Neural Network-Based Approach for Detection and Mitigation of DDoS Attacks in SDN Environments

2020 ◽  
Vol 14 (3) ◽  
pp. 50-71
Author(s):  
Oussama Hannache ◽  
Mohamed Chaouki Batouche
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Analysis Method ◽  
Organizational Network ◽  
Ddos Attack ◽  
Data Plane ◽  
Ddos Detection

Software defined networking (SDN) is a networking paradigm that allows for the easy programmability of network devices by decoupling the data plane and the control plane. On the other hand, Distributed Denial of Service (DDoS) attacks remains one of the major concerns for organizational network infrastructures and Cloud providers. In this article, the authors propose a Neural Network based Traffic Flow Classifier (TFC-NN) for live DDoS detection in SDN environments. This study provides a live traffic analysis method with a neural network. The training of the TFC-NN model is performed by a labelled dataset constructed from SDN normal traffic and an-under DDoS traffic. The study also provides a live mitigation process combined with the live TFC-NN-based DDoS detection. The approach is deployed and evaluated on an SDN architecture based on different performance metrics with different under-DDoS attack scenarios.

scholarly journals A Traffic Cluster Entropy Based Approach to Distinguish DDoS Attacks from Flash Event Using DETER Testbed

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Monika Sachdeva ◽  
Krishan Kumar
Keyword(s):  
Research Laboratory ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Cyber Defense ◽  
Ddos Attack ◽  
Defense Technology ◽  
Different Types ◽  
Ddos Detection ◽  
Flash Event

The detection of distributed denial of service (DDoS) attacks is one of the hardest problems confronted by the network security researchers. Flash event (FE), which is caused by a large number of legitimate requests, has similar characteristics to those of DDoS attacks. Moreover DDoS attacks and FEs require altogether different handling procedures. So discriminating DDoS attacks from FEs is very important. But the research involving DDoS detection has not laid enough emphasis on including FEs scenarios in the experiments. In this paper, we are using traffic cluster entropy as detection metric not only to detect DDoS attacks but also to distinguish DDoS attacks from FEs. We have validated our approach on cyber-defense technology experimental research laboratory (DETER) testbed. Different emulation scenarios are created on DETER using mix of legitimate, flash, and different types of attacks at varying strengths. It is found that, when flash event is triggered, source address entropy increases but the corresponding traffic cluster entropy does not increase. However, when DDoS attack is launched, traffic cluster entropy also increases along with source address entropy. An analysis of live traces on DETER testbed clearly manifests supremacy of our approach.

scholarly journals An Efficient DDoS Attack Detecting System using Levenberg-Marquardt Based Deep Artificial Neural Network Approach for IOT

2021 ◽  
Vol 10 (3) ◽  
pp. 59-66
Author(s):  
Ahmed Saeed Alzahrani
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Rapid Development ◽  
Denial Of Service ◽  
Attack Detection ◽  
Smart Devices ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Levenberg Marquardt ◽  
Iot Devices

The Internet of Things model envisions the widespread interconnection and collaboration of smart devices over the present and future Internet environment. Threats and attacks against IoT devices and services are on the rise due to their rapid development. Distributed-Denial-of-Service (DDoS) attacks are one of the main dangerous malwares that attack targeted organizations through infected devices. Many mechanisms are developed for IoT devices in order to detect DDoS attacks. Nonetheless, the prevailing DDoS Attack Detection (DAD) methods involve time-delay and a lower detection rate. This paper proposed an efficient approach using the Levenberg-Marquardt Neural Network (LMDANN) algorithm for detecting the DDoS attacks in order to enhance prediction accuracy. In the proposed system, a MapReduce technique is used to eliminate the redundant copies. In addition, the Entropy-based Fisher’s Discriminate Function (ENTFDF) method was developed to reduce the features from the extracted features, and the system suggests an LMDANN algorithm to classify DDoS attack data separately from the normal data. In this, 80% of the data is used for training, and 20% of the data is used for testing. The performance of the proposed LMDANN method was evaluated in contrast to other art of state algorithms (ANN, SVM, KNN, and ANFIS) in terms of some specific qualitative performance metrics (recall, sensitivity, f-measure, specificity, precision, accuracy, and training time). The results show that the proposed detection approach can efficiently detect the DDoS attack in the IoT environment, achieving 96.35% accuracy.

scholarly journals Reducing distributed denial of service (DDoS) attacks using client puzzle mechanism

2017 ◽  
Vol 7 (1.1) ◽  
pp. 230
Author(s):  
C. Vasan Sai Krishna ◽  
Y. Bhuvana ◽  
P. Pavan Kumar ◽  
R. Murugan
Keyword(s):  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Dos Attack ◽  
Computing Power ◽  
Server Side ◽  
Ddos Attack ◽  
Client Machine ◽  
Client Side

