scholarly journals A Multi-Criteria based Software Defined Networking System Architecture for DDoS-Attack Mitigation

2017 ◽  
Author(s):  
Tuyen Dang-Van ◽  
Huong Truong-Thu
Keyword(s):  
Network Architecture ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Software Defined Networking ◽  
Buffer Size ◽  
Ddos Attacks ◽  
Control Plane ◽  
Network Resource ◽  
Attack Mitigation ◽  
Sdn Controller

Nowadays, Software-Defined Networking (SDN) has become a promising network architecture in which network devices are controlled in a separate Control Plane (i.e., SDN controller). In a specific aspect, employing SDN in a network offers an attractive network security solution due to its flexibility in building and adding more new software security rules. From another perspective, attack prediction and mitigation, especially for Distributed Denial of Service (DDoS) attacks, are still challenges in SDN environments since a SDN control system works probably slower than a non-SDN one and theSDN controller can become a target of attacks. In this article, at first, we analyze a real traffic use case in order to derive DDoS indicators and thresholds. Secondly, we design an Openflow/SDN-based Attack Mitigation Architecture that is able to quickly mitigate DDoS attacks on the fly. The design solves the existing problems of the Openflow protocol, reducing the traffic volume traversing over the interface between the data plane (switch) and the control plane (SDN controller) and decreasing the buffer size at the Openflow switch. Applying our proposed Fuzzy Logic-based DDoS Mitigation algorithm that deploys multiple criteria for DDoS detection - FDDoM, the system demonstrates the ability to detect and filter 97% of attack flows and reach a False Positive Rate of 5% that are acceptable figures in real system management. The results also show that the network resource which is required to cope and maintain flow entries is 50% reduced during attack time.

scholarly journals Proposed statistical-based approach for detecting distribute denial of service against the controller of software defined network (SADDCS)

2018 ◽  
Vol 218 ◽  
pp. 02012 ◽  
Author(s):  
Mohammad A. AL-Adaileh ◽  
Mohammed Anbar ◽  
Yung-Wey Chong ◽  
Ahmed Al-Ani
Keyword(s):  
Statistical Analysis ◽  
Denial Of Service ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Ddos Attacks ◽  
Large Area ◽  
Network Resources ◽  
Network Resource ◽  
High Bandwidth ◽  
Sdn Controller

Software-defined networkings (SDNs) have grown rapidly in recent years be-cause of SDNs are widely used in managing large area networks and securing networks from Distributed Denial of Services (DDoS) attacks. SDNs allow net-works to be monitored and managed through centralized controller. Therefore, SDN controllers are considered as the brain of networks and are considerably vulnerable to DDoS attacks. Thus, SDN controller suffer from several challenges that exhaust network resources. For SDN controller, the main target of DDoS attacks is to prevent legitimate users from using a network resource or receiving their services. Nevertheless, some approaches have been proposed to detect DDoS attacks through the examination of the traffic behavior of networks. How-ever, these approaches take too long to process all incoming packets, thereby leading to high bandwidth consumption and delays in the detection of DDoS at-tacks. In addition, most existing approaches for the detection of DDoS attacks suffer from high positive/negative false rates and low detection accuracy. This study proposes a new approach to detecting DDoS attacks. The approach is called the statistical-based approach for detecting DDoS against the controllers of software-defined networks. The proposed approach is designed to detect the presence of DDoS attacks accurately, reduce false positive/negative flow rates, and minimize the complexity of targeting SDN controllers according to a statistical analysis of packet features. The proposed approach passively captures net-work traffic, filters traffic, and selects the most significant features that contribute to DDoS attack detection. The general stages of the proposed approach are (i) da-ta preprocessing, (ii) statistical analysis, (iii) correlation identification between two vectors, and (iv) rule-based DDoS detection.

scholarly journals Mitigation of DDoS attack instigated by compromised switches on SDN controller by analyzing the flow rule request traffic

2018 ◽  
Vol 7 (2.6) ◽  
pp. 46 ◽  
Author(s):  
Sanjeetha R ◽  
Shikhar Srivastava ◽  
Rishab Pokharna ◽  
Syed Shafiq ◽  
Dr Anita Kanavalli
Keyword(s):  
Network Architecture ◽  
Flow Rule ◽  
Software Defined Network ◽  
Ddos Attacks ◽  
Control Plane ◽  
Ddos Attack ◽  
Flow Table ◽  
Data Plane ◽  
Ddos Detection ◽  
Sdn Controller

