Artificial Intelligence Enabled Software Defined Networking: A
Comprehensive Overview
Majd Latah, Levent Toker
Department of Computer Engineering, Ege University, 35100, Bornova, Izmir, Turkey
latahmajd@gmail.com, toker1960@gmail.com

arXiv:1803.06818v1 [] 19 Mar 2018

Abstract
In recent years, the increased demand for dynamic management of network resources in modern computer networks in
general and in today’s data centers in particular has resulted
in a new promising architecture, in which a more flexible
controlling functionalities can be achieved with high level of
abstraction. In software defined networking (SDN) architecture, a central management of the forwarding elements (i.e.
switches and routers) is accomplished by a central unit, which
can be programmed directly to perform fundamental networking tasks or implementing any other additional services. Combining both central management and network programmability, opens the door to employ more advanced techniques such
as artificial intelligence (AI) in order to deal with high-demand
and rapidly-changing networks. In this study, we provide a detailed overview of current efforts and recent advancements to
include AI in SDN-based networks.

using the SDN paradigm for their own data centers [11, 12].
Artificial intelligence (AI) reveals a huge potential in SDN innovation. Our previous study [13] focused on highlighting the
first efforts to integrate AI in SDN. In this work, however, we
attempt to we cover the most relevant research efforts to gain
deeper insight into the significant role of AI in SDN paradigm.

2

SDN Architecture

As mentioned previously, SDN promotes innovation by introducing the concept of centralized programmable control of the
data plane, which facilitates the development of new network
services and protocols [4]. The SDN architecture is designed
based on the idea of the separation between control and data
planes (see Fig. 1).
The first attempt was network control point (NCP) [14],
which employed the separation concept in order to enhance
the control of AT&Ts telephone network, whereas recent contributions such as Ethane [7] and SANE [15] applied the same
concept for Ethernet networks [16]. The SDN applications
1 Introduction
reside in the application plane of SDN architecture where the
Software-defined networking (SDN) adopts the concept of northbound application programming interface (API) provides
programmable networks by using a logically centralized man- the commutation between the application and control planes
agement, which represents a simplified solution for complex [8], which allows implementing a set of network services such
tasks such as traffic engineering [1], network optimization [2] as traffic engineering, intrusion detection, quality of service
and orchestration [3]. Furthermore, dealing with modern net- (QoS), firewall and monitoring applications [4]. Northbound
work applications requires more scalable architecture which API allows developers to write their own applications without
should be able to provide reliable and sufficient services based the need for detailed knowledge of the controller functions or
on a specific traffic type [4]. This can be achieved with the understanding how the data plane works. It is worth mentionSDN architecture, which maintains a global view of network ing that several SDN controllers provide their own northbound
states and provides a flow-level control of the underlying lay- APIs [16].
ers [4]. This idea caused a dramatical change in the way how
The communication between control and data planes is pronetworks are designed and managed [5, 6]. In addition, the vided using a southbound API such as forwarding and conSDN architecture allows the involvement of third parties in trol element separation (ForCES) [17], open vSwitch database
the design and deployment of modern network applications (OVSDB) [18], protocol oblivious forwarding (POF) [19],
[6]. Ethane project [7] formed the foundation of today’s SDN OpenState [20], OpenFlow (OF) [21] and OpFlex [22], which
by presenting a new networking paradigm, in which a central- enables exchanging control messages with forwarding eleized controller is used for flow-level policy management and ments (e.g., OF-enabled switches). As shown in Figure 1,
security purposes in enterprise networks.
each OF-enabled switch adopts a flow-based decision making
The SDN paradigm separates the control plane from the logic determined by the so-called SDN controller, which is redata plane, which results in achieving much faster and dy- sponsible for preparing the forwarding tables of each switch
namic approach compared with conventional network archi- [8]. A typical OF-enabled switch has a pipeline of flow tatecture [8]. The control plane can be split into several virtual bles, which consist of flow entries, each of which has three
networks where each one implements a different policy [8]. parts: (1) matching rules, which are used for matching inAs a result, this paradigm can be viewed as a tool allows ad- coming packets (2) counters maintain statistics of matched
dressing various issues in networking from another perspec- flows and (3) actions or instructions, which can be configured
tive [5] and can be used also to fulfill the requirements of new proactively or reactively to be executed upon a match [6, 14].
technologies such as internet of things (IoT) [9]. The adoption The forwarding elements (i.e. OF-enabled switches) can be
of SDN paradigm, however, strongly depends on its success implemented in either software or hardware. Some software
in reaching an appropriate solution for the problems, which switches such as Open vSwitch have a great potential for procannot be solved by the traditional networking protocols [10]. viding a solution for data centers and virtual networks [16].
Some companies such as Microsoft and Google have started On the other hand, other APIs [10, 23] are proposed for a spe-

