Information centric networking (ICN) has been recently proposed as a prominent solution for content delivery in vehicular ad hoc networks. By caching the data packets in vehicular unused storage space, vehicles can obtain the replicate of contents from other vehicles instead of original content provider, which reduces the access pressure of content provider and increases the response speed of content request. In this paper, we propose a community similarity and population-based cache policy in an ICN vehicle-to-vehicle scenario. First, a dynamic probability caching scheme is designed by evaluating the community similarity and privacy rating of vehicles. Then, a caching vehicle selection method with hop numbers based on content popularity is proposed to reduce the cache redundancy. Moreover, to lower the cache replacement overhead, we put forward a popularity prediction-based cooperative cache replacement mechanism, which predicts and ranks popular content during a period of time. Simulation results show that the performance of our proposed mechanisms is greatly outstanding in reducing the average time delay and increasing the cache hit ratio and the cache hit distance.

Recently, the Internet of Things (IoT) is attracting significant attention from both academia and industry. To connect the huge amount of IoT devices effectively, software-defined networking (SDN) is considered as a promising way because of its centralized network management and programmable routing logic. However, due to the limited resources in both the data plane and the control plane, SDN is vulnerable to the new-flow attack, which can disable the SDN-based IoT by exhausting the switches or the controller. Therefore, in this paper, we propose a smart security mechanism (SSM) to defend against the new-flow attack. The SSM uses the standard southbound and northbound interfaces of SDN, and it includes a low-cost method that monitors the new-flow attack by reusing the asynchronous messages on the control link. The monitor method can differentiate the new-flow attack from the normal flow burst by checking the hit rate of the flow entries. Based on the monitoring result, the SSM uses a dynamic access control method to mitigate the new-flow attack by perceiving the behavior of the security middleware in the IoT. The dynamic access control method can intercept the attack flows at their access switch. Extensive simulations and testbed-based experiments are conducted and the corresponding results verify the feasibility of our claims.

Software-defined networking (SDN) is a promising network paradigm for future Internet. The centralized controller and simplified switches replace the traditional complex forwarding devices, and make network management convenient. However, the switches in SDN currently have limited ternary content addressable memory to store specific routing rules from the controller. This bottleneck provokes cyber attacks to overload the switches. Despite existing some countermeasures for such attacks, they are proposed based on simplified attack patterns. In this paper, we review the table-overflow attack using a sophisticated attack pattern. In the attack pattern, attack flows are targeted at their middle hops instead of endpoints. We first define potential targets in the network topology, then we propose three specific traffic features and a monitoring mechanism to detect and locate the attackers. Further, we propose a mitigation mechanism to limit the attack rate using the token bucket model. With the control of token add rate and bucket capacity, it avoids the table overflow on the victim switch. Extensive simulations in different types of topologies and experiments in our testbed are provided to show the performance of our proposal.

Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic and unpredictable link status. In this paper, we propose a cybertwin-driven learning-based transmission scheduling mechanism for software-defined vehicular networks, which can adaptively select/adjust transmission control methods, i.e., loss-based, delay-based and hybrid ones, to suit to the time-varying network environment. In particular, we first analyze the dynamic network characteristics of three realistic vehicular network scenarios in terms of network throughput, round-trip time (RTT) and RTT jitter. Furthermore, we propose a novel transmission scheduling model and formulate the SDVN transmission scheduling issue as a linear programming problem. To obtain the optimized scheduling policies and guarantee the effectiveness of transmission control, we further propose a Cybertwin-driven and Deep Reinforcement Learning based transmission control solution (TcpCDRL). Specifically, TcpCDRL is featured with: (i) using deep reinforcement learning (DRL) to adaptively adjust transmission control policy, (ii) using cybertwin-driven transmission controlling to improve the policy-making effectiveness and timeliness. Simulation results show that the proposed TcpCDRL approach outperforms the single well-known transmission control approach ( i.e ., TcpWestwood, TcpBic, TcpVeno and TcpVegas) in terms of network throughput and RTT.

State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%-81.24%.

5G network is envisioned to provide massive connectivity for a wide range of applications, such as ultra-clear media, internet of vehicles, and smart home. The traditional way of providing security services is difficult to support these new 5G applications flexibly and effectively. In our previous work, we proposed a SFC-based framework that chains security functions in different domains to provide security services on demand. However, creating cross-domain service function chains will inevitably result in the additional network latency. In this paper, we study this problem of minimizing the end-to-end latency when deploying cross-domain service function chains for 5G applications. First, an exact approach, consisting of service chain partition and service subchain embedding, is proposed to derive an optimal solution for cross-domain service function chain placement. Second, we improve the Viterbi algorithm and propose an efficient heuristic approach to derive near-optimal solutions for large networks. We also compare the performance of the proposed exact approach, the proposed heuristic approach, and the simple greedy approach in different scales of network infrastructures. Simulation results are presented to demonstrate the effectiveness of the proposed approaches.