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2021-09-08
Potluri, Sirisha, Mangla, Monika, Satpathy, Suneeta, Mohanty, Sachi Nandan.  2020.  Detection and Prevention Mechanisms for DDoS Attack in Cloud Computing Environment. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.
For optimal use of cloud resources and to reduce the latency of cloud users, the cloud computing model extends the services such as networking facilities, computational capabilities and storage facilities based on demand. Due to the dynamic behavior, distributed paradigm and heterogeneity present among the processing elements, devices and service oriented pay per use policies; the cloud computing environment is having its availability, security and privacy issues. Among these various issues one of the important issues in cloud computing paradigm is DDoS attack. This paper put in plain words the DDoS attack, its detection as well as prevention mechanisms in cloud computing environment. The inclusive study also explains about the effects of DDoS attack on cloud platform and the related defense mechanisms required to be considered.
2021-09-07
Bülbül, Nuref\c san Sertba\c s, Fischer, Mathias.  2020.  SDN/NFV-Based DDoS Mitigation via Pushback. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1–6.
Distributed Denial of Service (DDoS) attacks aim at bringing down or decreasing the availability of services for their legitimate users, by exhausting network or server resources. It is difficult to differentiate attack traffic from legitimate traffic as the attack can come from distributed nodes that additionally might spoof their IP addresses. Traditional DoS mitigation solutions fail to defend all kinds of DoS attacks and huge DoS attacks might exceed the processing capacity of routers and firewalls easily. The advent of Software-defined Networking (SDN) and Network Function Virtualization (NFV) has brought a new perspective for network defense. Key features of such technologies like global network view and flexibly positionable security functionality can be used for mitigating DDoS attacks. In this paper, we propose a collaborative DDoS attack mitigation scheme that uses SDN and NFV. We adopt a machine learning algorithm from related work to derive accurate patterns describing DDoS attacks. Our experimental results indicate that our framework is able to differentiate attack and legitimate traffic with high accuracy and in near-realtime. Furthermore, the derived patterns can be used to create OpenFlow (OF) or Firewall rules that can be pushed back into the direction of the attack origin for more efficient and distributed filtering.
Zebari, Rizgar R., Zeebaree, Subhi R. M., Sallow, Amira Bibo, Shukur, Hanan M., Ahmad, Omar M., Jacksi, Karwan.  2020.  Distributed Denial of Service Attack Mitigation Using High Availability Proxy and Network Load Balancing. 2020 International Conference on Advanced Science and Engineering (ICOASE). :174–179.
Nowadays, cybersecurity threat is a big challenge to all organizations that present their services over the Internet. Distributed Denial of Service (DDoS) attack is the most effective and used attack and seriously affects the quality of service of each E-organization. Hence, mitigation this type of attack is considered a persistent need. In this paper, we used Network Load Balancing (NLB) and High Availability Proxy (HAProxy) as mitigation techniques. The NLB is used in the Windows platform and HAProxy in the Linux platform. Moreover, Internet Information Service (IIS) 10.0 is implemented on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 as web servers. We evaluated each load balancer efficiency in mitigating synchronize (SYN) DDoS attack on each platform separately. The evaluation process is accomplished in a real network and average response time and average CPU are utilized as metrics. The results illustrated that the NLB in the Windows platform achieved better performance in mitigation SYN DDOS compared to HAProxy in the Linux platform. Whereas, the average response time of the Window webservers is reduced with NLB. However, the impact of the SYN DDoS on the average CPU usage of the IIS 10.0 webservers was more than those of the Apache 2 webservers.
Al'aziz, Bram Andika Ahmad, Sukarno, Parman, Wardana, Aulia Arif.  2020.  Blacklisted IP Distribution System to Handle DDoS Attacks on IPS Snort Based on Blockchain. 2020 6th Information Technology International Seminar (ITIS). :41–45.
