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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 448
A Study of DDoS Attacks in Software Defined Networks
Chinmay Dharmadhikari1, Salil Kulkarni2, Swarali Temkar3, Shailesh Bendale4
1,2,3B.E. Student, Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegoan, Pune- 411041,
Maharashtra, India
4Professor, Dept. of Computer. Engineering, NBN Sinhgad School of Engineering, Ambegaon, Pune – 411041,
Maharashtra, India
----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Virtualization has changed the ball game
completely in all the technological aspect. One of such
application of it is SDN. Software Defined Network (SDN) is
currently the most popular network architecture in use. SDN
separates the Data Plane from the Control Network. It
introduces controllers, based on the OpenFlow protocol, to
control the switches and routers in the data plane. Although
SDN provides a simplified way to control the network, it gives
rise to new security threats such as DoS attacks, Man in the
Middle attacks etc. SDN being centralized it has a single point
of failure which makes it vulnerable. One of the most common
among them is DDoS. It affects the server whichfailsthewhole
network in addition to this it is easy to start this attack.
Therefore, in order to detect and mitigatethesevulnerabilities
there have been several algorithms and approaches that has
been enforced. In this paper a study various types of DDoS
attack on SDN has been discussed, also the mitigation and
detection of the attack has been studied.
Key Words: Software Defined Networks (SDN), DDoS,
Security.
1. INTRODUCTION
As SDN has dramatically changed the working of the
network and made it more efficient by centralizing the
control of the network with thehelpofcontroller,inaddition
to that it also separates the data plane fromthecontrol plane
which helps in achieving the goal of centralization [1]. The
architecture of the SDN is as follows.
SDN consists of 2 planes- Data Plane and Control Plane.
Data plane is the actual network where the packets are
forwarded. Switches and routers are the main components
of data plane. Control plane consists of the controller as the
main component. Controller is used to control the switches
in the data plane. It provides central management and
monitoring of the network. The control plane consists of
controller and its 3 interfaces.
1. South-Bound Interface: Provides an interface
between switches and controller.
2. North-Bound Interface: Provides an interface
between controller and application layer program.
3. East-West Bound Interface: Provides an interface
between the different controllers.
Following figure depicts basic architecture of SDN.
Fig -1: SDN Architecture
SDN hasseveraladvantageslikecentralizedmanagement,
monitoring, centralized services and security.
Various Controllers are available in the market. Some of
the Controllers are OpenDayLight, FloodLight, Pox, Ryu etc.
SDN currently doesnothaveasecuritymodule.Securityis
the main concern of SDN. Although SDN architecture
overcomes some the traditional attacks, it also gives rise to
new attack strategies. Security concerns may arise due to
some unintentional actions from the devices inside the
network, or intentionalattacksperformedbymalicioususers
[4].
Attacks on SDN can be classified as Spoofing, Tampering,
Information Disclosure, Repudiation, Elevation of Privileges
and DoS [3].
1. Spoofing: Spoofing is falsifying the information
about the attacker. Spoofing is a part of a larger
attack. Attacker can perform ARP Spoofing and IP
Spoofing.Chances of Spoofing are reducedinSDNas
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 449
controller makes dynamic and regular changes in
the network.
2. Tampering: Altering of network information is
known as Tampering. Attackers might change the
information in the flow tablesof switches.Theymay
alter the firewall policies to deny services to some
users.
3. Repudiation: Denial of one of the host in 2-way
communication is Repudiation. Encryption can be
used to overcome repudiation.
4. InformationDisclosure:InformationDisclosureisan
attack that is used to infiltrate the network.
Infiltrators disclose information about the network
to the attacker. Attackerscantakecontrolofaswitch
and can change the flow entries. Controllers can
overcome this by regularly checking the switches
and the network for suspicious activities.
5. DoS: DoS is a major attack that has a huge effect on
the performance of a network. Dos might have
immense effect in SDN. It can affect the flows
between switchesandcontrollers.DoSinSDNcanbe
directed towardsthecontroller.Anattackercantake
control of switch and keep the controller busy with
various queries.
6. Elevation of Privilege: In this attack, the attacker
tries to increasehis/heraccessprivilegeleveltogain
access to privileged data. Attacker might try to gain
access of data and application which need special
privileges.
SDN is prone to a lot of vulnerabilities, one of them being
DDoS. DDoS is common, most harmful and most easy to
initiate.
The rest of the paper followsthis structure. In Section2,a
brief literaturesurvey is presented. In Section 3, DDoSattack
has been briefly discussed. DDoS detection and mitigation in
SDN is discussed in Section 4. In the end, the conclusion is
presented in Section 5.
2. LITERATURE SURVEY
Some of the paper that we studied have been discussed
below.