Figure 1: A basic SDN architecture
cific purpose (e.g, VOIP applications and inter-domain routing, not to mention various SDN programming languages such
as Procera [24], NetCore [25] and Frenetic [26], which provide high-level APIs that can be used to write different SDN
applications in more flexible and functional manner.

3

hanced matching/action abilities, optical ports, group tables,
meter tables and synchronized tables [4, 29]. More details
concerning various versions of OF specification can be found
in [30]. There are many OF controllers available, such as POX
[31], Beacon [32], OpenDayLight [33], Floodlight [34], and
Ryu [35].

OpenFlow Protocol
4

OpenFlow (OF) is considered the most commonly used southbound API in SDN, which is being continuously developed
and standardized by open networking foundation (ONF) [6].
OF provides an abstraction layer that enables the SDN controller to securely communicate with OF-enabled forwarding
elements [6]. OpenFlow has become the de-facto standard for
southbound APIs used in SDNs [6] and therefore, in this study,
we mainly focus on OF-based SDNs. OF-based forwarding
devices have been developed to coexist together with conventional Ethernet devices [16]. Hybrid switches, on the other
hand, reveal new possibilities by including both OF and nonOF ports [6]. As we mentioned previously, a set of control
messages can be sent by the controller in order to prepare and
update switchs flow tables. A typical OF-enabled switch handles new coming packets based on its flow table. Figure 2,
shows the fields of matching rules part in OF version 1.0.0. A
table-miss occurs when a new packet does not match any of
the flow table entries. In this case, the switch may either drop
the packet or forward it to the corresponding controller using
OF protocol [6].
It is worth to mention that the identity-based access control of the Ethane project [7] became the first specification
of OF switches. After the release of the first OF specification in Spring 2008, many vendors such as HP, NEC, Cisco
and Juniper were able to build their first OF-enabled hardware
switches where NOX [27] was the only available controller at
that period of time [28]. Recently, different versions of OF
protocol have been introduced in order to add more flexible
and reliable capabilities by including multiple flow tables, en-

Artificial Intelligence

Artificial Intelligence (AI) is a rapidly growing field that includes a wide range of subfields, including knowledge representation, reasoning, planning, decision making, optimization,
machine learning (ML) and metaheuristic algorithms. Turing Test [36], provided a fulfilling definition of intelligence,
where a computer has to answer some questions written by a
human interrogator. Accordingly, the computer passes the test
if the interrogator cannot tell whether the answer was written
by a human or a machine. In order to pass Turing Test, the
computer would need to include advanced capabilities such as
natural language processing, knowledge representation, automated reasoning, machine learning and computer vision [36].
AI research stared later in the mid-1950s, where a summer
workshop organized by Martin Minsky and Claude Shannon
at Dartmouth College resulted in the birth of the field of AI
[37]. The first contribution, however, was made in 1943 by
McCulloch and Pitts, proposing the first model for artificial
neural networks, in which each neuron has a binary output
(-1,+1) with a sign activation function [37]. Adoption of AI
approaches increased, thanks to key contributions led to the
emergence of new subfields such as expert systems, fuzzy
logic and evolutionary computation. Further efforts have fueled the research in AI field by refining the existing methods
and proposing brand new hybrid intelligent approaches. Machine learning, metaheuristic algorithms and fuzzy inference
systems are widely used in SDN paradigm therefore we provide an introduction regarding these approaches.
2

