Communication networks are difficult to model and predict because they have become very sophisticated and dynamic. We develop a reinforcement learning routing algorithm (RLRouting) to solve a traffic engineering (TE) problem of SDN in terms of throughput and delay. RL-Routing solves the TE problem via experience, instead of building an accurate mathematical model. We consider comprehensive network information for state representation and use one-to-many network configuration for routing choices. Our reward function, which uses network throughput and delay, is adjustable for optimizing either upward or downward network throughput. After appropriate training, the agent learns a policy that predicts future behavior of the underlying network and suggests better routing paths between switches. The simulation results show that RL-Routing obtains higher rewards and enables a host to transfer a large file faster than Open Shortest Path First (OSPF) and Least Loaded (LL) routing algorithms on various network topologies. For example, on the NSFNet topology, the sum of rewards obtained by RL-Routing is 119.30, whereas those of OSPF and LL are 106.59 and 74.76, respectively. The average transmission time for a 40GB file using RL-Routing is 25.2 s. Those of OSPF and LL are 63 sand 53.4 s, respectively.

Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics. This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches through the SDN controller. This can effectively help the network administrators manage related security problems.

In the current landscape of network technology, it is indisputable that the Domain Name System (DNS) plays a vital role but also encounters significant security challenges. Despite the potential of recent advancements in deep learning and machine learning, concept drift is often not addressed. In this work, we designed a DNS anomaly detection system leveraging client-domain associations. We propose the Modified Deterministic Sampling Classifier with weighted Bagging (MDSCB) method, a chunk-based ensemble learning approach addressing concept drift and data imbalance. It integrates weighted bagging, resampling, random feature selection, and a retention strategy for classifier updates, enhancing adaptability and efficiency. We conducted experiments using multiple real-world and synthetic datasets for evaluation. Empirical studies show that our detection system can help identify malicious domains that are difficult for firewalls to detect timely. Moreover, MDSCB outperforms other methods in terms of performance and efficiency.

To avoid distributed denial-of-service (DDoS) attacks or real-time transmission control protocol (TCP) incast in the software-defined networking (SDN) environment, the HashPipe algorithm was developed following the space-saving approach. Unfortunately, HashPipe implemented in the behavioral model (bmv2) cannot be directly executed at a real programming protocol-independent packet processor (P4) switch due to P4 pipeline limitation. Based on the Banzai machine model, this paper shows how to smartly utilize the Banzai atoms to develop HashPipe as a soft computing application in a real P4 switch. Then we propose an enhanced HashPipe algorithm that significantly improves the accuracy of the original HashPipe. The proposed heavy hitter detection is executed at the line-rate of the Tofino P4 switch with the highest process rate in the world.