Author Jacob H. Cox et al. [1] throws light upon the
architecture of the SDN. It also shows a concern and surveys
the vulnerabilities of the same. Furthermore,italsodiscusses
the further research aspects in the SDN. Overall it gives a
clear vision about the pros and cons of the SDN architecture.
The authors Kübra Kalkan, Gürkan Gür, and Fatih Alagöz
[2] have discussed and categorized the solutions against
DDoS attacks in SDN. The solutions are categorized based on
the dependability of the aspects.
Neelam Dayal and Shashank Shrivastava [6] about the
DDoS attacks in their paper, in addition to the DDoS attack, it
also talks about the severity and gives a systematic
classification of the attack on the SDN architecture.
Roshni Mary Thomas and Divya James [7] have used the
traffic monitoringapproachtomonitorthenetworktrafficfor
the specific amount of time to detect DDoS attack. The iftop
tool is used for monitoring and Firewall is placed in POX
controller. Thus, if any attacker is found, the address will be
forwarded to firewall and this will detect and block the
packets.
Nhu-Ngoc Dao, Junho Park, Minho Park, and Sungrae Cho
[9] have implemented an idea which helps to protect the
network against DDoS. By IP filtering technique and using
Openflow protocol the user traffic is analyzed to detect and
prevent the attack. They havedefinedatempletable(Ttable)
in the controller. This method can decrease the impact of
DDoS effectively only when the amount of attack trafficisnot
very huge.
The authors Yandong Liu et al. [8] has discussed a secure
framework for mitigation of the DDoS attack using the deep
reinforcement learning which can learn the different attack
policies under different attack scenarios andhelpinavoiding
the DDoS flood attack. It basically analyses the attack pattern
and throttle’s the attacking traffic whereas it forwards the
packet coming from the benign sources. The proposed
framework has two modules first an Information collection
modules which uses the OpenFlow protocol and sends
message in the networkrequestingtheinformationrelatedto
the network traffic which isanalyzedhereandsecondaDDoS
mitigation module which uses the mitigation server and the
agent where the agent is a separate host which is equipped
with the deep reinforcement algorithm whereas the server
runs as an application on SDN. DDPG is the learning
algorithm used to train the agent.
2. DDOS ATTACK IN SDN
DoS is a major attack that has a huge effect on the
performance of a network. Dos might have immense effectin
SDN. It can affect theflowsbetweenswitchesandcontrollers.
DoS in SDN can be directed towards the controller. An
attacker can take control of switch and keep the controller
busy with various queries [5].
Below figure (Fig 2) shows spoofing attack. In this attack
the attacker targets the server by flooding the server by
sending an overwhelming amount of requests which
eventually breaks down the server. As a result, the requests
from the legitimate users cannot be addressed by the server
leading to a DDoS attack.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 450
Fig -2: Spoofing Attack (DDoS)
Distributed Denial of Service (DDoS) attack is one of the
severe security challenges faced by the SDN. It is a major
attack, which has disastrous/catastrophic effect on the
performance of the network. It disrupts the flow of the
network by attacking theservicenodeshenceobstructingthe
legitimate users from getting service.
DDoS in SDN can be directed towards data plane as well
as the control plane. In attack directed towards the control
plane, results in keeping controller busy with requests
resulting in wastage of computation time. Moreover, if the
attack is on controller it can affect the whole network
because controller being the heart ofthenetwork.Legitimate
request might be discarded.DoSattackcanbedetectedbythe
controller when a large amount of flow is detected from a
single source. DoS is easy to identify. Spoofingislesseffective
in SDN due to continuous monitoring and updating policies.
Man in the Middle (MiM) attack can be executed by taking
control over a switch. Also, attacker can also enter a system
by impersonating a switch. To overcome this SDN Controller
should verify every switch before allowing it into the
network.
DDoS attacks broadly classified into two categories DDoS
attack on data plane and control plane [6].
Following figure classification of DDoS attacks.
Fig -3: Classification of DDoS attacks
1.2 DDoS Plane DDoS
Thiscategory has 2 sub-categories viz, volumetricattackand
protocol exploitationattack.Involumetricattacktheattacker
sends a huge amount of request to the victim. As a result, the
victim tries to fulfil these requests eventually the victim
overwhelmed by amount of packets received, hence crashes
and fails to complete the requests. Basically, this attack
overwhelms the victim with a large amount of request
resulting in failing the server. Example of this attack is the
ICMP flood, UDP flood, Smurf attack.
Whereas the protocol exploitation targets the device
resources andapplicationresources,thatisitaffectsthemain
functioning of the victim by exhausting the resourcessuchas
memory, bandwidth, etc. An example of this attack is
SYN_FLOOD attack where the attacker continuously sends
SYN packets without waitingfortheACKfromthereceiveron
the other hand the server keeps on allocatingmemoryforthe
SYN requests eventually exhausting the memory, thus,
leading to the failureof the network. Another example ofthis
type is the ping of death.