Figure 2: Structure of OpenFlow V1.0.0 matching rules

4.1

Machine Learning

An intelligent machine learns from experience (i.e., learns
from the data available in its environment) and uses it to improve the overall performance [36, 37]. In this context, learning methods fall into four types:
4.1.1

Supervised learning

Supervised learning methods are provided with a pre-defined
knowledge. For instance, a training dataset that consists of
input-output pairs, where the system learns a function that
maps a given input to an appropriate output [36]. This approach requires having a dataset that represents the system under consideration and can be used to estimate the performance
of the selected method [38].
4.1.2

Unsupervised learning

Unsupervised learning methods are provided without a predefined knowledge (i.e., having an unlabeled data) [36].
Therefore, the system mainly focuses on finding specific patterns in the input. An example of the unsupervised learning
approach is clustering, which is used for detecting useful clusters in the input data based on similar properties defined by a
proper distance metric such as Euclidian, Jaccard, and cosine
distance metrics [36, 38].
4.1.3

Reinforcement learning

In reinforcement learning approach, the system learns based
on a set of reinforcements from its environment. For instance,
a reward or punishment determines whether the system performed well or not [36].
4.1.4

Semi-supervised learning

In semi-supervised learning the system learns from both labeled and unlabeled data, where the lack of labels as well as
the labeled part may contain a random noise forms a situation
between supervised and unsupervised learning [36].

4.2

solve difficult problems such as combinatorial, highly nonlinear and multimodal optimization problems [44]. Each one
of these algorithms has different advantages, therefore many
efforts were made to design hybrid approaches that combine
their benefits and ultimately attain better results [44]. The
success of these algorithms is determined by achieving a balanced approach between the exploration and the exploitation
[39]. Exploitation is useful for determining the most promising high-quality solutions in the search space, whereas exploitation is needed to concentrate search in some areas based
on previous search results [39].

4.3

Fuzzy Inference Systems

Unlike classical binary logic where any fact can only be true
or false, fuzzy logic (FL) is a multi-valued logic, which deals
with a degree of truth or degree of membership (i.e., any value
between 0 and 1) [37]. Therefore, Boolean logic can be seen
as a special case of FL. Fuzzy systems (FS) are used for mapping a given input to an appropriate output based on the principles of fuzzy set theory, which was developed by Lotfi Zaheh
[37, 46]. FS make use of fuzzy rules in the form:
If x is A and y is B then z is k

(1)

where x, y and z are linguistic variables; A and B are linguistic values determined by fuzzy sets on the universe of discourses X, Y respectively and k is a constant that represents
the consequent of the rule. As shown in Fig. 3, the system
converts the crisp input to the appropriate fuzzy sets using the
membership functions. Thereafter, it will be evaluated using
the inference engine. Finally, the output is determined using
an appropriate defuzzification method such as center of gravity (COG) or weighted average (WA) [37].
Mamdani and Sugeno methods are two inference techniques that can be used in fuzzy systems. Unlike Mamdani’s
approach which employs a fuzzy membership function of the
rule consequent, Sugeno’s approach uses a single point to represent the rule consequent. Sugenos approach, is considered
to be more efficient than Mamdani’s approach which requires
finding the centroid of a two-dimensional shape and this in
turn increases the computational cost [37].

Metaheuristic Algorithms

The heuristic algorithms use a problem-specific heuristic
whereas the metaheuristics form an efficient general purpose
approach that includes a wide range of application area ranging from finance to engineering and networking [39]. Metaheuristics have been increasingly employed to solve hard optimization problems [40], that cannot be solved by any deterministic (exact) approach within a reasonable time [39]. Most
of these algorithms are nature-inspired [40], from simulated
annealing (SA) [41] to genetic algorithms (GA) [42], and from
ant colony optimization (ACO) [43] to whale optimization algorithm (WOA) [44]. These algorithms are widely used to

