# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 2 Related Work
In this study, the proposed countermeasure redirected all traffic to the proposed solution, thereby increasing RTT for all traffic. In [26], timing probing packets were used to ascertain data plane event parameters and control plane characteristics. The method proposed in this study is dependent on intensive flow insertion operations and uses the switch's computation and queuing resources, resulting in data rate restrictions. [PERSON] et al. [27] have also demonstrated timing-based side-channel attacks. They proposed that the network information can be derived from the delay caused when a packet is sent to the controller. [PERSON] et al. [28] recommended delaying the implementation of the flow at random more recently to conceal the disparity in timing between multiple queries. This method delays flow installation whenever the MAC address of the host changes. The authors did not consider the manipulation of the MAC addresses. Adversaries can be easily fabricated and employ various MAC addresses. [PERSON] et al. [29] also presented a timing attack technique for vehicular ad hoc networks. [PERSON] et al. [30] proposed a technique to detect timing channel intrusions in SDN. Although the results demonstrate that the method can identify timing channels in an SDN cloud, it can be argued that an adversary could potentially exhaust cloud resources. In addition, it reduces the ability of an adversary to identify the virtual machine that is being exploited. [31] addresses timing side-channel attacks on the IoT using machine learning techniques. The findings suggest that this technique can be used to identify the timing side-channel in the IoT. Machine learning has emerged as an effective approach for identifying the various types of SDN attacks. Moreover, it has been utilised for traffic flow management and route optimisation [32]. Machine learning techniques have been demonstrated to produce accurate results with low false positive rates. [PERSON] et al. [33] recently proposed a machine learning algorithm to route applications and improve the performance of software-defined vehicular networks. [PERSON] et al. [34] presented a thorough analysis of the integration of machine learning and deep learning techniques for intrusion detection in SDN. This study demonstrates that most research in this field concentrates on high-rate distributed denial of service (DDoS) assaults, whereas only a minority of studies examine low-rate attacks. [PERSON] et al. [35] designed an SDN intrusion detection system based on machine learning. This paper illustrates the application of multiple classification techniques based on machine learning for attack detection. Common types of attacks, such as denial of service (DoS) and network scanning, were detected in this study. [PERSON] et al. [36] employed machine learning techniques to detect DDoS attacks in the SDN. By comparing the average duration of each session with the amount of time required to access the server from a specific IP address, they were able to identify anomalies. [PERSON] et al. [37] evaluated machine-learning algorithms for SDN security. They employed a variety of machine-learning algorithms for a customised dataset and evaluated the performance of each algorithm. [PERSON] et al. [38] proposed a DDoS detection and mitigation solution for IoT devices based on machine learning and SDN. This method detects attack patterns by using an SVM algorithm. [PERSON] et al. [39] presented an SDN attack detection framework based on machine learning. In this study, we evaluate a dataset of multiple attacks. The primary objective of this study was to collect data efficiently from a network. [PERSON] et al. [40] presented a comprehensive study of Knowledge-defined Networks (KDN). They elaborated on how KDNs use machine learning models to combine the management and control planes of SDN to form a knowledge plane. One of the primary challenges during the incorporation of machine learning into SDN security is the absence of high-quality labelled datasets [41].