On the other hand, the in the exhaustion application
resources basicallysendsconnectionrequestsandeventually
over burdens the victim resulting in failing to acknowledge
any more connection requests
1.2 Control Plane DDoS
In this category of attack, the control plane is at a risk. The
attacker sends an excessive randomflowtotheswitch,which
eventually leads to a scenario where the switch misses the
attackand sendsa Packet_Inmessage, which symbolizestwo
things first, it cannot process the message, or it has miss a
packet. The other type of attack is the one which affects the
bandwidth of the OpenFlow protocol. In this case too, the
Packet_In messages are sent as the controller is unable to
process the request. This eventually leads to the exhaustion
of the band width which brings the network down.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 451
2. DDOS DETECTION AND MITIGATION IN SDN
Various SDN features can be used in stopping DDoS. SDN
architectureofferscentralized control of thenetwork.Alocal
controller always monitors and controls the network. The
controller can take actions during abnormal network
conditions. There are numerous applications used forpacket
capturing and traffic analysis in SDN. SDN allows us to set
dynamic flow rules. Policy rules are applied to the whole
network. SDN allowsdynamicupdatingofFirewallsrulesand
forwarding rules.
1.2 DDoS Detection in SDN
DDoS attack floods the target with large number of packets.
DDoS can be detected when the volume of packets in a
networkincreasesuddenly.TherearevariousDDoSdetection
strategies based on different approaches.
A. Detection for Volumetric Attacks
Volumetric attacks include icmp flood, udp flood, smurf
attack etc. In this type of large number of packets flow
through the network. These types of attacks can be detected
by setting a threshold. This threshold denotes the average
number of packets flowing per second. If the number of
packets flowing per second ismore than the threshold, DDoS
attack is being performed.
Chaitanya Buragohain and Nabajyoti Medhi [10]
introduceFlowTrApp,aDDoSattackdetectionandmitigation
mechanism. FlowTrApp is implemented using FloodLight
Controller in mininet environment. It uses sFlow to collect
flow information. Authors use flow rate and flow duration as
parameters. They perform a UDP Flood DDoS attack. DDoS
attack is detected by comparing the values of these
parameters. Malicious devices are blocked.
Babatunde Hafis Lawal and Nuray AT [11] implement a
real time detection and mitigation of DDoS attack. Authors
use sFlow tool to monitor the network. They determine a
threshold for packets per seconds. They perform an ICMP
Flood attack. DDoS attack is detectedifthenumberofpackets
flowing through the network is higher than the threshold.
B. Detection for Protocol Exploitation Attacks
Protocol exploitation attacks include SYN-Flood attack and
HTTP attack. Volume of packets in this type of attack is
usually less than Volumetric attack. They focus on resource
exploitation of the target node. Although number of packets
are less, these types of attacks are more severe on the node.
Protocol Exploitation attacks can be detected by packet
inspection. Packets are checked for abnormal values. InSYN-
Flood attack, the MSS value in an abnormal packet is usually
lower.
Authors Tushar Ubale and Ankit Kumar Jain [12] have
implemented SRL, a system which provides security against
TCP SYN Flood DDoS attack.Thispaperintroduces2modules
in SRL, Hashing module and Flow Aggregator module.
Hashing module is used to remove fake values from flow
table, while Flow Aggregator module is used to limit the
number of requests to the server. Hence it successfully
mitigates SYN FLOOD DDoS attack.
Seungwon Shin et al [13] propose AVANT-GUARD to
mitigateSYN Flood DDoS. Itreliesonthefactthattheattacker
never tries to complete the TCP handshake. Attacker never
sends a TCP-ACK packet. AVANT- GUARD is implemented on
switches. It acts as a temporary server whichacceptstheTCP
connection. If the connection is completed the module
connects with the actual server. It then reports the
failed/passed connections to the controller.
C. Detection using ML Algorithms
DDoS attacks can be detected using Machine Learning
algorithms. ML is used to classify of packets. Algorithms are
trained using classified dataset. After training it is capable to
classify form normal to abnormal packets. Various ML
algorithms are used for intrusion detection. Algorithms like
Naïve Bayes, SVM, Decision Tree etc are used in packet
inspection.
Saif Saad Mohammed et al. [14] propose a Machine
Learning based DDoS mitigation system. Authors have used
NSL-KDD dataset. An attack detection server is created to
detect a DDoS attack. Naive-Bayes Algorithm is used in this
paper. The classifier is trained and tested using the data
provided by NSL-KDD dataset. Authentication module and
Wrapper module are also included in ML Server. DDoS
attacks are successfully mitigated using Machine Learning
techniques.
Yao Yu et al. [15] implement a DDoS detection platform.
The authors use machine learning algorithm. They depict a
vehicular network in SDN. Algorithm used is SVM. Features
are extracted from PACKET_IN, this data is used to train the
algorithm. SVM training model is implemented in the
controller. Suspicious packets are sent to the SVM model,
which are classified by the model. Hence DDoS attack is
detected using SVM algorithm.