5

Artificial Intelligence in SDN

AI and ML approaches have been widely used for solving various problems such as routing [47], traffic classification [48],
flow clustering [49] intrusion detection [50], load balancing
[51], fault detection [52], quality of service (QoS) and quality of experience (QoE) optimization [53], admission control
and resource allocation [54]. However, in the era of SDN the
role of AI was significantly increased due to the huge efforts
made by industry and research community. Recent studies
have shown a strong tendency of research community towards

Figure 3: A structure of a typical fuzzy inference system
adoption of AI approaches in SDNs. It is worth mentioning that ML, metaheuristics and fuzzy systems were the most
common approaches for solving various networking related
problems. We investigate the application of each approach in
SDN paradigm separately as will be shown in the reset of this
study.

5.1

Machine Learning in SDN

5.1.1

Supervised learning in SDN

In this context, neural networks [55-63], support vector machine [65-71], random forest [72-78], decision trees [79-92]
and deep learning [93-94] approaches were the most used supervised learning techniques in SDN. From Table 1, we observe that neural networks (NN) approach was used mainly
for intrusion detection and prevention [55, 57-58], solving
controller placement problem [57], load balancing [59], performance perdition [60], service level agreements (SLA) enforcement [61, 62], routing and optimal virtual machine (VM)
placement [63].
Table 2, on the other hand, shows that support vector machine (SVM) approach was mainly used for deploying intrusion detection systems in the SDN paradigm [65-71].
As shown in Table 3, the random forest (RF) approach was
widely used for estimating service-level metrics [72], intrusion detection, security applications [73-74], QoE prediction
[75], traffic classification [76] and threat assessment [77, 78].
Decision trees were widely used for application identification [79, 80], intrusion detection [81] as well as solving SDNrelated security challenged [82].
Furthermore, deep learning techniques have shown promising results in SDN compared to traditional ML approaches as
shown in Table 5.
5.1.2

Unsupervised learning in SDN

K-means [95-99] and self-organizing map [100-104] were
the most used unsupervised learning techniques in SDN
paradigm. Research efforts made to apply k-means approach
to SDNs are summarized in Table 6.
On the other hand, self-organizing map (SOM) approach
was used widely in the SDN paradigm for intrusion detection
as shown in Table 7.
Other unsupervised ML methods such as hidden markov
model (HMM) and restricted Boltzmann machine were used
in the SDN paradigm for security assessment and intrusion
detection (see Table 8).
5.1.3

Reinforcement learning in SDN

In this context, it is worth noting that Q-learning technique
[110-114] was applied widely in SDN paradigm for routing

and adaptive video streaming. The application of reinforcement approach to SDN is summarized in Table 9.
5.1.4

Semi-supervised learning in SDN

Semi-supervised learning [115-117] were also used in SDN
but much less common compared with other learning approaches. As shown in Table 10, the research was focused
on traffic classification [115, 117] and routing [116].

5.2

Metaheuristic Algorithms Used in SDN
Paradigm

A large variety of metaheuristic algorithms such as ant colony
optimization [118-126], genetic algorithms [127-133], evolutionary algorithms [134-137], particle swarm optimization
[138-142] and simulated annealing [143-145] were widely
adopted in SDN. Research efforts made to apply metaheuristic
approach to SDN are summarized in Table 11.

5.3

Fuzzy Inference Systems in SDN

Fuzzy inference systems were also widely used in SDN
paradigm [152-157]. The main research was focused on introducing new protocols [152], intrusion detection [153-155],
selection the optimal network deployment schemes [156] and
traffic engineering [157].

5.4

Other AI-based Approaches in SDN

Other researchers proposed many hybrid models that make
use of two or more intelligent approach in order to enhance
the overall performance and accuracy of these algorithms as
shown in Table 13.

6

Conclusion

In this paper, we provided a state-of-the-art overview of research efforts made for applying artificial intelligence techniques in SDN paradigm. Compared to our recent paper [13],
this study showed an increased adoption of various AI techniques to solve a wide range of networking problems and address new challenges introduced by the SDN paradigm. This
study showed that ML, metaheuristics and fuzzy systems were
the most common AI approaches used in SDN. Many other
hybrid AI approaches were used for achiever higher accuracy
compared to other traditional AI approaches. We conclude
that AI techniques form a significant component for future networks in general and SDN paradigm in particular.

