A Slow HTTP Distributed Denial of Service (DDoS) attack causes a web server to be unavailable, but it is difficult to detect in a network because its traffic patterns are similar to those of legitimate clients. In this paper, we propose a network-based Slow HTTP DDoS attack defense method which is assisted by a Software-Defined Network (SDN) that can detect and mitigate Slow HTTP DDoS attacks in the network. Simulation results show that the proposed Slow HTTP DDoS attack defense method successfully protects web servers against Slow HTTP DDoS attacks.

This study proposes a 2D surface correlation-based indoor localization technology using LTE fingerprinting with an accuracy of several meters. The most important problem with RF fingerprinting is that the location discernment of signal strength becomes exceedingly low as the distance from the RF signal source increases. Instantaneous RSS measurement based conventional fingerprinting involves the installation of several signal sources to improve location discernment. However, additional installations of LTE base stations (BSs) are impossible. To improve location discernment, the proposed technology utilizes a spatial RSS pattern extracted using the Pedestrian-Dead Reckoning during user movement. The use of the proposed technology greatly improves the accuracy and availability of LTE signals using the pattern. Additionally, the following two points should be considered. First, the spatially accumulated pattern contains location errors that can cause pattern distortion. The proposed technology performs pattern correction through feature matching using RSS mark and crossroad locations. Second, the accuracy of pattern matching may be decreased prior to sufficient pattern accumulation. For the rapid convergence of the pattern matching, the proposed technology performs correlation pattern analysis. This approach detects the point in which the discernment is increased by pattern accumulation and limits the search range around the matching point. To verify the performance, we conducted tests in a shopping mall where only one LTE BS ID is available. Consequently, the convergence distance of pattern matching was improved by 69% after pattern analysis. Furthermore, it was confirmed that the localization error after convergence improved from 4.16 m to 2.82 m.

Due to the dynamic mobility feature, the proactive flow rule cache method has become one promising solution in software-defined networking (SDN)-based access networks to reduce the number of flow rule installation procedures between the forwarding nodes and SDN controller. However, since there is a flow rule cache limit for the forwarding node, an efficient flow rule cache strategy is required. To address this challenge, this paper proposes the mobility-aware hybrid flow rule cache scheme. Based on the comparison between the delay requirement of the incoming flow and the response delay of the controller, the proposed scheme decides to install the flow rule either proactively or reactively for the target candidate forwarding nodes. To find the optimal number of proactive flow rules considering the flow rule cache limits, an integer linear programming (ILP) problem is formulated and solved using the heuristic method. Extensive simulation results demonstrate that the proposed scheme outperforms the existing schemes in terms of the flow table utilization ratio, flow rule installation delay, and flow rules hit ratio under various settings.

The Global Satellite Navigation System (GNSS) used in various location-based services is accurate and stable in outdoor environments. However, it cannot be utilized in an indoor environment because of low signal availability and degradation of accuracy due to the multipath distortion of satellite signals in urban areas. On the contrary, LTE signals are available almost everywhere in urban areas and are quite stable without much variation throughout the year. This is because of the fixed location of base stations and the well-maintained policy of mobile communication service providers. Its varied stability and reliability make LTE signals a more viable method for localization. However, there are some complexities in utilizing LTE signals including signal interference distortion phenomena during propagation multipath fading, and various types of noise. In this paper, we propose a surface correlation-based fingerprinting method to utilize LTE signals for localization in urban areas. The surface correlation converts timely measured signal strength into spatial pattern using the walking distance from a Pedestrian Dead-Reckoning (PDR). The surface correlation is carried out by comparing the spatial signal strength pattern of a pedestrian`s movement trajectory with a fingerprinting database to estimate the location. A reference trajectory of the moving pedestrian is chosen to have a greater correlation among the multiple trajectory candidates generated from a link-based fingerprinting database. By comparing spatial signal strength patterns, the proposed method can improve robustness in localization overcoming the accuracy degradation problem due to RF multipath and noise that are dominant in the conventional RSS measurement-based LTE localization scheme. The test results in urban areas demonstrate that the proposed surface correlation-based fingerprinting method has improved performance compared to the other conventional methods, thus proving to be a useful complementary method to the GNSS in urban areas.