In a typical DoS attack, the attacker tries to bring the server down. In this case, the attacker sends a lot of bogus queries to the server to consume its computing power and bandwidth. As the server’s bandwidth and computing power are always greater than attacker’s client machine, He seeks help from a group of connected computers. DDoS attack involves a lot of client machines which are hijacked by the attacker (together called as botnet). As the server handles all these requests sent by the attacker, all its resources get consumed and it cannot provide services. In this project, we are more concerned about reducing the computing power on the server side by giving the client a puzzle to solve. To prevent such attacks, we use client puzzle mechanism. In this mechanism, we introduce a client-side puzzle which demands the machine to perform tasks that require more resources (computation power). The client’s request is not directly sent to the server. Moreover, there will be an Intermediate Server to monitor all the requests that are being sent to the main server. Before the client’s request is sent to the server, it must solve a puzzle and send the answer. Intermediate Server is used to validate the answer and give access to the client or block the client from accessing the server.

Cluster-Based Countermeasures for DDoS Attacks

2016 ◽  
pp. 206-227
Author(s):  
Mohammad Jabed Morshed Chowdhury ◽  
Dileep Kumar G
Keyword(s):  
Unsupervised Learning ◽  
Network Traffic ◽  
Denial Of Service ◽  
Security Threats ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Real Progress ◽  
Bandwidth Capacity

Distributed Denial of Service (DDoS) attack is considered one of the major security threats in the current Internet. Although many solutions have been suggested for the DDoS defense, real progress in fighting those attacks is still missing. In this chapter, the authors analyze and experiment with cluster-based filtering for DDoS defense. In cluster-based filtering, unsupervised learning is used to create profile of the network traffic. Then the profiled traffic is passed through the filters of different capacity to the servers. After applying this mechanism, the legitimate traffic will get better bandwidth capacity than the malicious traffic. Thus the effect of bad or malicious traffic will be lesser in the network. Before describing the proposed solutions, a detail survey of the different DDoS countermeasures have been presented in the chapter.

Defending against Distributed Denial of Service

2011 ◽  
pp. 121-129
Author(s):  
Yang Xiang ◽  
Wanlei Zhou
Keyword(s):  
Web Sites ◽  
Credit Card ◽  
Denial Of Service ◽  
Online Gambling ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Web Traffic ◽  
Internet Applications ◽  
Ddos Attack ◽  
Interactive Advertising

Recently the notorious Distributed Denial of Service (DDoS) attacks made people aware of the importance of providing available data and services securely to users. A DDoS attack is characterized by an explicit attempt from an attacker to prevent legitimate users of a service from using the desired resource (CERT, 2006). For example, in February 2000, many Web sites such as Yahoo, Amazon.com, eBuy, CNN.com, Buy. com, ZDNet, E*Trade, and Excite.com were all subject to total or regional outages by DDoS attacks. In 2002, a massive DDoS attack briefly interrupted Web traffic on nine of the 13 DNS “root” servers that control the Internet (Naraine, 2002). In 2004, a number of DDoS attacks assaulted the credit card processor Authorize. net, the Web infrastructure provider Akamai Systems, the interactive advertising company DoubleClick (left that company’s servers temporarily unable to deliver ads to thousands of popular Web sites), and many online gambling sites (Arnfield, 2004). Nowadays, Internet applications face serious security problems caused by DDoS attacks. For example, according to CERT/CC Statistics 1998-2005 (CERT, 2006), computer-based vulnerabilities reported have increased exponentially since 1998. Effective approaches to defeat DDoS attacks are desperately demanded (Cisco, 2001; Gibson, 2002).

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 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.

scholarly journals Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow

2021 ◽  
Author(s):  
◽  
Jarrod Bakker
Keyword(s):  
Denial Of Service ◽  
Network Control ◽  
Traffic Classification ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Network Information ◽  
Ddos Attack ◽  
Machine Learning Methods ◽  
Attack Scenario ◽  
And Control

<p>Distributed denial of service (DDoS) attacks utilise many attacking entities to prevent legitimate use of a resource via consumption. Detecting these attacks is often difficult when using a traditional networking paradigm as network information and control are not centralised. Software-Defined Networking is a recent paradigm that centralises network control, thus improving the ability to gather network information. Traffic classification techniques can leverage the gathered data to detect DDoS attacks.This thesis utilises nmeta2, a SDN-based traffic classification architecture, to study the effectiveness of machine learning methods to detect DDoS attacks. These methods are evaluated on a physical network testbed to demonstrate their application during a DDoS attack scenario.</p>

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 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.

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