Ref
[55]
[56]

[57]
[58]
[59]
[60]

[61, 62]

[63]

Brief Summary
A distributed intrusion prevention system that can be used in large scale networks. The system achieved lower
computational and communication overhead due to its parallel and simple computational capabilities.
A multi-label classification to predict global network allocations (i.e. weighted controller placement problem).
Compared to decision tree and logistic regression, neural network approach showed better results and saved up to
two-thirds of the algorithm runtime.
A neural network was used for offline prediction of traffic demands in a mobile network operator reducing the
optimality gap below 0.2% (virtual wavelength path-hourly-NN) and 0.45% (wavelength path-hourly-NN).
In addition, NN approach was used for offline prediction of the next configuration time point.
An intrusion detection system for SDN based on NN approach which achieved high accuracy reached 97.3%
accuracy using NSL-KDD dataset.
NN approach for load balancing. Compared to [64] and static Round Robin strategy, the experimental study
showed that this method can achieve better performance and resulted in 19.3% decreasing of network latency.
A NN approach based on Levenberg Marquardt (LM) algorithm to predict the performance of SDN according
to round-trip time (RTT) and throughput.
A new approach to enforce service level agreements (SLA) in SDN and virtualized network functions (NFV).
The research focused on prediction of service level objectives (SLOs) breaches for streaming services
running on NFV and SDN. The research showed that long short term memory (LSTM) is more robust than
feed forward neural networks.
A new paradigm that employs AI in SDN, termed as knowledge-defined networking (KDN). The paradigm
included a proof of concept concerning routing in an overlay network based on NN approach where MSE
reached 1%. In addition to solving the problem of optimal virtual machine (VM) placement in NFV paradigm.

Table 1: The application of neural network approach to the SDN paradigm

Ref
[65]
[66]
[67, 68]
[69]
[70]
[71]

Brief Summary
Detection of DDoS attacks on the SDN controller. SVM showed higher accuracy and less false positive rate compared
to other classifiers.
Detection of flooding attacks based on SVM and idletimeout adjustment (IA) algorithm which leads to an accurate
system and reduction of CPU usage of Open vSwitches and their correspondence SDN controller.
An enhanced behavior-based SVM approach for network intrusion [3] and malware [4] detection where information
gain (IG) metric was used for selecting the most relevant features.
A new approach that combines SVM with self-organizing map (SOM) in order to get a higher accuracy and better
detection rate for DDoS attacks in SDN as well as achieving lower false alarm rate.
Detection of DDoS TCP flood attacks based on least squares support vector machine (LS-SVM), which outperformed
the following algorithms: naive bayes, k-nearest neighbor and multilayer perceptron (MLP).
An SVM approach for implementing a traffic-based filtering classifier, which represents the second stage of a framework
proposed in order to mitigate DoS attacks.

Table 2: The application of SVM approach to the SDN paradigm

Ref
[72]
[73]
[74]
[75]
[76]
[77]
[78]

Brief Summary
An RF approach for estimating service-level metrics based on network-level statistics.
Intrusion detection system where the RF approach has been used for both feature selection and classification.
An SDN-based system for automatic identification and security enforcement of potentially vulnerable IoT devices
using device type-specific profiling approach.
ML based QoE prediction in SDN paradigm. The authors utilized the following ML algorithms: RF, k-nearest neighbors
(k-NN), NN and decision trees where RF showed better performance.
Several ML techniques such as random forest, stochastic gradient boosting and extreme gradient boosting were employed
for traffic classification.
An RF approach showed the best performance among k-NN, naive bayes and logistic regression (LR) for cyber threat detection.
An approach for analyzing, detecting and reacting against cyber threats. Experimental resulted based on ISOT botnet dataset
showed that RF approach achieves the best performance among k-NN, naive bayes and logistic regression.

