The payload of Distribute Denial of Service (DDoS) attack changes drastically, the manual intervention of setting warning threshold relies on experience and the signature of abnormal traffic updates not timely, an improved DDoS attack detection algorithm based on Binary Stream Point Bag of Words (BSP-BoW) model was proposed. The Stream Point (SP) was extracted automatically from current network traffic data, the adaptive anomaly detection was carried out for different topology networks, and the labor cost was reduced by decreasing frequently updated feature set. Firstly, the mean clustering was carried out for the existing attack traffic and normal traffic to look for SP in the network traffic. Then, the original traffic was mapped to the corresponding SP for formalized expression by histogram. Finally, the DDoS was detected and classified by Euclidean distance. The experimental results on public database DARPA LLDOS1.0 show that, compared with Locally Weighted Learning (LWL), Support Vector Machine (SVM), Random Tree (RT), Logistic regression analysis (Logistic), Naive Bayes (NB), the proposed algorithm has higher recognition rate of abnormal network traffic. The proposed algorithm based on BoW model has the good recognition effect and generalization ability in abnormal network traffic recognition of denial of service attack, which is suitable for the deployment in the Small Medium Enterprise (SME) network traffic equipment.

The traditional traffic flow detection system based on sensor device has complex hardware equipment and the universal traffic flow detection algorithm cannot distinguish the directions of vehicles. To resolve the above problems, a real-time vehicle monitoring algorithm for single-lane based on Digital Signal Processor (DSP) was proposed and applied to parking lot. Firstly, the background differential algorithm was used to detect vehicles on virtual detection zone and the method of mean background modeling was improved. Then, an adjacent frame two-value classification algorithm was proposed to distinguish the directions of vehicles. Finally, virtual detection zone was used for vehicle counting and the number of empty parking spots was real-time displayed on a Light Emitting Diode (LED) screen. The feasibility of the proposed algorithm was verified by the simulation experiment. The actual test results showed that the accuracy rate of the adjacent frame two-value classification algorithm for direction detection was 96.5% and the accuracy rate of parking spot monitoring algorithm was 92.2%. The proposed real-time vehicle monitoring algorithm for single-lane has high accuracy and needs less detection equipment, so it can be applied to single-lane parking lot for real-time vehicle monitoring.

The generalization of Support Vector Machines (SVM) will decline when the training data sets get imbalanced distribution. A modification method of the optimal hyperplane based on average divergence ratio according to Fisher within-class scatter was proposed to solve the problem. The normal vector of the optimal hyperplane was got after SVM training. The Fisher within-class scatter was introduced to evaluate the distribution of the two classes. On this basis, the optimal hyperplane was modified by the ratio of the average distribution scatter that was obtained according to the number of samples. The experimental results on benchmarks data sets show that the proposed method improves the classification accuracy of the class with less training data, so as to improve the SVM's generalization.