Research on College Frustrated Students＇ Network Mentality and Behaviors

Network has not only become a habit and lifestyle for university student, but also brought all sorts of ethical misconducts and ethical issues in society. Based on the analysis of college students＇ frustrations, this paper explores the causes of network behavior anomie for college students, which mainly include： dissatisfaction in real communication, game addiction to the network, craving online pornography, and hooking on online shopping. In addition, it also investigates the ways to wipe out mental frustration in such a cyber era. These ways mainly are to strenzthen online education and management, to make psychological counseling, and to carry on frustration education.

Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks

With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.

Research on behavior recognition algorithm based on SE-I3D-GRU network

In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined with 3 D Inception network(I3 D) and gated recurrent unit(GRU) network is proposed.The algorithm first expands the Inception module to three-dimensional,and builds a network based on the three-dimensional module,and expands SENet to three-dimensional,making it an attention mechanism that can pay attention to the three-dimensional channel.Then SENet is introduced into the 13 D network,named SE-I3 D,and SENet is introduced into the CRU network,named SE-GRU.And,SE-13 D and SE-GRU are merged,named SE-13 D-GRU.Finally,the network uses Softmax to classify the results in the UCF-101 dataset.The experimental results show that the SE-I3 D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.

Trust evolvement method of Web service combination based on network behavior

Based on the problem that the service entity only has the partial field of vision in the network environment,a trust evolvement method of the macro self-organization for Web service combination was proposed.In the method,the control rule of the trust degree in the Dempster-Shafer(D-S)rule was utilized based on the entity network interactive behavior,and a proportion trust control rule was put up.The control rule could make the Web service self-adaptively study so as to gradually form a proper trust connection with its cooperative entities and to improve the security performance of the whole system.The experimental results show that the historical successful experience is saved during the service combination alliance,and the method can greatly improve the reliability and success rate of Web service combination.

Efficient Feature Extraction Using Apache Spark for Network Behavior Anomaly Detection

Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical features of traffic volume. However, this approach is not sufficient to reflect the communication pattern features. A different approach is required to detect anomalous behaviors that do not exhibit traffic volume changes, such as low-intensity anomalous behaviors caused by Denial of Service/Distributed Denial of Service （DoS/DDoS） attacks, Internet worms and scanning, and BotNets. We propose an efficient traffic feature extraction architecture based on our proposed approach, which combines the benefit of traffic volume features and network communication pattern features. This method can detect low-intensity anomalous network behaviors and conventional traffic volume anomalies. We implemented our approach on Spark Streaming and validated our feature set using labelled real-world dataset collected from the Sichuan University campus network. Our results demonstrate that the traffic feature extraction approach is efficient in detecting both traffic variations and communication structure changes. Based on our evaluation of the MIT-DRAPA dataset, the same detection approach utilizes traffic volume features with detection precision of 82.3% and communication pattern features with detection precision of 89.9%. Our proposed feature set improves precision by 94%.

An Unknown Trojan Detection Method Based on Software Network Behavior

Aiming at the difficulty of unknown Trojan detection in the APT flooding situation, an improved detecting method has been proposed. The basic idea of this method originates from advanced persistent threat （APT） attack intents： besides dealing with damaging or destroying facilities, the more essential purpose of APT attacks is to gather confidential data from target hosts by planting Trojans. Inspired by this idea and some in-depth analyses on recently happened APT attacks, five typical communication characteristics are adopted to describe application’s network behavior, with which a fine-grained classifier based on Decision Tree and Na ve Bayes is modeled. Finally, with the training of supervised machine learning approaches, the classification detection method is implemented. Compared with general methods, this method is capable of enhancing the detection and awareness capability of unknown Trojans with less resource consumption.

Constitutive modeling of compression behavior of TC4 tube based on modified Arrhenius and artificial neural network models

Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.

An Anomalous Behavior Detection Model in Cloud Computing

This paper proposes an anomalous behavior detection model based on cloud computing. Virtual Machines （VMs） are one of the key components of cloud Infrastructure as a Service （laaS）. The security of such VMs is critical to laaS security. Many studies have been done on cloud computing security issues, but research into VM security issues, especially regarding VM network traffic anomalous behavior detection, remains inadequate. More and more studies show that communication among internal nodes exhibits complex patterns. Communication among VMs in cloud computing is invisible. Researchers find such issues challenging, and few solutions have been proposed--leaving cloud computing vulnerable to network attacks. This paper proposes a model that uses Software-Defined Networks （SDN） to implement traffic redirection. Our model can capture inter-VM traffic, detect known and unknown anomalous network behaviors, adopt hybrid techniques to analyze VM network behaviors, and control network systems. The experimental results indicate that the effectiveness of our approach is greater than 90%, and prove the feasibility of the model.

Design,analysis,and neural control of a bionic parallel mechanism

Although the torso plays an important role in the movement coordination and versatile locomotion of mammals,the structural design and neuromechanical control of a bionic torso have not been fully addressed.In this paper,a parallel mechanism is designed as a bionic torso to improve the agility,coordination,and diversity of robot locomotion.The mechanism consists of 6-degree of freedom actuated parallel joints and can perfectly simulate the bending and stretching of an animal’s torso during walking and running.The overall spatial motion performance of the parallel mechanism is improved by optimizing the structural parameters.Based on this structure,the rhythmic motion of the parallel mechanism is obtained by supporting state analysis.The neural control of the parallel mechanism is realized by constructing a neuromechanical network,which merges the rhythmic signals of the legs and generates the locomotion of the bionic parallel mechanism for different motion patterns.Experimental results show that the complete integrated system can be controlled in real time to achieve proper limb-torso coordination.This coordination enables several different motions with effectiveness and good performance.