}\) & Known features. \\ \(y^{1}_{K^{\prime}}\), \(y^{2}_{K^{\prime}}\), \(\ldots\), \(y^{n}_{K}\) & Labels of known features. \\ \(n\) & Number of known features in the feature database. \\ \(f_{Unknown}\) & Set of unknown features to be recognized. \\ \(f^{1}_{U^{\prime}}\), \(f^{2}_{U^{\prime}}\), \(\ldots\), \(f^{n}_{U}\) & Known features. \\ \(m\) & Number of unknown features to be recognized. \\ \(\epsilon\) & Small neighborhood radius. \\ \(\mu\) & Larger neighborhood radius. \\ \(\mathcal{N}_{\epsilon}(f)\) & \(\epsilon\) neighborhood of feature \(f\). \\ \(\mathcal{N}_{\mu}(f)\) & \(\mu\) neighborhood of feature \(f\). \\ \(\tau\) & Density radio threshold. \\ \(T\) & Distance threshold. \\ \(C\) & Set of known class labels. \\ \(Class_{1}\), \(Class_{2}\), \(\ldots\), \(Class_{N}\) & Known class labels. \\ \(N\) & Number of known traffic categories. \\ \(C^{\prime}\) & New unknown class labels. \\ \(Noi\) & Noise. \\ \(\mathcal{F}\) & Feature database. \\ \(W\) & Objective function of the multi-class SVM. \\ \(w\) & One-versus-rest hyperplane vector. \\ \(b\) & Bias vector. \\ \(e\) & Slack variable. \\ \(\Phi(\cdot)\) & Kernel function. \\ \(\psi\) & Penalty factor. \\ \(s\) & Current state variable. \\ \(\mathcal{F}^{\prime}\) & Updated feature database. \\ \(N^{\prime}\) & Number of updated traffic categories. \\ \(\xi_{s+1}\) & Weight of the old classification plane. \\ \(\lambda_{s+1}\) & Knowledge weight of the old feature base. \\ \(Acc\) & Accuracy. \\ \(TPR\) & True positive rate. \\ \(FPR\) & False positive rate. \\ \(FTF\) & A metric that combines \(TPR\) and \(FPR\). \\ \(F1\) & A metric that combines precision and recall rate. \\ \(TP\) & True positive. \\ \(FP\) & False positive. \\ \(TN\) & True negative. \\ \(FN\) & False negative. \\ \(kappa\) & A metric for consistency. \\ \(p_{o}\) & Overall classification accuracy. \\ \(p_{e}\) & Expected agreement rate. \\ \(AUC\) & Area under ROC curve. \\ \(Openness\) & Ratio of unknown traffic to known traffic in testing dataset. \\ \(C_{T}\) & Number of known classes used in training dataset. \\ \(C_{E}\) & Number of all traffic classes in testing dataset. \\ \end{tabular}