scholarly journals OverWatch: A Cross-Plane DDoS Attack Defense Framework with Collaborative Intelligence in SDN

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Biao Han ◽  
Xiangrui Yang ◽  
Zhigang Sun ◽  
Jinfeng Huang ◽  
Jinshu Su
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Ddos Attacks ◽  
Control Plane ◽  
Flow Monitoring ◽  
Ddos Attack ◽  
Data Plane ◽  
Collaborative Intelligence

Distributed Denial of Service (DDoS) attacks are one of the biggest concerns for security professionals. Traditional middle-box based DDoS attack defense is lack of network-wide monitoring flexibility. With the development of software-defined networking (SDN), it becomes prevalent to exploit centralized controllers to defend against DDoS attacks. However, current solutions suffer with serious southbound communication overhead and detection delay. In this paper, we propose a cross-plane DDoS attack defense framework in SDN, called OverWatch, which exploits collaborative intelligence between data plane and control plane with high defense efficiency. Attack detection and reaction are two key procedures of the proposed framework. We develop a collaborative DDoS attack detection mechanism, which consists of a coarse-grained flow monitoring algorithm on the data plane and a fine-grained machine learning based attack classification algorithm on the control plane. We propose a novel defense strategy offloading mechanism to dynamically deploy defense applications across the controller and switches, by which rapid attack reaction and accurate botnet location can be achieved. We conduct extensive experiments on a real-world SDN network. Experimental results validate the efficiency of our proposed OverWatch framework with high detection accuracy and real-time DDoS attack reaction, as well as reduced communication overhead on SDN southbound interface.

scholarly journals A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Fine Grained ◽  
Ddos Attack ◽  
Network Status ◽  
Ddos Attack Detection

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

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 A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

2021 ◽  
Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Fine Grained ◽  
Ddos Attack ◽  
Network Status ◽  
Ddos Attack Detection

Abstract In order to solve the problem of Distributed Denial of Service (DDoS) attack detection in software defined network (SDN), we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse grained initial detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

scholarly journals Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jieren Cheng ◽  
Chen Zhang ◽  
Xiangyan Tang ◽  
Victor S. Sheng ◽  
Zhe Dong ◽  
...  
Keyword(s):  
Detection Method ◽  
Denial Of Service ◽  
Multiple Kernel Learning ◽  
Attack Detection ◽  
Kernel Learning ◽  
Economic Losses ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Multiple Kernel ◽  
Ddos Attack Detection

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

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 Detecting High and Low Intensity Distributed Denial of Service (DDoS) Attacks

2021 ◽  
Author(s):  
◽  
Abigail Koay
Keyword(s):  
Information Sharing ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Traffic Classification ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Low Intensity ◽  
Ddos Attack ◽  
Ddos Attack Detection

<p>High and low-intensity attacks are two common Distributed Denial of Service (DDoS) attacks that disrupt Internet users and their daily operations. Detecting these attacks is important to ensure that communication, business operations, and education facilities can run smoothly. Many DDoS attack detection systems have been proposed in the past but still lack performance, scalability, and information sharing ability to detect both high and low-intensity DDoS attacks accurately and early. To combat these issues, this thesis studies the use of Software-Defined Networking technology, entropy-based features, and machine learning classifiers to develop three useful components, namely a good system architecture, a useful set of features, and an accurate and generalised traffic classification scheme. The findings from the experimental analysis and evaluation results of the three components provide important insights for researchers to improve the overall performance, scalability, and information sharing ability for building an accurate and early DDoS attack detection system.</p>

scholarly journals Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

2021 ◽  
Vol 30 (1) ◽  
Author(s):  
Francesco Musumeci ◽  
Ali Can Fidanci ◽  
Francesco Paolucci ◽  
Filippo Cugini ◽  
Massimo Tornatore
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Transmission Control Protocol ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Worst Case ◽  
Latency Reduction ◽  
Data Plane ◽  
Network Switches

Abstract Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of $$\upmu \text {s}$$ μ s in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language. Graphic Abstract

scholarly journals DDoS detection and prevention based on artificial intelligence techniques

2019 ◽  
Vol XXII (1) ◽  
pp. 134-143
Author(s):  
Glăvan D.
Keyword(s):  
Artificial Intelligence ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Ddos Attacks ◽  
Great Loss ◽  
Artificial Intelligence Techniques ◽  
Ddos Attack ◽  
Random Forest Tree ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of Things (IoT), artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.

scholarly journals IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1280-1303
Author(s):  
Jiushuang Wang ◽  
◽  
Ying Liu ◽  
Huifen Feng
Keyword(s):  
Internet Of Things ◽  
Firefly Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Attack ◽  
Improved Firefly Algorithm ◽  
Iot Devices ◽  
Ddos Attack Detection

<abstract><p>Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.</p></abstract>

scholarly journals DDoS attack detection using deep learning

2021 ◽  
Vol 10 (2) ◽  
pp. 382
Author(s):  
Thapanarath Khempetch ◽  
Pongpisit Wuttidittachotti
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Attack Surface ◽  
Iot Devices ◽  
Ddos Attack Detection ◽  
Flow Of Information

<span id="docs-internal-guid-58e12f40-7fff-ea30-01f6-fbbed132b03c"><span>Nowadays, IoT devices are widely used both in daily life and in corporate and industrial environments. The use of these devices has increased dramatically and by 2030 it is estimated that their usage will rise to 125 billion devices causing enormous flow of information. It is likely that it will also increase distributed denial-of-service (DDoS) attack surface. As IoT devices have limited resources, it is impossible to add additional security structures to it. Therefore, the risk of DDoS attacks by malicious people who can take control of IoT devices, remain extremely high. In this paper, we use the CICDDoS2019 dataset as a dataset that has improved the bugs and introducing a new taxonomy for DDoS attacks, including new classification based on flows network. We propose DDoS attack detection using the deep neural network (DNN) and long short-term memory (LSTM) algorithm. Our results show that it can detect more than 99.90% of all three types of DDoS attacks. The results indicate that deep learning is another option for detecting attacks that may cause disruptions in the future.</span></span>

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