The mechanism for distributing information on the source of the attack by combining blockchain technology with the Intrusion Prevention System (IPS) can be done so that DDoS attack mitigation becomes more flexible, saves resources and costs. Also, by informing the blacklisted Internet Protocol(IP), each IPS can share attack source information so that attack traffic blocking can be carried out on IPS that are closer to the source of the attack. Therefore, the attack traffic passing through the network can be drastically reduced because the attack traffic has been blocked on the IPS that is closer to the attack source. The blocking of existing DDoS attack traffic is generally carried out on each IPS without a mechanism to share information on the source of the attack so that each IPS cannot cooperate. Also, even though the DDoS attack traffic did not reach the server because it had been blocked by IPS, the attack traffic still flooded the network so that network performance was reduced. Through smart contracts on the Ethereum blockchain, it is possible to inform the source of the attack or blacklisted IP addresses without requiring additional infrastructure. The blacklisted IP address is used by IPS to detect and handle DDoS attacks. Through the blacklisted IP distribution scheme, testing and analysis are carried out to see information on the source of the attack on each IPS and the attack traffic that passes on the network. The result is that each IPS can have the same blacklisted IP so that each IPS can have the same attack source information. The results also showed that the attack traffic through the network infrastructure can be drastically reduced. Initially, the total number of attack packets had an average of 115,578 reduced to 27,165.
Sanjeetha, R., Srivastava, Shikhar, Kanavalli, Anita, Pattanaik, Ashutosh, Gupta, Anshul.  2020.  Mitigation of Combined DDoS Attack on SDN Controller and Primary Server in Software Defined Networks Using a Priority on Traffic Variation. 2020 International Conference for Emerging Technology (INCET). :1–5.
A Distributed Denial of Service ( DDoS ) attack is usually instigated on a primary server that provides important services in a network. However such DDoS attacks can be identified and mitigated by the controller in a Software Defined Network (SDN). If the intruder further performs an attack on the controller along with the server, the attack becomes successful.In this paper, we show how such a combined DDoS attack can be instigated on a controller as well as a primary server. The DDoS attack on the primary server is instigated by compromising few hosts to send packets with spoofed IP addresses and the attack on the controller is instigated by compromising few switches to send flow table requests repeatedly to the controller. With the help of an emulator called mininet, we show the severity of this attack on the performance of the network. We further propose a common technique that can be used to mitigate this kind of attack by observing the variation of destination IP addresses and setting different priorities to switches and handling the flow table requests accordingly by the controller.
Jonker, Mattijs, Sperotto, Anna, Pras, Aiko.  2020.  DDoS Mitigation: A Measurement-Based Approach. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1–6.
Society heavily relies upon the Internet for global communications. Simultaneously, Internet stability and reliability are continuously subject to deliberate threats. These threats include (Distributed) Denial-of-Service (DDoS) attacks, which can potentially be devastating. As a result of DDoS, businesses lose hundreds of millions of dollars annually. Moreover, when it comes to vital infrastructure, national safety and even lives could be at stake. Effective defenses are therefore an absolute necessity. Prospective users of readily available mitigation solutions find themselves having many shapes and sizes to choose from, the right fit of which may, however, not always be apparent. In addition, the deployment and operation of mitigation solutions may come with hidden hazards that need to be better understood. Policy makers and governments also find themselves facing questions concerning what needs to be done to promote cybersafety on a national level. Developing an optimal course of action to deal with DDoS, therefore, also brings about societal challenges. Even though the DDoS problem is by no means new, the scale of the problem is still unclear. We do not know exactly what it is we are defending against and getting a better understanding of attacks is essential to addressing the problem head-on. To advance situational awareness, many technical and societal challenges need still to be tackled. Given the central importance of better understanding the DDoS problem to improve overall Internet security, the thesis that we summarize in this paper has three main contributions. First, we rigorously characterize attacks and attacked targets at scale. Second, we advance knowledge about the Internet-wide adoption, deployment and operational use of various mitigation solutions. Finally, we investigate hidden hazards that can render mitigation solutions altogether ineffective.
2021-08-11
Steinberger, Jessica, Sperotto, Anna, Baier, Harald, Pras, Aiko.  2020.  Distributed DDoS Defense:A collaborative Approach at Internet Scale. NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium. :1–6.