Software Defined Network (SDN) is a new network architecture which separates the data plane from the control plane. The SDN controller implements the control plane and switches implement the data plane. Many papers discuss about DDoS attacks on primary servers present in SDN and how they can be mitigated with the help of controller. In our paper we show how DDoS attack can be instigated on the SDN controller by manipulating the flow table entries of switches, such that they send continuous requests to the controller and exhaust its resources. This is a new, but one of the possible way in which a DDoS attack can be performed on controller. We show the vulnerability of SDN for this kind of attack. We further propose a solution for mitigating it, by running a DDoS Detection module which uses variation of flow entry request traffic from all switches in the network to identify compromised switches and blocks them completely.

scholarly journals ADAPTIVE ENTROPY-BASED DETECTION AND MITIGATION OF DDOS ATTACKS IN SOFTWARE DEFINED NETWORKS

2020 ◽  
pp. 399-410
Author(s):  
Jawad Dalou' ◽  
Basheer Al-Duwairi ◽  
Mohammad Al-Jarrah
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
Point Of View ◽  
Software Defined Networks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Control Plane ◽  
Data Plane ◽  
And Control ◽  
Sdn Controller

Software Defined Networking (SDN) has emerged as a new networking paradigm that is based on the decoupling between data plane and control plane providing several benefits that include flexible, manageable, and centrally controlled networks. From a security point of view, SDNs suffer from several vulnerabilities that are associated with the nature of communication between control plane and data plane. In this context, software defined networks are vulnerable to distributed denial of service attacks. In particular, the centralization of the SDN controller makes it an attractive target for these attacks because overloading the controller with huge packet volume would result in bringing the whole network down or degrade its performance. Moreover, DDoS attacks may have the objective of flooding a network segment with huge traffic volume targeting single or multiple end systems. In this paper, we propose an entropy-based mechanism for Distributed Denial of Service (DDoS) attack detection and mitigation in SDN networks. The proposed mechanism is based on the entropy values of source and destination IP addresses of flows observed by the SDN controller which are compared to a preset entropy threshold values that change in adaptive manner based on network dynamics. The proposed mechanism has been evaluated through extensive simulation experiments.

scholarly journals Detection and Mitigation of Flood Attacks in IPv6 Enabled Software Defined Networks

2020 ◽  
pp. 1-9
Author(s):  
O. Ashimi Quadri ◽  
Adeniji Oluwashola David
Keyword(s):  
Network Architecture ◽  
Denial Of Service ◽  
Cost Effective ◽  
Centralized Control ◽  
Ddos Attacks ◽  
Control Plane ◽  
Network Resources ◽  
Effective Manner ◽  
Data Plane ◽  
Experimental Testbed

Software-defined networking (SDN) is an emerging technology, which provides network architecture that decouples the control plane from the data plane. Due to the centralized control, the network becomes more dynamic, and the network resources are managed in a more efficient and cost-effective manner. The centralization of the control plane requires robust and real-time security techniques. The security Techniques will protect it from any sign of vulnerabilities associated with the network such as a distributed denial of service (DDoS) attacks. The problem of the data-plane is that the attack is hard to be tracked by the SDN controlling plane. This makes the switches to be more susceptible against these types of attacks and hence it is very important to have quick provisional methods in place to prevent the switches from breaking down as soon as first signs of an attack are detected. To resolve this problem, the research developed a mechanism that detects and mitigates flood attacks in IPv6 enabled software to define networks. An experimental testbed was developed using sFlow technique, floodlight controller, and OpenFlow version 1.3. A mitigation algorithm was also developed and was tested with a simulation tool Mininet. The real network traffic was tested on the testbed to investigate the effective mitigation of a DDoS attack. The mitigation time performance for IPv6 was 46.6% while IPv4 was 66.6%. Also, The result gathered from the experiment showed that both the response and detection times were 4 secs while the mitigation time was 7secs respectively. The overall control time being 11 secs. The experimental Testbed result shows that the developed testbed outperformed the previous methods with the ability to detect threats on the network faster. The result from the IPv6 testbed is a probable solution to mitigate the threats posed by DDoS attacks on the IPv6 enabled SDN network resources.

scholarly journals Optimal software-defined network topology for distributed denial of service attack mitigation

2020 ◽  
Vol 9 (6) ◽  
pp. 2588-2594
Author(s):  
Branislav Mladenov ◽  
Georgi Iliev
Keyword(s):  
Network Topology ◽  
Network Architecture ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Optimal Network ◽  
Service Attack ◽  
Network Topologies ◽  
Sdn Controller

Distributed denial of service (DDoS) attacks are a major threat to all internet services. The main goal is to disrupt normal traffic and overwhelms the target. Software-defined networking (SDN) is a new type of network architecture where control and data plane are separated. A successful attack may block the SDN controller which may stop processing the new request and will lead to a total disruption of the whole network. The main goal of this paper is to find the optimal network topology and size which can handle Distributed denial of service attack without management channel bandwidth exhaustion or run out of SDN controller CPU and memory. Through simulations, it is shown that mesh topologies with more connections between switches are more resistant to DDoS attacks than liner type network topologies. 

