With the explosive growth of mobile data traffic, roadside-unit (RSU) caching is considered an effective way to offload download traffic in vehicular ad hoc networks (VANETs). Many existing works investigate the content placement of RSU caching. However, few of them consider the download deadline constraint when caching the content in the RSUs. In this paper, the main objective is to maximize the hit rate of downloading the requested content from the RSUs before the deadline expires. We propose a Markov-based mobility model and a Markov clustering-based content placement algorithm to group the RSUs into clusters and allocate the content to the cache of the RSUs in the cluster. We also investigate the impact on the cache hit rate under different simulation parameters, such as the total number of RSUs, the cache size, and the number of RSUs visited by vehicles during the download period. According to the simulations conducted, when the region of interest (RoI) is small, the MVP method increases the cache hit rate by at least 21.40% compared to the existing methods. When the RoI is large, our approach outperforms other existing methods by at least 26.16% and at most 337.77%, which significantly increases the efficiency of the download session in VANET.

As the number of autonomous cars grows explosively, it is important to broadcast messages with less time and fewer transmissions, especially emergency messages. Therefore, we present an efficient way to broadcast messages by recording the information of informed and uninformed nodes and adopt the mechanism into the push-pull algorithm for vehicular ad hoc networks.

In software-defined networking (SDN), emerging new-flow attacks aim at exhausting the resources of switches and controllers through massive packet-in messages. To detect new-flow attacks, SDNGuardian was proposed as a protocol-independent defense method, which uses entropy to detect anomalies and mitigate attacks with rate limits. In this paper, we introduce a crafty new-flow attack named timeout-aware attack that SDNGuardian cannot detect. We, therefore, propose a novel defense method: SDNSnapshot. Through simulations, we show that SDNSnapshot can successfully detect the timeout-aware attack. The number of dropped benign packet-in messages in SDNSnapshot is around one third of that in SDNGuardian. Besides, a snapshot only consumes 0.9Mb static random access memory (SRAM) for each anomalous sensitive field. The results indicate that SDNSnapshot is a feasible solution to mitigate new-flow attacks in practice.

Port scanning is a well-known behavior when a botnet searches target devices. To detect port scanning accurately, data with high discriminatory power are indispensable. Most related works, however, focus on data analysis methods but neglect storage limitations of switches, which makes their methods impractical. Therefore, we propose a new data collection method for collecting network information of port scanning in P4-based SDN named 0-replacement. Through simulations, we compare the 0-replacement method with two classic data collection methods. Results show that the 0-replacement method improves the true positive ratio by at least 25 percentage points but only consumes 0.36% memory space.

As the number of network devices increases rapidly, it becomes more and more difficult to defend network attacks. Large-scaled attacks, such as SYN flooding, may lead to heavy burden to the switches as well as the controller in a software defined network (SDN). In this paper, we investigate the SYN flooding and Address Resolution Protocol (ARP) spoofing attacks in SDN, and then propose mechanisms to address these two attacks. We also present a new scheme to detect SYN flooding by using only a few forwarding rules. Moreover, we utilize the Programming Protocol-independent Packet Processors (P4) technique to mitigate the burden of the controller.

Software-defined Network (SDN) is considered as a promising technology to provide a flexible network for 5G. However, as the resource of flow entries in switches is limited in SDN, a novel flow management method is required when facing massive amounts of IoT devices. To the best of our knowledge, existing methods designed for IoT can only be applied to uncomplicated network environments without multiple periodic traffic flows. In this paper, we propose Periodic Subflow-based Proactive Flow Installation Mechanism (PSPFIM), a more widely applicable method with better performance. Compared to related works, we improve the hit ratio by 45% and installation efficiency by 6.19 times according to results of simulations, which means PSPFIM can reduce the average network latency with even less flow entries.