1.2 DDoS Mitigation
A DDoS attack cannot be fully prevented, as sometimes
legitimate clients could be mistaken as attackers. Once a
DDoS attack is detected, is needs tobemitigated.DDoScanbe
mitigated by identifying the attacker and blocking it from
making any further requests.
SDN enables us toadd dynamic policies.Custom rulesare
added in the switches which allows us to block users.
Firewalls are used to block IP/MAC addresses.
Various algorithms limit the number of requests a node
can make to a server. For example, in a situation where a
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 452
client needs to have a TCP connection with the server, a limit
can be decided for how many SYN packets it can send to the
server in specific time period. The nodes which exceed these
limits can be blocked by installing an IP/MAC based firewall
rule. The nodewill then be blockedfrommakingarequestfor
a certain period.
Fig -4: DDoS Mitigation architecture in SDN
Above figure shows a basic working of the mitigation
architecture of the DDoS attack on control plane. There is a
module in the control plane which collectsthetraffic flowing
through the network. This data of the traffic is sent to the
DDoS detection module where the packet analysis and a
detection algorithm is applied to see if the network is under
attack. If found that the network is under attack the
mitigation module is informed about the attack and the
module takes the required action against it. These actions
include blocking the suspicious hosts.
2. CONCLUSION
Hence, we studied about the new and emerging technology,
SDN and the threats that it comes with. In addition to this,
we discussed about the DDoS attack on the SDN. Different
types of DDoS attacks were reviewed. This paper reviews
various DDoS detectionapproaches.Italsothrowslightupon
the mitigation measures taken against the DDoS on this
architecture.
REFERENCES
[1] J. H. Cox et al., "AdvancingSoftware-DefinedNetworks:A
Survey," in IEEE Access, vol. 5, pp. 25487-25526, 2017.
[2] K. Kalkan, G. Gur and F. Alagoz, "Defense Mechanisms
against DDoS Attacks in SDN Environment," in IEEE
Communications Magazine, vol. 55, no. 9, pp. 175-179,
Sept. 2017.
[3] Alsmadi, Izzat, and Xu, Dianxiang. (2015). "Security of
Software Defined Networks: A Survey". Computers &
Security, 53, 79-108.
[4] Siddhant Shah, Shailesh Bendale, "An Intuitive Study:
Intrusion Detection Systems and Anomalies,HowAIcan
be used as a tool to enable the majority, in 5G era.",
ICCUBEA, 2019
[5] S. P. Bendale and J. Rajesh Prasad, "Security Threats and
Challenges in Future Mobile Wireless Networks," 2018
IEEE Global Conference on Wireless Computing and
Networking (GCWCN), Lonavala, India, 2018, pp. 146-
150.
[6] N. Dayal and S. Srivastava, "Analyzing behavior of DDoS
attacks to identify DDoS detection features in SDN,"
2017 9th International Conference on Communication
Systems and Networks (COMSNETS), Bangalore, 2017,
pp. 274-281.
[7] R. M. Thomas and D. James, "DDOS detection and denial
using third partyapplication inSDN,"2017International
Conference on Energy, Communication, Data Analytics
and Soft Computing (ICECDS), Chennai, 2017, pp. 3892-
3897.
[8] Y. Liu, M. Dong, K. Ota, J. Li and J. Wu, "Deep
ReinforcementLearningbasedSmartMitigationofDDoS
Flooding in Software-Defined Networks," 2018 IEEE
23rd International Workshop on Computer Aided
Modeling and Design of Communication Links and
Networks (CAMAD), Barcelona, 2018, pp. 1-6.
[9] Nhu-Ngoc Dao, Junho Park, Minho Park and Sungrae
Cho, "A feasible method to combat against DDoS attack
in SDN network," 2015 International Conference on
Information Networking (ICOIN), Cambodia, 2015, pp.
309-311
[10] C. Buragohain and N. Medhi, "FlowTrApp:AnSDN based
architecture for DDoS attack detection andmitigationin
data centers," 2016 3rd International Conference on
Signal Processing and Integrated Networks (SPIN),
Noida, 2016, pp. 519-524.
[11] B. H. Lawal and A. T. Nuray, "Real-time detection and
mitigation of distributed denial of service (DDoS)
attacks in software defined networking (SDN)," 2018
26th Signal Processing and Communications
Applications Conference (SIU), Izmir, 2018, pp. 1-4.
[12] Ubale, Tushar & Jain, Ankit. (2018). SRL: An TCP
SYNFLOOD DDoS Mitigation Approach in Software-
Defined Networks. 956-962.
10.1109/ICECA.2018.8474561.
