Low earth orbit (LEO) satellite networks can be used as supplementary networks for ground-based networks to provide network services for complex areas. However, the satellite networks characteristics such as open channels, a dynamic network topology, and a large number of user terminals can lead to problems of security, quality of service (QoS), and network control center overloading. This paper presents a dynamic access authentication protocol based on Token and the satellite orbit predictability and accurate clock synchronization to construct pre-authentication vectors which implement user random access and seamless switching. Simulations show that this protocol satisfies the security requirements with low handover delay and low computational costs for efficient and secure access authentication for users in LEO satellite networks.

The development of computers and networking has been accompanied by exponential increases in the amount of malware which greatly threaten cyber space applications. This study combines the reverse analysis of malicious codes with a visualization method in a method that visualizes operating code sequences extracted from the ".text" section of portable and excutable (PE) files. This method not only improves the efficiency of malware, but also solves the difficulty of simHash similarity measurements. Tests show that this method identifies more effective features with higher information densities. This method is more efficient and has better classification accuracy than traditional malware visualization methods.

The accuracy and efficiency of program analyses are hindered by very large control flow graphs (CFG). This paper presents an improved GN (Girvan-Newman) algorithm for CFG division. The node weights are added as parameters to the betweenness calculation to better balance the subgraph sizes with the sizes controlled dynamically to terminate the algorithm at a suitable time to improve the execution efficiency. Then, the binary programs indicated by the CFGs are analyzed using the angr tool. The improved GN algorithm, K-means algorithm, spectral clustering algorithm and naive aggregation algorithm were all tested with the results showing the improved GN algorithm provided the best modularity and subgraph size balance.

Methods are needed to protect a person's privacy while monitoring their location. This paper presents a scheme for collecting location data based on local differential privacy. First, a multi-phase randomized response is used to collect the location data based on their local differential privacy. Then, the density of a certain section is estimated using the statistical method and expectation maximization (EM) to analyze the location data. The scheme guarantees that an untrustworthy data collector can still obtain the location statistics without direct access to the original data. Extensive tests verify that EM provides better privacy protection and better utility than the statistical method with limited location data. The results of the statistical method and EM are similar with abundant location data.

Distributed denial-of-service (DDoS) attacks, which are becoming increasingly serious, have become one of the biggest threats to network security. Traditional defense mechanisms such as instruction detection, traffic filtering and multiple authentication are limited to static networks, which leads to obvious drawbacks. Software-defined networking (SDN) is a typical dynamic network that provides defenses against DDoS. The existing SDN-based DDoS protection solutions are still in development with many problems that need improvement. A DDoS detection scheme combined with trigger detection and in-depth detection is given here to shorten the detection period with low system overhead. A low-overhead, coarse-grained trigger detection algorithm is integrated with a precise, fine-grained, in-depth detection algorithm to reduce system complexity while ensuring high detection accuracy. An SDN DDoS detection system has been implemented on the Mininet platform to test and evaluate the system. The test show that the detection system has low system overhead, high detection accuracy, and strong practical value.

This paper presents a software defined network (SDN) based defense mechanism to detect and mitigate a new distributed denial of service (DDoS) attack named Crossfire. An SDN traffic-level centralized monitoring and shunt control model was defined based on the Crossfire characteristics for the defense mechanism. The SDN re-routing strategy was used to resolve the congestion load of the attacked link with flexible traffic scheduling used to alleviate the congestion and avoid critical link interruption that could seriously interfere with network service. The SDN mobile target defense mechanism was used to dynamically adjust the network configuration and network behavior to induce the attacker to adjust the attack traffic; thereby improving the attack detection efficiency of the bait server. Tests show that this mechanism can effectively defend against Crossfire attacks and that the SDN defense mechanism and rerouting strategy does not require significant overhead.

This paper presents an intrusion detection method based on a deep convolutional neural network (dCNN) to improve the detection accuracy and efficiency of intrusion detection systems. This method uses deep learning to design the deep intrusion detection model including the tanh, Dropout, and Softmax algorithms. The method first transforms the one-dimensional raw intrusion data into two-dimensional "image" data using data padding. Then, the method uses dCNN to learn effective features from the data and the Softmax classifier to generate the final detection result. The method was implemented on a Tensorflow-GPU and evaluated on a Nvidia GTX 1060 3 GB GPU using the ADFA-LD and NSL-KDD datasets. Tests show that this method has shorter training time, improved detection accuracy, and lower false alarm rates. Thus, this method enhances the real-time processing and detection efficiency of intrusion detection systems.

During pre-delivery inspections of lithium ion batteries and the staggered utilization phase after elimination, the battery self-discharge rate needs to be measured to confirm the uniformity of the lithium ion batteries. This study analyzed the lithium ion battery self-discharge mechanisms, the key factors affecting the self-discharge, and the two main methods for measuring the self-discharge rate. The deposit method for measuring the self-discharge rate stores the batteries for a long time, which is very time consuming. The dynamic method measures the self-discharge rate over a short period based on an equivalent circuit model which significantly shortens the measuring time. The dynamic method needs to be further optimized to realize rapid measurements.

High-strength steel is an effective way to reduce automobile masses, with the forming limit curve of the steel as an important tool for evaluating the sheet metal forming. A forming limit curve simulation prediction method for the anisotropic high-strength steel Q490C is given here based on the maximum punch force criterion to reduce experiment costs and shorten the development cycle. The results are then fit to correlations. The simulations agree well with experimental data. The simulated curve intercept agrees well with the Keeler's formula. Thus, the finite element predictions of the forming limit curve used in this paper can accurately predict the forming limit curve for anisotropic sheets, which lays a foundation for blank stamping forming.

Key parameter interval changes are used to quantify start-ups uncertainty and to deduce the discounted cash flow (DCF) and a compound real options model based on fuzzy theory. This research shows that the fuzzy real option method improves the DCF by giving the range of values with a fuzzy uncertainty to make more reasonable valuations. The fuzzy parameter sensitivity analysis shows that the start-ups uncertainty negatively correlates with the probability, the minimum value positively correlates with the left width, and the maximum value positively correlates with the right width. Analyses of the start-ups values for different situations can improve the investment decision accuracy. A case study further verifies the effectiveness of the fuzzy real options method in multi-stage investments for start-ups.