Distributed large-scale cyber attacks targeting the availability of computing and network resources still remain a serious threat. To limit the effects caused by those attacks and to provide a proactive defense, mitigation should move to the networks of Internet Service Providers (ISPs). In this context, this thesis focuses on a development of a collaborative, automated approach to mitigate the effects of Distributed Denial of Service (DDoS) attacks at Internet Scale. This thesis has the following contributions: i) a systematic and multifaceted study on mitigation of large-scale cyber attacks at ISPs. ii) A detailed guidance selecting an exchange format and protocol suitable to use to disseminate threat information. iii) To overcome the shortcomings of missing flow-based interoperability of current exchange formats, a development of the exchange format Flow-based Event Exchange Format (FLEX). iv) A communication process to facilitate the automated defense in response to ongoing network-based attacks, v) a model to select and perform a semi-automatic deployment of suitable response actions. vi) An investigation of the effectiveness of the defense techniques moving-target using Software Defined Networking (SDN) and their applicability in context of large-scale cyber attacks and the networks of ISPs. Finally, a trust model that determines a trust and a knowledge level of a security event to deploy semi-automated remediations and facilitate the dissemination of security event information using the exchange format FLEX in context of ISP networks.
2021-04-27
Junosza-Szaniawski, K., Nogalski, D., Wójcik, A..  2020.  Exact and approximation algorithms for sensor placement against DDoS attacks. 2020 15th Conference on Computer Science and Information Systems (FedCSIS). :295–301.
In DDoS attack (Distributed Denial of Service), an attacker gains control of many network users by a virus. Then the controlled users send many requests to a victim, leading to lack of its resources. DDoS attacks are hard to defend because of distributed nature, large scale and various attack techniques. One of possible ways of defense is to place sensors in the network that can detect and stop an unwanted request. However, such sensors are expensive so there is a natural question about a minimum number of sensors and their optimal placement to get the required level of safety. We present two mixed integer models for optimal sensor placement against DDoS attacks. Both models lead to a trade-off between the number of deployed sensors and the volume of uncontrolled flow. Since above placement problems are NP-hard, two efficient heuristics are designed, implemented and compared experimentally with exact linear programming solvers.
2021-04-09
Fadhilah, D., Marzuki, M. I..  2020.  Performance Analysis of IDS Snort and IDS Suricata with Many-Core Processor in Virtual Machines Against Dos/DDoS Attacks. 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP). :157—162.
The rapid development of technology makes it possible for a physical machine to be converted into a virtual machine, which can operate multiple operating systems that are running simultaneously and connected to the internet. DoS/DDoS attacks are cyber-attacks that can threaten the telecommunications sector because these attacks cause services to be disrupted and be difficult to access. There are several software tools for monitoring abnormal activities on the network, such as IDS Snort and IDS Suricata. From previous studies, IDS Suricata is superior to IDS Snort version 2 because IDS Suricata already supports multi-threading, while IDS Snort version 2 still only supports single-threading. This paper aims to conduct tests on IDS Snort version 3.0 which already supports multi-threading and IDS Suricata. This research was carried out on a virtual machine with 1 core, 2 core, and 4 core processor settings for CPU, memory, and capture packet attacks on IDS Snort version 3.0 and IDS Suricata. The attack scenario is divided into 2 parts: DoS attack scenario using 1 physical computer, and DDoS attack scenario using 5 physical computers. Based on overall testing, the results are: In general, IDS Snort version 3.0 is better than IDS Suricata. This is based on the results when using a maximum of 4 core processor, in which IDS Snort version 3.0 CPU usage is stable at 55% - 58%, a maximum memory of 3,000 MB, can detect DoS attacks with 27,034,751 packets, and DDoS attacks with 36,919,395 packets. Meanwhile, different results were obtained by IDS Suricata, in which CPU usage is better compared to IDS Snort version 3.0 with only 10% - 40% usage, and a maximum memory of 1,800 MB. However, the capabilities of detecting DoS attacks are smaller with 3,671,305 packets, and DDoS attacks with a total of 7,619,317 packets on a TCP Flood attack test.
2021-03-09
Memos, V. A., Psannis, K. E..  2020.  AI-Powered Honeypots for Enhanced IoT Botnet Detection. 2020 3rd World Symposium on Communication Engineering (WSCE). :64—68.

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

Muhammad, A., Asad, M., Javed, A. R..  2020.  Robust Early Stage Botnet Detection using Machine Learning. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1—6.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

2021-02-16
Li, R., Wu, B..  2020.  Early detection of DDoS based on φ-entropy in SDN networks. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:731—735.
