Low-rate Denial of service (LDoS) attacks has become one of the biggest threats to the Internet, cloud computing platforms, and big data centers. As an evolutionary species of DDoS attack, LDoS attack is essentially different from the DDoS attack. DDoS attacks are the behavior of malicious blocking legitimate network traffic by destroying the targets and the infrastructure around it with huge network traffic. While, LDoS attacks are the behavior of intentional degrading the quality of TCP links by throttling TCP flows to a small fraction of its ideal rate with periodic small pulse sequence. Hence, LDoS attack has a very small flow (around 10%-20% of the background traffic), it is easy to eluding the detection of routers and counter-DoS mechanisms. We try to reveal the mechanism of the LDoS attack and attempt to figure out the generation principle of LDoS attack in this paper. We classify the LDoS attacks and existing defense methods according to time domain and frequency domain in which detection and defense are performed. Furthermore, we highlight the filter approach to defense against LDoS attack. The initial purpose of our work is to encourage researchers to study effective ways to detect and defend against LDoS attacks with innovation and aggressiveness.

A large number of facts prove that Global Navigation Satellite System (GNSS) has certain security risks and is threatened by attacks. In particular, GNSS civilian signal receivers have some vulnerability in responding to spoofing attack and jamming attack, because the format and modulation of GNSS civilian signals are public. Based on the concerns of existing GNSS spoofing scenarios, this paper reviews the research on GNSS anti-spoofing technologies from two aspects. In this paper, the strategies of spoofing attacks discovered at this stage are classified in detail, the protection effect and scheme implementation complexity of various anti-spoofing techniques against spoofing attacks are analyzed from the perspective of signal-level and data-level. More specifically, we comprehensively analyze the ability for the combined anti-spoofing methods at the signal-level and the data-level to protect from combinations of different spoofing attack. Finally, the future development trend and potential research direction of GNSS anti-spoofing technology are summarized and predicted.

As the Software Define Network (SDN) adopts centralized control logic, it is vulnerable to various types of Distributed Denial of Service (DDoS) attacks. At present, almost all the research work focuses on high-rate DDoS attack against the SDN control layer. Moreover, most of the existing detection methods are effective for high-rate DDoS attack detection of the control layer, while a low-rate DDoS attack against the SDN data layer is highly concealed, and the detection accuracy against this kind of attack is low. In order to improve the detection accuracy of the low-rate DDoS attack against the SDN data layer, this paper studies the mechanism of such attacks, and then proposes a multi-feature DDoS attack detection method based on Factorization Machine (FM). The features extracted from the flow rules are used to detect low-rate DDoS attacks, and the detection of low-rate DDoS attacks based on FM machine learning algorithms is implemented. The experimental results show that the method can effectively detect the low-rate DDoS attack against the SDN data layer, and the detection accuracy reaches 95.80 percent. Because FM algorithm can achieve fine-grained detection for low-rate DDoS attack, which provides a reliable condition for defending against such attacks. Finally, this paper proposes a defense method based on dynamic deletion of flow rules, and carries out experimental simulation and analysis to prove the effectiveness of the defense method, and the success rate of forwarding normal packets reached 97.85 percent.

As a new generation of air transportation surveillance technology, the automatic dependent surveillance - broadcast (ADS-B) system mainly completes the extraction and processing of the position information and other additional information of the aviation aircraft to form a clear and intuitive background map and trajectory. However, ADS-B broadcasts information via open and unencrypted protocols, it is vulnerable to deliberate intrusions and attacks, which poses a great security risk. This paper studies the security issues of the ADS-B system in information leakage and tampering. Starting from the vulnerability of the ADS-B system, it is divided into vulnerability based on attack intention and vulnerability based on security requirements. Various solutions for solving vulnerabilities from two aspects, secure location authentication, and secure broadcast authentication are proposed, and the solutions are compared in terms of security and feasibility. The research results show that a single solution does not fully protect the security of the ADS-B system. For example, the PKI (Public Key Infrastructure) technology and the SS (Spread Spectrum) technology can resist most attacks, but there are still deficiencies. Therefore, this paper proposes to propose a multi-layered security framework in future research work, which includes detecting and preventing different attacks in the ADS-B system.

Due to the dynamic changes of network data over time, static intrusion detection systems cannot adapt well to the behavioral characteristics of the input network data, resulting in reduced detection accuracy. In addition, continuous input data streams will bring huge challenges to resource storage and computing costs. Therefore, we propose an intrusion detection method of dynamic ensemble incremental learning (DEIL-RVM), and realize a dynamically adjusted ensemble intrusion detection model. In which a new overall misclassification probability weight value (OMPW) based on incremental set or data chunk is designed as the basis for updating the ensemble model, and it can be used to prune and replace the poor base component in the ensemble model. We presented a probabilistic decision function taking into account the posterior probability of each base RVM model dividing the sample into each category. The RVM with high sparsity is used as the base component to obtain the good balance between the accuracy, robustness and resource consumption, which can sacrifice less time and storage cost in ensemble incremental learning while achieving higher detection accuracy and stability in network data streams.

To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.