Table 3: The application of RF approach to the SDN paradigm

5

Ref

Brief Summary
The C4.5 decision tree approach was used for application identification in order to associate each application type
with a corresponding QoS level.
An intrusion detection and prevention system based on C4.5 algorithm.
Employing a decision tree approach in mitigation of host location hijacking attacks on SDN controllers.
A framework for detection and prevention of simple mail transfer protocol (SMTP) flow attacks.
Flow based botnet detection based on C4.5 algorithm in SDN.
Traffic classification for mobile agents in SDN based on C5.0 algorithm.
Solving flow table congestion problem based on C4.5 algorithm.
Prediction of potential vulnerable hosts using historical network data and ML algorithms such as C4.5, bayesian network (BN),
decision table (DT), and naive-bayes (NB). The best results were achieved by bayesian network approach.
Introducing new extensions for decision-tree algorithms, namely adaptive grouping factor (AGF) and independent sub-rule (ISR)
leaf structure achieving better performance for larger rules as well as reducing the number of memory access by a factor of 3.
This study considers SDN rule-sets.
A two-stage approach for detection of elephant flows where the first stage consists of an efficient sampling and the next stage,
employs C4.5 for correlated flows.
Prediction of QoS violations based on M5Rules appoarch which combines decision trees and linear regression.
Integrating a J48-tree classifier for intrusion detection on OpenFlow switches.
Detecting Botnets using SDN paradigm, where decision trees showed better results for detecting peer-to-peer botnets
whereas SVM and Bayesian networks were more effective in detecting command and control (C&C) related botnets such as
HTTP and IRC (internet relay chat) botnets.

[79, 80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]
[89]
[90]
[91]
[92]

Table 4: The application of decision trees approach to the SDN paradigm

Ref
[93]
[94]

Brief Summary
Intrusion detection system based on a simple deep neural network, where deep learning approach achieved the best results
compared to other supervised ML algorithms such as naive bayes, SVM and decision trees.
Construction of routing table based on supervised deep belief networks. The experimental results showed that the proposed
approach outperformed traditional open shortest path first (OSPF) protocol in terms of throughout and average delay per hope.

Table 5: The application of deep learning approach to the SDN paradigm

Ref
[95]
[96]
[97]
[98]
[99]

Brief Summary
K-means and probabilistic transition based approach were used for detecting application layer DDoS attacks.
SDN-based approach for achieving Wi-Fi direct clustering in campus networks.
Solving the problem of optimal controller placement based on k-means approach.
K-means was used for user traffic profiling in campus SDN.
Naive bayes, k-NN, k-means and k-medoids were used for detecting DDoS attacks in SDN, where the best
results achieved by naive bayes approach.

Table 6: The application of k-means approach to the SDN paradigm

Ref
[100]
[101]
[102]
[103]
[104]

Brief Summary
Intrusion detection based on SOM approach. The main disadvantage in this approach is high-grained flow-based traffic
matching and sampling.
Detecting and mitigating DoS attacks based on a combination of access control and SOM classification. The proposed
method can deal with IP/MAC spoofing attacks.
Distributed SOM approach for tackling the performance bottleneck and overload problems for large-sized SDNs under
flooding attacks.
Detecting DDoS flooding attacks based on SOM approach.
Comparing different ML approaches for intrusion detection in SDN paradigm. The experimental study showed that
hierarchical learning vector quantization (LVQ1) is more efficient than other approaches such as SOM and LVQ1.

Table 7: The application of SOM approach to the SDN paradigm

Ref
[105]
[106]
[107]
[108]

Brief Summary
Anomaly detection and mitigation based on a two-stage unsupervised learning approach for feature selection and clustering.
Mitigation of domain name system (DNS) query-based DDoS attacks based on Dirichlet process mixture model for clustering
traffic flows. The proposed system achieved better results than mean shift (MS) clustering approach.
Security situation assessment for SDN based on hidden markov model (HMM) where Baum-Welch algorithm was used for training
and Viterbi algorithm was used for predicting the network state.
Detection of DDoS attacks in SDN based on restricted boltzmann machine.