Software defined network (SDN) is an emerging network architecture. Its control logic and forwarding logic are separated. SDN has the characteristics of centralized management, which makes it easier for malicious attackers to use the security vulnerabilities of SDN networks to implement distributed denial Service (DDoS) attack. Information entropy is a kind of lightweight DDoS early detection method. This paper proposes a DDoS attack detection method in SDN networks based on φ-entropy. φ-entropy can adjust related parameters according to network conditions and enlarge feature differences between normal and abnormal traffic, which can make it easier to detect attacks in the early stages of DDoS traffic formation. Firstly, this article demonstrates the basic properties of φ-entropy, mathematically illustrates the feasibility of φ-entropy in DDoS detection, and then we use Mini-net to conduct simulation experiments to compare the detection effects of DDoS with Shannon entropy.
Başkaya, D., Samet, R..  2020.  DDoS Attacks Detection by Using Machine Learning Methods on Online Systems. 2020 5th International Conference on Computer Science and Engineering (UBMK). :52—57.
DDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN), C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.
Zhai, P., Song, Y., Zhu, X., Cao, L., Zhang, J., Yang, C..  2020.  Distributed Denial of Service Defense in Software Defined Network Using OpenFlow. 2020 IEEE/CIC International Conference on Communications in China (ICCC). :1274—1279.
Software Defined Network (SDN) is a new type of network architecture solution, and its innovation lies in decoupling traditional network system into a control plane, a data plane, and an application plane. It logically implements centralized control and management of the network, and SDN is considered to represent the development trend of the network in the future. However, SDN still faces many security challenges. Currently, the number of insecure devices is huge. Distributed Denial of Service (DDoS) attacks are one of the major network security threats.This paper focuses on the detection and mitigation of DDoS attacks in SDN. Firstly, we explore a solution to detect DDoS using Renyi entropy, and we use exponentially weighted moving average algorithm to set a dynamic threshold to adapt to changes of the network. Second, to mitigate this threat, we analyze the historical behavior of each source IP address and score it to determine the malicious source IP address, and use OpenFlow protocol to block attack source.The experimental results show that the scheme studied in this paper can effectively detect and mitigate DDoS attacks.
2020-12-28
Kumar, R., Mishra, A. K., Singh, D. K..  2020.  Packet Loss Avoidance in Mobile Adhoc Network by using Trusted LDoS Techniques. 2nd International Conference on Data, Engineering and Applications (IDEA). :1—5.
Packet loss detection and prevention is full-size module of MANET protection systems. In trust based approach routing choices are managed with the aid of an unbiased have faith table. Traditional trust-based techniques unsuccessful to notice the essential underlying reasons of a malicious events. AODV is an approachable routing set of guidelines i.e.it finds a supply to an endpoint only on request. LDoS cyber-attacks ship assault statistics packets after period to time in a brief time period. The community multifractal ought to be episodic when LDoS cyber-attacks are hurled unpredictably. Real time programs in MANET necessitate certain QoS advantages, such as marginal end-to-end facts packet interval and unobjectionable records forfeiture. Identification of malevolent machine, information security and impenetrable direction advent in a cell system is a key tasks in any wi-fi network. However, gaining the trust of a node is very challenging, and by what capability it be able to get performed is quiet ambiguous. This paper propose a modern methodology to detect and stop the LDoS attack and preserve innocent from wicked nodes. In this paper an approach which will improve the safety in community by identifying the malicious nodes using improved quality grained packet evaluation method. The approach also multiplied the routing protection using proposed algorithm The structure also accomplish covered direction-finding to defend Adhoc community against malicious node. Experimentally conclusion factor out that device is fine fabulous for confident and more advantageous facts communication.
2020-12-21
Bediya, A. K., Kumar, R..  2020.  Real Time DDoS Intrusion Detection and Monitoring Framework in 6LoWPAN for Internet of Things. 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON). :824–828.
The Internet of things is an extremely enormous space and still, IoT is spreading over a wide range of zones of development with very fast speed. The IoT is going to create a new world of efficient services. IoT is a collective system consisting of hardware like sensors, Radio Frequency Identification RFID, Bluetooth devices, Near Field Communication (NFC) devices, etc. and software that provides data queries, exchange, repository and exchanges, etc. Security of the IoT network is also a big and important issue of concern. This paper reviews the DDoS attack impact on IoT network and its mitigation methods for IoT in network, also discusses CoAP protocol, RPL protocol and 6LoWPAN network. This paper also represents the security framework to detect and monitor the DDoS attack for low power devices based IoT network.