[13] Shin, Seungwon & Yegneswaran, Vinod & Porras, Phillip
& Gu, Guofei. (2013). AVANT-GUARD: scalable and
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 453
vigilant switch flow management in software-defined
networks. 10.1145/2508859.2516684.
[14] S. S. Mohammed et al., "A New Machine Learning-based
Collaborative DDoS Mitigation Mechanism in Software-
Defined Network," 2018 14th International Conference
on Wireless and Mobile Computing, Networking and
Communications (WiMob), Limassol, 2018, pp. 1-8.
[15] Y. Yu, L. Guo, Y. Liu, J. Zheng and Y. Zong, "An Efficient
SDN-Based DDoS Attack Detection and Rapid Response
Platform in Vehicular Networks," in IEEE Access, vol. 6,
pp. 44570-44579, 2018.

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IRJET- A Study of DDoS Attacks in Software Defined Networks

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 448 A Study of DDoS Attacks in Software Defined Networks Chinmay Dharmadhikari1, Salil Kulkarni2, Swarali Temkar3, Shailesh Bendale4 1,2,3B.E. Student, Dept. of Computer Engineering, NBN Sinhgad School of Engineering, Ambegoan, Pune- 411041, Maharashtra, India 4Professor, Dept. of Computer. Engineering, NBN Sinhgad School of Engineering, Ambegaon, Pune – 411041, Maharashtra, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Virtualization has changed the ball game completely in all the technological aspect. One of such application of it is SDN. Software Defined Network (SDN) is currently the most popular network architecture in use. SDN separates the Data Plane from the Control Network. It introduces controllers, based on the OpenFlow protocol, to control the switches and routers in the data plane. Although SDN provides a simplified way to control the network, it gives rise to new security threats such as DoS attacks, Man in the Middle attacks etc. SDN being centralized it has a single point of failure which makes it vulnerable. One of the most common among them is DDoS. It affects the server whichfailsthewhole network in addition to this it is easy to start this attack. Therefore, in order to detect and mitigatethesevulnerabilities there have been several algorithms and approaches that has been enforced. In this paper a study various types of DDoS attack on SDN has been discussed, also the mitigation and detection of the attack has been studied. Key Words: Software Defined Networks (SDN), DDoS, Security. 1. INTRODUCTION As SDN has dramatically changed the working of the network and made it more efficient by centralizing the control of the network with thehelpofcontroller,inaddition to that it also separates the data plane fromthecontrol plane which helps in achieving the goal of centralization [1]. The architecture of the SDN is as follows. SDN consists of 2 planes- Data Plane and Control Plane. Data plane is the actual network where the packets are forwarded. Switches and routers are the main components of data plane. Control plane consists of the controller as the main component. Controller is used to control the switches in the data plane. It provides central management and monitoring of the network. The control plane consists of controller and its 3 interfaces. 1. South-Bound Interface: Provides an interface between switches and controller. 2. North-Bound Interface: Provides an interface between controller and application layer program. 3. East-West Bound Interface: Provides an interface between the different controllers. Following figure depicts basic architecture of SDN. Fig -1: SDN Architecture SDN hasseveraladvantageslikecentralizedmanagement, monitoring, centralized services and security. Various Controllers are available in the market. Some of the Controllers are OpenDayLight, FloodLight, Pox, Ryu etc. SDN currently doesnothaveasecuritymodule.Securityis the main concern of SDN. Although SDN architecture overcomes some the traditional attacks, it also gives rise to new attack strategies. Security concerns may arise due to some unintentional actions from the devices inside the network, or intentionalattacksperformedbymalicioususers [4]. Attacks on SDN can be classified as Spoofing, Tampering, Information Disclosure, Repudiation, Elevation of Privileges and DoS [3]. 1. Spoofing: Spoofing is falsifying the information about the attacker. Spoofing is a part of a larger attack. Attacker can perform ARP Spoofing and IP Spoofing.Chances of Spoofing are reducedinSDNas
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 449 controller makes dynamic and regular changes in the network. 2. Tampering: Altering of network information is known as Tampering. Attackers might change the information in the flow tablesof switches.Theymay alter the firewall policies to deny services to some users. 3. Repudiation: Denial of one of the host in 2-way communication is Repudiation. Encryption can be used to overcome repudiation. 4. InformationDisclosure:InformationDisclosureisan attack that is used to infiltrate the network. Infiltrators disclose information about the network to the attacker. Attackerscantakecontrolofaswitch and can change the flow entries. Controllers can overcome this by regularly checking the switches and the network for suspicious activities. 5. DoS: DoS is a major attack that has a huge effect on the performance of a network. Dos might have immense effect in SDN. It can affect the flows between switchesandcontrollers.DoSinSDNcanbe directed towardsthecontroller.Anattackercantake control of switch and keep the controller busy with various queries. 6. Elevation of Privilege: In this attack, the attacker tries to increasehis/heraccessprivilegeleveltogain access to privileged data. Attacker might try to gain access of data and application which need special privileges. SDN is prone to a lot of vulnerabilities, one of them being DDoS. DDoS is common, most harmful and most easy to initiate. The rest of the paper followsthis structure. In Section2,a brief literaturesurvey is presented. In Section 3, DDoSattack has been briefly discussed. DDoS detection and mitigation in SDN is discussed in Section 4. In the end, the conclusion is presented in Section 5. 