# Software-Defined Networking: Categories, Analysis, and Future Directions
## 10 Future Research Directions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Studies** & **Year** & **Area Discussed** & **Methods Used** \\ \hline [PERSON] et al. [250] & 2017 & Traffic classification and security & Comprehensive study on security and traffic using traditional and formal methods \\ \hline [PERSON] et al. [251] & 2018 & Intrusion detection & Deep and unsupervised learning for IDS \\ \hline [PERSON] et al. [252] & 2018 & Traffic profiling & Different supervised and unsupervised learning methods \\ \hline [PERSON] et al. [253] & 2018 & Network function virtualization and QoS & Using machine learning and mathematical methods \\ \hline [PERSON] et al. [254] & 2014 & Comprehensive survey on SDN core concepts & General discussion based on the traditional and Openflow concepts \\ \hline [PERSON] et al. [255] & 2015 & Heterogeneous networks & AI-based techniques \\ \hline [PERSON] et al. [256] & 2016 & Cybersecurity intrusion detection & Supervised learning and unsupervised learning \\ \hline [PERSON] at al. [257] & 2019 & Traffic classification and prediction & Supervised, reinforcement, and unsupervised learning \\ \hline [PERSON] et al. [258] & 2018 & Security in network & Machine and deep learning for security \\ \hline [PERSON] et al. [259] & 2018 & General concepts of hybrid SDN and terminologies & Categorization of hybrid SDN based on different technical models \\ \hline [PERSON] et al. [260] & 2021 & A comprehensive state-of-the-art progress report on specific topics of SDN & Classifications of traffic management, load balance, network safety, and controller placement \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._Information-centric networking (ICN) [267] is gaining popularity for the future Internet to increase the efficiency of content delivery and availability. With the increasing demand for video streaming of public users through mobile devices, the Internet speed is significant to fulfill the desired need for end-users through efficient use of bandwidth. Nowadays, different applications of ICN are very popular, such as SDN-based ICN [268], IoT-based ICN [269], ICN with edge computing [270], green ICN [271], and so on. These areas need to be explored in more detail to help the ICN community reach the next step of implementation in live deployments. The network programmability feature of SDN is used to enrich response functionality. The data plane provides the possibility of adding new functions that are more competent to secure the entire network. In prevention systems, security policies are defined to stop attackers from contacting targets, which require investigation once policies change. The dynamic flow control features of SDN enhance the detection of attacks without adding middleboxes and virtually turn switches into network security devices that can prevent attacks dynamically. Moreover, ML-based SDN includes network optimization, improving network security, and high-quality training datasets. Some other broader perspectives on SDN, such as software-defined mobile networks and software-defined vehicular networks, are also important areas to explore. Regarding QoS, researchers are carrying out experiments with real matrices through different network topologies so that each flow may have different QoS requirements. If DROM [272] is extended with QoS routing, more efficient and enhanced results can be generated. The QoS measures the traffic conditions and traffic classification, while DROM dynamically measures the reliability, effectiveness, and awareness of QoS. The queuing delay of the switches and the processing delay of the server improves the QoS. For traffic engineering, some machine learning techniques provide fundamental improvements compared to the traditional traffic engineering paradigm. Many researchers have devoted their skills to developing efficient systems for traffic classification, routing, and traffic optimizations.