2020-12-14
Kyaw, A. T., Oo, M. Zin, Khin, C. S..  2020.  Machine-Learning Based DDOS Attack Classifier in Software Defined Network. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :431–434.
Due to centralized control and programmable capability of the SDN architecture, network administrators can easily manage and control the whole network through the centralized controller. According to the SDN architecture, the SDN controller is vulnerable to distributed denial of service (DDOS) attacks. Thus, a failure of SDN controller is a major leak for security concern. The objectives of paper is therefore to detect the DDOS attacks and classify the normal or attack traffic in SDN network using machine learning algorithms. In this proposed system, polynomial SVM is applied to compare to existing linear SVM by using scapy, which is packet generation tool and RYU SDN controller. According to the experimental result, polynomial SVM achieves 3% better accuracy and 34% lower false alarm rate compared to Linear SVM.
2020-12-01
Di, A., Ruisheng, S., Lan, L., Yueming, L..  2019.  On the Large-Scale Traffic DDoS Threat of Space Backbone Network. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :192—194.

Satellite networks play an important role in realizing the combination of the space networks and ground networks as well as the global coverage of the Internet. However, due to the limitation of bandwidth resource, compared with ground network, space backbone networks are more likely to become victims of DDoS attacks. Therefore, we hypothesize an attack scenario that DDoS attackers make reflection amplification attacks, colluding with terminal devices accessing space backbone network, and exhaust bandwidth resources, resulting in degradation of data transmission and service delivery. Finally, we propose some plain countermeasures to provide solutions for future researchers.

2020-11-02
Siddiqui, Abdul Jabbar, Boukerche, Azzedine.  2018.  On the Impact of DDoS Attacks on Software-Defined Internet-of-Vehicles Control Plane. 2018 14th International Wireless Communications Mobile Computing Conference (IWCMC). :1284—1289.

To enhance the programmability and flexibility of network and service management, the Software-Defined Networking (SDN) paradigm is gaining growing attention by academia and industry. Motivated by its success in wired networks, researchers have recently started to embrace SDN towards developing next generation wireless networks such as Software-Defined Internet of Vehicles (SD-IoV). As the SD-IoV evolves, new security threats would emerge and demand attention. And since the core of the SD-IoV would be the control plane, it is highly vulnerable to Distributed Denial of Service (DDoS) Attacks. In this work, we investigate the impact of DDoS attacks on the controllers in a SD-IoV environment. Through experimental evaluations, we highlight the drastic effects DDoS attacks could have on a SD-IoV in terms of throughput and controller load. Our results could be a starting point to motivate further research in the area of SD-IoV security and would give deeper insights into the problems of DDoS attacks on SD-IoV.

2020-09-11
Mendes, Lucas D.P., Aloi, James, Pimenta, Tales C..  2019.  Analysis of IoT Botnet Architectures and Recent Defense Proposals. 2019 31st International Conference on Microelectronics (ICM). :186—189.
The rise in the number of devices joining the Internet of Things (IoT) has created a huge potential for distributed denial of service (DDoS) attacks, especially due to the lack of security in these computationally limited devices. Malicious actors have realized that and managed to turn large sets of IoT devices into botnets under their control. Given this scenario, this work studies botnet architectures identified so far and assesses how they are considered in the few recent defense proposals that consider botnet architectures.
2020-09-04
Baek, Ui-Jun, Ji, Se-Hyun, Park, Jee Tae, Lee, Min-Seob, Park, Jun-Sang, Kim, Myung-Sup.  2019.  DDoS Attack Detection on Bitcoin Ecosystem using Deep-Learning. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1—4.
Since Bitcoin, the first cryptocurrency that applied blockchain technology was developed by Satoshi Nakamoto, the cryptocurrency market has grown rapidly. Along with this growth, many vulnerabilities and attacks are threatening the Bitcoin ecosystem, which is not only at the bitcoin network-level but also at the service level that applied it, according to the survey. We intend to analyze and detect DDoS attacks on the premise that bitcoin's network-level data and service-level DDoS attacks with bitcoin are associated. We evaluate the results of the experiment according to the proposed metrics, resulting in an association between network-level data and service-level DDoS attacks of bitcoin. In conclusion, we suggest the possibility that the proposed method could be applied to other blockchain systems.