Table 8: The application of other unsupervised ML approaches to the SDN paradigm

6

[109]
[110]
[111]
[112]

[113]

[114]

Routing based on reinforcement learning approach which showed less loss rate and better jitter values, when compared
to traditional OSPF routing protocol.
QoS-aware adaptive routing (QAR) for distributed hierarchical control planes where markov decision processes (MDP)
with QoS-aware reward function used for modeling the system. Instead of using conventional Q-learning method, softmax
action selection policy and stateactionrewardstateaction (SARSA) approach were used for quality update.
Congestion prevention based on Q-learning approach. Compared with Dijkstra’s algorithm and extended Dijkstra’s algorithm
the proposed approach showed better results when the size of transmitted data increases.
Adaptive video streaming based on Q-learning approach where markov decision process (MDP) was used for modeling the
system which showed better results when compared with the shortest path routing and greedy-based approaches.
An end-to-end SDN-based intelligent architecture for large-scale HTTP adaptive streaming (HAS) systems where partially
observable markov decision process (POMDP) was used for modeling the system and Q-learning based approach that employs
a per-cluster decision algorithm to maximize QoE. The experimental study showed that the system was able to increase the video
stability and achieve better QoE fairness and network resource utilization.
An online Q-learning-based dynamic bandwidth allocation algorithm (Q-FDBA) for better QoE fairness. Q-FDBA showed
better results when compared to bandwidth-aware (BA) streaming and QoE fairness framework (QFF).

Table 9: The application of reinforcement learning approaches to the SDN paradigm

[115]

[116]

[117]

Proposed a new framework for QoS-aware traffic classification based on semi-supervised learning. This approach
can classify the network traffic according to the QoS requirements. The system allows achieving deep packet inspection
(DPI) and semi-supervised learning based on Laplacian SVM. The Laplacian SVM approach outperformed a previous
semi-supervised approach based on k-means classifier.
Introduced an efficient routing pre-design solution based on semi-supervised approach. The study suggested using an appropriate
clustering algorithm such as Gaussian mixture model and k-means clustering for feature extraction. Thereafter, a supervised
classification approach such as extreme learning machine (ELM) can be used for flow demand forecasting. The authors also
suggested using an adaptive multipath routing approach based on analytic hierarchy process (AHP) for handling to elephant
flows according different constraint factor weights.
Proposed a new method for fine-grained traffic classification based on semi-supervised approach called nearest application
based cluster classifier (NACC). Unlike traditional methods which use one feature vectors, this algorithm constructs a matrix
with several cluster centroids based on k-means clustering to represent the application. The algorithm uses a small number of
labeled flows to build a supervised dataset. Then collects the unlabeled flows to be merged with previously collected dataset,
based on investigating the correlated flows, which is used in order to map an application to different clusters. The experimental
study showed a good identification accuracy reaching 90%.

Table 10: The application of semi-supervised learning approaches to the SDN paradigm
Ref
Ant colony optimization (ACO)

Genetic algorithms (GA)
Evolutionary algorithm (EA)
Particle swarm optimization(PSO)
Simulated annealing (SA)
Artificial bee colony (ABC)
Honeybee foraging algorithm (HFA)
Binary bat algorithm (BBA)
PSO and firefly algorithm (FA)
Whale optimization algorithm (WOA)
Chaotic grey wolf optimization
algorithm (CGWOA)

Brief Summary
Used for solving various networking problems such as routing [118- 120],
load balancing [121, 122], video surveillance [123], network security [124, 125]
and maximizing network utilization [126].
Used for routing [127], virtual network planning [128], optimizing the cost of
deploying deep packet inspection functions in SDN [129], load balancing [130-132]
and designing optimal observation matrices [133].
Used for solving controller placement problem in distributed SDNs [134], routing [135],
moving target defense [136] and designing optimal observation matrix for network measurement [137].
Used for routing [138, 139], resource management [140], multi-tenant virtual
network customization [141], controller provision and switch assignment [142] for cloud datacenters
Used for solving controller placement problem [143, 144] and dynamic controller provisioning [145].
Used for traffic engineering for SDN-based video surveillance systems [146].
Used for achieving load balancing for SDN-based cloud systems [147].
Used for feature selection for anomaly detection [148].
Used for solving controller placement problem in SDN-based wide area networks [149].
Used for achieving clustering for heterogeneous, randomly distributed and dense IoT networks [150].
Solving controller placement problem in software-defined mobile networks [151].

