Malware detection is an important issue for network security, especially for the Internet of Things (IoT) network. Traditional network intrusion detection system (NIDS), running on external host servers, are not scalable for ever-increasing IoT traffic and waste time on transmitting data back and forth. Here, we propose a novel architecture called on-switch malware detector that utilizes the programmable switch and the machine-learning technique to achieve better performance on detecting malicious flows in the network. The on-switch malware detector mainly consists of four components: (1) packet forwarder, (2) feature extractor, (3) flow director, and (4) neural-network detector. According to the experimental results, the on-switch malware detection has a 99.57% shorter response time than a conventional signature-based NIDS; meanwhile its processing capacity increases by 800 times. As a result, the on-switch malware detector efficiently overcomes the shortcomings of conventional NIDSs, making it a better fit for the IoT network.

The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.

Multiple distributed controllers have been used in software-defined networks (SDNs) to improve scalability and reliability, where each controller manages one static partition of the network. In this paper, we show that dynamic mapping between switches and controllers can improve efficiency in managing traffic load variations. In particular, we propose balanced controller (BalCon) and BalConPlus, two SDN switch migration schemes to achieve load balance among SDN controllers with small migration cost. BalCon is suitable for the scenarios where the network does not require a serial processing of switch requests. For other scenarios, BalConPlus is more suitable, as it is immune to the switch migration blackout and does not cause any service disruption. Simulations demonstrate that BalCon and BalConPlus significantly reduce the load imbalance among SDN controllers by migrating only a small number of switches with low computation overhead. We also build a prototype testbed based on the open-source SDN framework RYU to verify the practicality and effectiveness of BalCon and BalConPlus. Experiment confirms the results of the simulations. It also shows that BalConPlus is immune to switch migration blackout, an adverse effect in the baseline BalCon.

The fifth generation (5G) mobile communication network aims at providing high-rate, low-latency services. When a user subscribes a chain of service functions (a.k.a. service chain) from the telecom providers, a Service Level Agreement (SLA) is specified according to his requirement. Deploying service chains optimally has always been a big issue. Several previous works have presented various strategies of service-chain deployment for optimizing either latency or computational resources; however, over-optimization of latency or computational resource is not necessarily equivalent to improvement on quality of experience. Therefore, in this paper, we formally formulate this problem of optimizing quality of experience with the queuing theory and mixed-integer linear programming. In addition, we propose an efficient algorithm named “QoE-driven Service-Chain Deployment with Latency Prediction” for deploying a service chain for a user in practice. According to the experiments, our algorithm reduces > 99% rejections and > 99% waiting time, notably elevating the quality of experience for users.

To operate a reliable SDN network, the control plane should be robust in order to guarantee the correctness of traffic forwarding. For in-band control, the traffic flows along with the data plane without a dedicated control network. Thus, the failure of data plane may isolate the switches from control plane and lead to erroneous forwarding behavior. In this paper, we design a control plane, FASIC, which can bootstrap the control network autonomously and adaptively span the control plane with the control-link switching mechanism. By implementing our design with Open vSwitch (OVS), OpenDayLight OVSDB Integration (ODL-OVSDB) and Floodlight software, we demonstrate that FASIC is an in-band control solution which is feasible under support of the available SDN and OpenFlow resources. Compared with standard OVS, the experimental result shows that FASIC reduces 87.83% of control plane downtime under hard link failure and incurs zero downtime under congestion. The preemptive control-link switching can prevent the control-link failure when it suffers from heavy traffic load.