2. LITERATURE SURVEY Some of the paper that we studied have been discussed below. Author Jacob H. Cox et al. [1] throws light upon the architecture of the SDN. It also shows a concern and surveys the vulnerabilities of the same. Furthermore,italsodiscusses the further research aspects in the SDN. Overall it gives a clear vision about the pros and cons of the SDN architecture. The authors Kübra Kalkan, Gürkan Gür, and Fatih Alagöz [2] have discussed and categorized the solutions against DDoS attacks in SDN. The solutions are categorized based on the dependability of the aspects. Neelam Dayal and Shashank Shrivastava [6] about the DDoS attacks in their paper, in addition to the DDoS attack, it also talks about the severity and gives a systematic classification of the attack on the SDN architecture. Roshni Mary Thomas and Divya James [7] have used the traffic monitoringapproachtomonitorthenetworktrafficfor the specific amount of time to detect DDoS attack. The iftop tool is used for monitoring and Firewall is placed in POX controller. Thus, if any attacker is found, the address will be forwarded to firewall and this will detect and block the packets. Nhu-Ngoc Dao, Junho Park, Minho Park, and Sungrae Cho [9] have implemented an idea which helps to protect the network against DDoS. By IP filtering technique and using Openflow protocol the user traffic is analyzed to detect and prevent the attack. They havedefinedatempletable(Ttable) in the controller. This method can decrease the impact of DDoS effectively only when the amount of attack trafficisnot very huge. The authors Yandong Liu et al. [8] has discussed a secure framework for mitigation of the DDoS attack using the deep reinforcement learning which can learn the different attack policies under different attack scenarios andhelpinavoiding the DDoS flood attack. It basically analyses the attack pattern and throttle’s the attacking traffic whereas it forwards the packet coming from the benign sources. The proposed framework has two modules first an Information collection modules which uses the OpenFlow protocol and sends message in the networkrequestingtheinformationrelatedto the network traffic which isanalyzedhereandsecondaDDoS mitigation module which uses the mitigation server and the agent where the agent is a separate host which is equipped with the deep reinforcement algorithm whereas the server runs as an application on SDN. DDPG is the learning algorithm used to train the agent. 2. DDOS ATTACK IN SDN DoS is a major attack that has a huge effect on the performance of a network. Dos might have immense effectin SDN. It can affect theflowsbetweenswitchesandcontrollers. DoS in SDN can be directed towards the controller. An attacker can take control of switch and keep the controller busy with various queries [5]. Below figure (Fig 2) shows spoofing attack. In this attack the attacker targets the server by flooding the server by sending an overwhelming amount of requests which eventually breaks down the server. As a result, the requests from the legitimate users cannot be addressed by the server leading to a DDoS attack.
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 450 Fig -2: Spoofing Attack (DDoS) Distributed Denial of Service (DDoS) attack is one of the severe security challenges faced by the SDN. It is a major attack, which has disastrous/catastrophic effect on the performance of the network. It disrupts the flow of the network by attacking theservicenodeshenceobstructingthe legitimate users from getting service. DDoS in SDN can be directed towards data plane as well as the control plane. In attack directed towards the control plane, results in keeping controller busy with requests resulting in wastage of computation time. Moreover, if the attack is on controller it can affect the whole network because controller being the heart ofthenetwork.Legitimate request might be discarded.DoSattackcanbedetectedbythe controller when a large amount of flow is detected from a single source. DoS is easy to identify. Spoofingislesseffective in SDN due to continuous monitoring and updating policies. Man in the Middle (MiM) attack can be executed by taking control over a switch. Also, attacker can also enter a system by impersonating a switch. To overcome this SDN Controller should verify every switch before allowing it into the network. DDoS attacks broadly classified into two categories DDoS attack on data plane and control plane [6]. Following figure classification of DDoS attacks. Fig -3: Classification of DDoS attacks 1.2 DDoS Plane DDoS Thiscategory has 2 sub-categories viz, volumetricattackand protocol exploitationattack.Involumetricattacktheattacker sends a huge amount of request to the victim. As a result, the victim tries to fulfil these requests eventually the victim overwhelmed by amount of packets received, hence crashes and fails to complete the requests. Basically, this attack overwhelms the victim with a large amount of request resulting in failing the server. Example of this attack is the ICMP flood, UDP flood, Smurf attack. Whereas the protocol exploitation targets the device resources andapplicationresources,thatisitaffectsthemain functioning of the victim by exhausting the resourcessuchas memory, bandwidth, etc. An example of this attack is SYN_FLOOD attack where the attacker continuously sends SYN packets without waitingfortheACKfromthereceiveron the other hand the server keeps on allocatingmemoryforthe SYN requests eventually exhausting the memory, thus, leading to the failureof the network. Another example ofthis type is the ping of death. On the other hand, the in the exhaustion application resources basicallysendsconnectionrequestsandeventually over burdens the victim resulting in failing to acknowledge any more connection requests 1.2 Control Plane DDoS In this category of attack, the control plane is at a risk. The attacker sends an excessive randomflowtotheswitch,which eventually leads to a scenario where the switch misses the attackand sendsa Packet_Inmessage, which symbolizestwo things first, it cannot process the message, or it has miss a packet. The other type of attack is the one which affects the bandwidth of the OpenFlow protocol. In this case too, the Packet_In messages are sent as the controller is unable to process the request. This eventually leads to the exhaustion of the band width which brings the network down.