2020-06-29
Luo, Wenliang, Han, Wenzhi.  2019.  DDOS Defense Strategy in Software Definition Networks. 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). :186–190.
With the advent of the network economy and the network society, the network will enter a ubiquitous and omnipresent situation. Economic, cultural, military and social life will strongly depend on the network, while network security issues have become a common concern of all countries in the world. DDOS attack is undoubtedly one of the greatest threats to network security and the defense against DDOS attack is very important. In this paper, the principle of DDOS attack is summarized from the defensive purpose. Then the attack prevention in software definition network is analyzed, and the source, intermediate network, victim and distributed defense strategies are elaborated.
Ahuja, Nisha, Singal, Gaurav.  2019.  DDOS Attack Detection Prevention in SDN using OpenFlow Statistics. 2019 IEEE 9th International Conference on Advanced Computing (IACC). :147–152.
Software defined Network is a network defined by software, which is one of the important feature which makes the legacy old networks to be flexible for dynamic configuration and so can cater to today's dynamic application requirement. It is a programmable network but it is prone to different type of attacks due to its centralized architecture. The author provided a solution to detect and prevent Distributed Denial of service attack in the paper. Mininet [5] which is a popular emulator for Software defined Network is used. We followed the approach in which collection of the traffic statistics from the various switches is done. After collection we calculated the packet rate and bandwidth which shoots up to high values when attack take place. The abrupt increase detects the attack which is then prevented by changing the forwarding logic of the host nodes to drop the packets instead of forwarding. After this, no more packets will be forwarded and then we also delete the forwarding rule in the flow table. Hence, we are finding out the change in packet rate and bandwidth to detect the attack and to prevent the attack we modify the forwarding logic of the switch flow table to drop the packets coming from malicious host instead of forwarding it.
Kaljic, Enio, Maric, Almir, Njemcevic, Pamela.  2019.  DoS attack mitigation in SDN networks using a deeply programmable packet-switching node based on a hybrid FPGA/CPU data plane architecture. 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT). :1–6.
The application of the concept of software-defined networks (SDN) has, on the one hand, led to the simplification and reduction of switches price, and on the other hand, has created a significant number of problems related to the security of the SDN network. In several studies was noted that these problems are related to the lack of flexibility and programmability of the data plane, which is likely first to suffer potential denial-of-service (DoS) attacks. One possible way to overcome this problem is to increase the flexibility of the data plane by increasing the depth of programmability of the packet-switching nodes below the level of flow table management. Therefore, this paper investigates the opportunity of using the architecture of deeply programmable packet-switching nodes (DPPSN) in the implementation of a firewall. Then, an architectural model of the firewall based on a hybrid FPGA/CPU data plane architecture has been proposed and implemented. Realized firewall supports three models of DoS attacks mitigation: DoS traffic filtering on the output interface, DoS traffic filtering on the input interface, and DoS attack redirection to the honeypot. Experimental evaluation of the implemented firewall has shown that DoS traffic filtering at the input interface is the best strategy for DoS attack mitigation, which justified the application of the concept of deep network programmability.
Ahalawat, Anchal, Dash, Shashank Sekhar, Panda, Abinas, Babu, Korra Sathya.  2019.  Entropy Based DDoS Detection and Mitigation in OpenFlow Enabled SDN. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). :1–5.
Distributed Denial of Service(DDoS) attacks have become most important network security threat as the number of devices are connected to internet increases exponentially and reaching an attack volume approximately very high compared to other attacks. To make the network safe and flexible a new networking infrastructure such as Software Defined Networking (SDN) has come into effect, which relies on centralized controller and decoupling of control and data plane. However due to it's centralized controller it is prone to DDoS attacks, as it makes the decision of forwarding of packets based on rules installed in switch by OpenFlow protocol. Out of all different DDoS attacks, UDP (User Datagram Protocol) flooding constitute the most in recent years. In this paper, we have proposed an entropy based DDoS detection and rate limiting based mitigation for efficient service delivery. We have evaluated using Mininet as emulator and Ryu as controller by taking switch as OpenVswitch and obtained better result in terms of bandwidth utilization and hit ratio which consume network resources to make denial of service.