Table 11: The application of metaheuristic algorithms to the SDN paradigm
Ref
[152]
[153]
[154]
[155]
[156]
[157]

Brief Summary
Topology discovery protocol for software defined wireless sensor networks. The proposed protocol showed
better performance when compared with software defined networking solution for wireless sensor networks (SDN-WISE) [158].
Prevention of DDoS attacks based on Sugeno’s fuzzy inference approach.
A cooperative defense approach against DoS flood attacks based on fuzzy modeling.
An information security management system based on Mamdani’s fuzzy inference approach. Threat degree is determined by
the fuzzy inference module which mainly depends on threshold random walk with credit-based rate limiting (TRW-CB)
and rate limiting algorithms.
Selection of network deployment schemes based on Mamdani’s approach.
Traffic,engineering method for SDN-based video surveillance systems using adaptive,type-2 fuzzy approach which showed
better results when compared with type-1,fuzzy approach, available bandwidth based traffic engineering approach and,OSPF
routing protocol in terms of end to end delay, peak signal to noise,ratio (PSNR) and packet loss ratio.

Table 12: The application of fuzzy inference approaches to the SDN paradigm
7

8

[166]

[165]

[164]

[163]

[162]

[161]

[160]

[159]

Ref

Table 13: The application of AI-based hybrid approach to the SDN paradigm

Brief Summary
Preventing video freezes in HTTP adaptive streaming based on combination of random undersampling boosting (RUSBoost)
algorithm and fuzzy logic approach. The Experimental results showed that the proposed system was able to reduce video
freezes with 65% and freeze time with 45%, when compared with fair in-network enhanced adaptive streaming (FINEAS)
and Microsoft ISS smooth streaming (MSS).
Mitigating DoS/DDoS attacks based on a game theatrical approach that employs Holt-Winters and genetic algorithm with
fuzzy logic. Holt-Winters for digital signature (HWDS) approach was used for detecting and identifying anomalies and
game theory decision-making (GTDM) approach was used for choosing the optimal defense strategy. HWDS was compared
with fuzzy genetic algorithm digital signature (Fuzzy-GADS) approach where HWDS fared better in performance tests.
Fuzzy-GADS, on the other hand, found to be more efficient on detecting the occurrence of stealthier anomalies.
An intelligent hybrid intrusion detection system for SDN-based 5G network where random forest approach was used for
feature selection and k-means++ with Adaboost approach was used for flow classification.
Anomaly detection and classification based on two phases: 1) lightweight phase based on entropy analysis and 2) heavyweight
approach based on a hybrid ML approach which employs k-means and SVM algorithms.
Optimization the performance of SDN-based on hybrid intelligence approach which makes use of a neural network model.
GA and PSO algorithms were used separately to select the optimal set of inputs that maximizes the network efficiency.
The experimental results showed that PSO achieves a better performance and a faster convergence compared with GA approach.
An intelligent SDN-based video management system which uses face detection and recognition techniques based on EigenFace
algorithm principal component analysis (PCA), FisherFace algorithm based on linear discriminant analysis (LDA), and
local binary patterns (LBP).
Revenue maximization in data centers based on workload-aware SDN controller which determines the optimal combination of a VM
and routing path for each application. The proposed system uses a hybrid approach named hybrid chaotic simulated-annealing PSO (HCSP)
that combines chaotic PSO and SA algorithm. Compared with OSPF and Round Robin approaches, the proposed approach was successful
in reducing the RTT and increasing the revenue.
Application identification based on DNS responses inspection and ML. A classification system, which adopts a voting strategy,
was implemented by Weak Java API from which three algorithms were used namely random forest, rotation forest and random
committee with random tree. The experimental results showed that the combination of DNS responses inspection and
ML techniques was able to achieve higher accuracy compared to standalone ML techniques.

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