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 451 2. DDOS DETECTION AND MITIGATION IN SDN Various SDN features can be used in stopping DDoS. SDN architectureofferscentralized control of thenetwork.Alocal controller always monitors and controls the network. The controller can take actions during abnormal network conditions. There are numerous applications used forpacket capturing and traffic analysis in SDN. SDN allows us to set dynamic flow rules. Policy rules are applied to the whole network. SDN allowsdynamicupdatingofFirewallsrulesand forwarding rules. 1.2 DDoS Detection in SDN DDoS attack floods the target with large number of packets. DDoS can be detected when the volume of packets in a networkincreasesuddenly.TherearevariousDDoSdetection strategies based on different approaches. A. Detection for Volumetric Attacks Volumetric attacks include icmp flood, udp flood, smurf attack etc. In this type of large number of packets flow through the network. These types of attacks can be detected by setting a threshold. This threshold denotes the average number of packets flowing per second. If the number of packets flowing per second ismore than the threshold, DDoS attack is being performed. Chaitanya Buragohain and Nabajyoti Medhi [10] introduceFlowTrApp,aDDoSattackdetectionandmitigation mechanism. FlowTrApp is implemented using FloodLight Controller in mininet environment. It uses sFlow to collect flow information. Authors use flow rate and flow duration as parameters. They perform a UDP Flood DDoS attack. DDoS attack is detected by comparing the values of these parameters. Malicious devices are blocked. Babatunde Hafis Lawal and Nuray AT [11] implement a real time detection and mitigation of DDoS attack. Authors use sFlow tool to monitor the network. They determine a threshold for packets per seconds. They perform an ICMP Flood attack. DDoS attack is detectedifthenumberofpackets flowing through the network is higher than the threshold. B. Detection for Protocol Exploitation Attacks Protocol exploitation attacks include SYN-Flood attack and HTTP attack. Volume of packets in this type of attack is usually less than Volumetric attack. They focus on resource exploitation of the target node. Although number of packets are less, these types of attacks are more severe on the node. Protocol Exploitation attacks can be detected by packet inspection. Packets are checked for abnormal values. InSYN- Flood attack, the MSS value in an abnormal packet is usually lower. Authors Tushar Ubale and Ankit Kumar Jain [12] have implemented SRL, a system which provides security against TCP SYN Flood DDoS attack.Thispaperintroduces2modules in SRL, Hashing module and Flow Aggregator module. Hashing module is used to remove fake values from flow table, while Flow Aggregator module is used to limit the number of requests to the server. Hence it successfully mitigates SYN FLOOD DDoS attack. Seungwon Shin et al [13] propose AVANT-GUARD to mitigateSYN Flood DDoS. Itreliesonthefactthattheattacker never tries to complete the TCP handshake. Attacker never sends a TCP-ACK packet. AVANT- GUARD is implemented on switches. It acts as a temporary server whichacceptstheTCP connection. If the connection is completed the module connects with the actual server. It then reports the failed/passed connections to the controller. C. Detection using ML Algorithms DDoS attacks can be detected using Machine Learning algorithms. ML is used to classify of packets. Algorithms are trained using classified dataset. After training it is capable to classify form normal to abnormal packets. Various ML algorithms are used for intrusion detection. Algorithms like Naïve Bayes, SVM, Decision Tree etc are used in packet inspection. Saif Saad Mohammed et al. [14] propose a Machine Learning based DDoS mitigation system. Authors have used NSL-KDD dataset. An attack detection server is created to detect a DDoS attack. Naive-Bayes Algorithm is used in this paper. The classifier is trained and tested using the data provided by NSL-KDD dataset. Authentication module and Wrapper module are also included in ML Server. DDoS attacks are successfully mitigated using Machine Learning techniques. Yao Yu et al. [15] implement a DDoS detection platform. The authors use machine learning algorithm. They depict a vehicular network in SDN. Algorithm used is SVM. Features are extracted from PACKET_IN, this data is used to train the algorithm. SVM training model is implemented in the controller. Suspicious packets are sent to the SVM model, which are classified by the model. Hence DDoS attack is detected using SVM algorithm. 1.2 DDoS Mitigation A DDoS attack cannot be fully prevented, as sometimes legitimate clients could be mistaken as attackers. Once a DDoS attack is detected, is needs tobemitigated.DDoScanbe mitigated by identifying the attacker and blocking it from making any further requests. SDN enables us toadd dynamic policies.Custom rulesare added in the switches which allows us to block users. Firewalls are used to block IP/MAC addresses. Various algorithms limit the number of requests a node can make to a server. For example, in a situation where a
