Biblio

Distributed Denial-of-Service (DDoS) attack is a long-lived attack that is hugely harmful to the Internet. In particular, the emergence of a new type of DDoS called Link Flooding Attack (LFA) makes the detection and defense more difficult. In LFA, the attacker cuts off a specific area by controlling large numbers of bots to send low-rate traffic to congest selected links. Since the attack flows are similar to the legitimate ones, traditional schemes like anomaly detection and intrusion detection are no longer applicable. Blockchain provides a new solution to address this issue. In this paper, we propose a blockchain-based LFA detection scheme, which is deployed on routers and servers in and around the area that we want to protect. Blockchain technology is used to record and share the traceroute information, which enables the hosts in the protected region to easily trace the flow paths. We implement our scheme in Ethereum and conduct simulation experiments to evaluate its performance. The results show that our scheme can achieve timely detection of LFA with a high detection rate and a low false positive rate, as well as a low overhead.

Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.

In order to improve the reliability of power supply in adjacent hydropower stations, the auxiliary power systems of the two stations are connected through a contact transformer. The magnetizing inrush current generated by the connecting transformer of a hydropower station has the characteristics of high frequency, strong energy, and multi-coupling. The harm caused by the connecting transformer is huge. In order to prevent misoperation during the closing process of the connecting transformer, this article aims at the problem of setting the switching current of the connecting transformer of the two hydropower stations, and establishes the analysis model of the excitation inrush current with SimPowerSystem software, and carries out the quantitative simulation calculation of the excitation inrush current of the connecting transformer. A setting strategy for overcurrent protection of tie transformers to suppress the excitation inrush current is proposed. Under the conditions of changing switch closing time, generator load, auxiliary transformer load, tie transformer core remanence, the maximum amplitude of the excitation inrush current is comprehensively judged Value, and then achieve the suppression of the excitation inrush current, and accurately determine the protection setting of the switch.

The key feature of NDN is in-network caching that every router has its cache to store data for future use, thus improve the usage of the network bandwidth and reduce the network latency. However, in-network caching increases the security risks - cache pollution attacks (CPA), which includes locality disruption (ruining the cache locality by sending random requests for unpopular contents to make them popular) and False Locality (introducing unpopular contents in the router's cache by sending requests for a set of unpopular contents). In this paper, we propose a machine learning method, named Triangle Area Based Multivariate Correlation Analysis (TAB-MCA) that detects the cache pollution attacks in NDN. This detection system has two parts, the triangle-area-based MCA technique, and the threshold-based anomaly detection technique. The TAB-MCA technique is used to extract hidden geometrical correlations between two distinct features for all possible permutations and the threshold-based anomaly detection technique. This technique helps our model to be able to distinguish attacks from legitimate traffic records without requiring prior knowledge. Our technique detects locality disruption, false locality, and combination of the two with high accuracy. Implementation of XC-topology, the proposed method shows high efficiency in mitigating these attacks. In comparison to other ML-methods, our proposed method has a low overhead cost in mitigating CPA as it doesn't require attackers' prior knowledge. Additionally, our method can also detect non-uniform attack distributions.

Nowadays, smartphones are everywhere. They play an indispensable role in our lives and makes people convenient to communicate, pay, socialize, etc. However, they also bring a lot of security and privacy risks. Keystroke operations of numeric keypad are often required when users input password to perform mobile payment or input other privacy-sensitive information. Different keystrokes may cause different finger movements that will bring different interference to WiFi signal, which may be reflected by channel state information (CSI). In this paper, we propose WiPass, a password-keystroke recognition system for numerical keypad input on smartphones, which especially occurs frequently in mobile payment APPs. Based on only a public WiFi hotspot deployed in the victim payment scenario, WiPass would extracts and analyzes the CSI data generated by the password-keystroke operation of the smartphone user, and infers the user's payment password by comparing the CSI waveforms of different keystrokes. We implemented the WiPass system by using COTS WiFi AP devices and smartphones. The average keystroke segmentation accuracy was 80.45%, and the average keystroke recognition accuracy was 74.24%.

With the advent of fifth generation (5G) network and increasingly powerful mobile devices, people can conveniently obtain network resources wherever they are and whenever they want. However, the problem of mobility support in current network has not been adequately solved yet, especially in inter-domain mobile scenario, which leads to poor experience for mobile consumers. MobilityFirst is a clean slate future Internet architecture which adopts a clean separation between identity and network location. It provides new mechanisms to address the challenge of wireless access and mobility at scale. However, MobilityFirst lacks effective ways to deal with multicast service over mobile networks. In this paper, we design an efficient multicast mechanism based on MobilityFirst architecture and present the deployment in current network at scale. Furthermore, we propose a hierarchical multicast packet header with additional destinations to achieve low-cost dynamic multicast routing and provide solutions for both the multicast source and the multicast group members moving in intra- or inter-domain. Finally, we deploy a multicast prototype system to evaluate the performance of the proposed multicast mechanism.