An intelligent flow-based and signature-based IDS for SDNs using ensemble feature selection and a multi-layer machine learning-based classifier

2020 ◽  
pp. 1-20
Author(s):  
K. Muthamil Sudar ◽  
P. Deepalakshmi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Architecture ◽  
Detection System ◽  
Software Defined Networking ◽  
Control Plane ◽  
Control Logic ◽  
Data Plane

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

scholarly journals Evaluation of Takagi-Sugeno-Kang fuzzy method in entropy-based detection of DDoS attacks

2018 ◽  
Vol 15 (1) ◽  
pp. 139-162 ◽  
Author(s):  
Miodrag Petkovic ◽  
Ilija Basicevic ◽  
Dragan Kukolj ◽  
Miroslav Popovic
Keyword(s):  
Network Traffic ◽  
Fuzzy System ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Sudden Change ◽  
Change Point Detection ◽  
Statistical Characteristics ◽  
Ddos Attacks ◽  
Detection Process ◽  
Takagi Sugeno

The detection of distributed denial of service (DDoS) attacks based on internet traffic anomalies is a method which is general in nature and can detect unknown or zero-day attacks. One of the statistical characteristics used for this purpose is network traffic entropy: a sudden change in entropy may indicate a DDoS attack. However, this approach often gives false positives, and this is the main obstacle to its wider deployment within network security equipment. In this paper, we propose a new, two-step method for detection of DDoS attacks. This method combines the approaches of network traffic entropy and the Takagi-Sugeno-Kang fuzzy system. In the first step, the detection process calculates the entropy distribution of the network packets. In the second step, the Takagi-Sugeno-Kang fuzzy system (TSK-FS) method is applied to these entropy values. The performance of the TSK-FS method is compared with that of the typically used approach, in which cumulative sum (CUSUM) change point detection is applied directly to entropy time series. The results show that the TSK-FS DDoS detector reaches enhanced sensitivity and robustness in the detection process, achieving a high true-positive detection rate and a very low false-positive rate. As it is based on entropy, this combined method retains its generality and is capable of detecting various types of attack.

scholarly journals A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller

2018 ◽  
Vol 10 (9) ◽  
pp. 83 ◽  
Author(s):  
Wentao Wang ◽  
Xuan Ke ◽  
Lingxia Wang
Keyword(s):  
Data Center ◽  
False Positive ◽  
Data Flow ◽  
Denial Of Service ◽  
True Positive ◽  
Ddos Attacks ◽  
Data Center Network ◽  
Data Packets ◽  
Positive Rate ◽  
Sdn Controller

A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze data packets entering the data center network and calculate the Renyi entropies base on IP of data packets, and then combine them with the hidden Markov model to get a probability model HMM-R to detect L-DDoS attacks at different rates. Compared with the four common attack detection algorithms (KNN, SVM, SOM, BP), HMM-R is superior to them in terms of the true positive rate, the false positive rate, and the adaptivity.

scholarly journals SGS: Safe-Guard Scheme for Protecting Control Plane Against DDoS Attacks in Software-Defined Networking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 34699-34710 ◽  
Author(s):  
Yang Wang ◽  
Tao Hu ◽  
Guangming Tang ◽  
Jichao Xie ◽  
Jie Lu
Keyword(s):  
Software Defined Networking ◽  
Ddos Attacks ◽  
Control Plane

scholarly journals A Feature Analysis Based Identifying Scheme Using GBDT for DDoS with Multiple Attack Vectors

2019 ◽  
Vol 9 (21) ◽  
pp. 4633 ◽  
Author(s):  
Jian Zhang ◽  
Qidi Liang ◽  
Rui Jiang ◽  
Xi Li
Keyword(s):  
Success Rate ◽  
Denial Of Service ◽  
Detection Algorithm ◽  
Feature Analysis ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Detection Capability ◽  
Ddos Attack ◽  
Attack Mitigation ◽  
Multiple Attack

In recent years, distributed denial of service (DDoS) attacks have increasingly shown the trend of multiattack vector composites, which has significantly improved the concealment and success rate of DDoS attacks. Therefore, improving the ubiquitous detection capability of DDoS attacks and accurately and quickly identifying DDoS attack traffic play an important role in later attack mitigation. This paper proposes a method to efficiently detect and identify multivector DDoS attacks. The detection algorithm is applicable to known and unknown DDoS attacks.

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