  • 5. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 452 client needs to have a TCP connection with the server, a limit can be decided for how many SYN packets it can send to the server in specific time period. The nodes which exceed these limits can be blocked by installing an IP/MAC based firewall rule. The nodewill then be blockedfrommakingarequestfor a certain period. Fig -4: DDoS Mitigation architecture in SDN Above figure shows a basic working of the mitigation architecture of the DDoS attack on control plane. There is a module in the control plane which collectsthetraffic flowing through the network. This data of the traffic is sent to the DDoS detection module where the packet analysis and a detection algorithm is applied to see if the network is under attack. If found that the network is under attack the mitigation module is informed about the attack and the module takes the required action against it. These actions include blocking the suspicious hosts. 2. CONCLUSION Hence, we studied about the new and emerging technology, SDN and the threats that it comes with. In addition to this, we discussed about the DDoS attack on the SDN. Different types of DDoS attacks were reviewed. This paper reviews various DDoS detectionapproaches.Italsothrowslightupon the mitigation measures taken against the DDoS on this architecture. REFERENCES [1] J. H. Cox et al., "AdvancingSoftware-DefinedNetworks:A Survey," in IEEE Access, vol. 5, pp. 25487-25526, 2017. [2] K. Kalkan, G. Gur and F. Alagoz, "Defense Mechanisms against DDoS Attacks in SDN Environment," in IEEE Communications Magazine, vol. 55, no. 9, pp. 175-179, Sept. 2017. [3] Alsmadi, Izzat, and Xu, Dianxiang. (2015). "Security of Software Defined Networks: A Survey". Computers & Security, 53, 79-108. [4] Siddhant Shah, Shailesh Bendale, "An Intuitive Study: Intrusion Detection Systems and Anomalies,HowAIcan be used as a tool to enable the majority, in 5G era.", ICCUBEA, 2019 [5] S. P. Bendale and J. Rajesh Prasad, "Security Threats and Challenges in Future Mobile Wireless Networks," 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), Lonavala, India, 2018, pp. 146- 150. [6] N. Dayal and S. Srivastava, "Analyzing behavior of DDoS attacks to identify DDoS detection features in SDN," 2017 9th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, 2017, pp. 274-281. [7] R. M. Thomas and D. James, "DDOS detection and denial using third partyapplication inSDN,"2017International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017, pp. 3892- 3897. [8] Y. Liu, M. Dong, K. Ota, J. Li and J. Wu, "Deep ReinforcementLearningbasedSmartMitigationofDDoS Flooding in Software-Defined Networks," 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Barcelona, 2018, pp. 1-6. [9] Nhu-Ngoc Dao, Junho Park, Minho Park and Sungrae Cho, "A feasible method to combat against DDoS attack in SDN network," 2015 International Conference on Information Networking (ICOIN), Cambodia, 2015, pp. 309-311 [10] C. Buragohain and N. Medhi, "FlowTrApp:AnSDN based architecture for DDoS attack detection andmitigationin data centers," 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2016, pp. 519-524. [11] B. H. Lawal and A. T. Nuray, "Real-time detection and mitigation of distributed denial of service (DDoS) attacks in software defined networking (SDN)," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4. [12] Ubale, Tushar & Jain, Ankit. (2018). SRL: An TCP SYNFLOOD DDoS Mitigation Approach in Software- Defined Networks. 956-962. 10.1109/ICECA.2018.8474561. [13] Shin, Seungwon & Yegneswaran, Vinod & Porras, Phillip & Gu, Guofei. (2013). AVANT-GUARD: scalable and
  • 6. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 453 vigilant switch flow management in software-defined networks. 10.1145/2508859.2516684. [14] S. S. Mohammed et al., "A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software- Defined Network," 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Limassol, 2018, pp. 1-8. [15] Y. Yu, L. Guo, Y. Liu, J. Zheng and Y. Zong, "An Efficient SDN-Based DDoS Attack Detection and Rapid Response Platform in Vehicular Networks," in IEEE Access, vol. 6, pp. 44570-44579, 2018.