scholarly journals Signature-based Traffic Classification and Mitigation for DDoS Attacks using Programmable Network Data Planes

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Marinos Dimolianis ◽  
Adam Pavlidis ◽  
Vasilis Maglaris
Keyword(s):  
Network Data ◽  
Traffic Classification ◽  
Ddos Attacks ◽  
Programmable Network

Traffic Classification Features and its Application in DDoS Detection

2013 ◽  
Vol 380-384 ◽  
pp. 2673-2676
Author(s):  
Ze Yu Xiong
Keyword(s):  
Detection Method ◽  
Single Point ◽  
Attack Detection ◽  
Traffic Classification ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Detection ◽  
Point Detection ◽  
Ddos Attack Detection ◽  
Collaborative Detection

DDoS attacks have relatively low proportion of normal flow in the boundary network at the attack traffic,In this paper,we establish DDoS attack detection method based on defense stage and defensive position, and design and implement collaborative detection of DDoS attacks. Simulation results show that our approach has good timeliness, accuracy and scalability than the single-point detection and route-based distributed detection scheme.

TRAWLING TRAFFIC UNDER ATTACK OVERCOMING DDoS ATTACKS BY TARGET-CONTROLLED TRAFFIC FILTERING

2011 ◽  
Vol 22 (05) ◽  
pp. 1073-1098
Author(s):  
SHLOMI DOLEV ◽  
YUVAL ELOVICI ◽  
ALEX KESSELMAN ◽  
POLINA ZILBERMAN
Keyword(s):  
Denial Of Service ◽  
The Internet ◽  
Traffic Classification ◽  
Classification Rules ◽  
Ddos Attacks ◽  
Dos Attack ◽  
Dos Attacks ◽  
Worst Case ◽  
Filtering Algorithm ◽  
Internet Community

As more and more services are provided by servers via the Internet, Denial-of-Service (DoS) attacks pose an increasing threat to the Internet community. A DoS attack overloads the target server with a large volume of adverse requests, thereby rendering the server unavailable to "well-behaved" users. In this paper, we propose two algorithms that allow attack targets to dynamically filter their incoming traffic based on a distributed policy. The proposed algorithms defend the target against DoS and distributed DoS (DDoS) attacks and simultaneously ensure that it continues to serve "well-behaved" users. In a nutshell, a target can define a filtering policy which consists of a set of traffic classification rules and the corresponding amounts of traffic for each rule. A filtering algorithm is enforced by the ISP's routers when a target is being overloaded with traffic. The goal is to maximize the amount of filtered traffic forwarded to the target, according to the filtering policy, from the ISP. The first proposed algorithm is a collaborative algorithm which computes and delivers to the target the best possible traffic mix in polynomial time. The second algorithm is a distributed non-collaborative algorithm for which we prove a lower bound on the worst-case performance.

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 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>

Improving Discriminating Accuracy Rate of DDoS Attacks and Flash Events

2021 ◽  
Vol 11 (4) ◽  
pp. 21-42
Author(s):  
Sahareesh Agha ◽  
Osama Rehman ◽  
Ibrahim M. H. Rahman
Keyword(s):  
Denial Of Service ◽  
Internet Security ◽  
Classification Model ◽  
Traffic Classification ◽  
Ddos Attacks ◽  
Intelligent Network ◽  
Random Forest Algorithm ◽  
Accuracy Rate ◽  
Discrimination Accuracy ◽  
Network Traffic Classification

Internet security has become a big issue with the passage of time. Among many threats, the distributed denial-of-service (DDoS) attack is the most frequent threat in the networks. The purpose of the DDoS attacks is to interrupt service availability provided by different web servers. This results in legitimate users not being able to access the servers and hence facing denial of services. On the other hand, flash events are a high amount of legitimate users visiting a website due to a specific event. Consequences of these attacks are more powerful when launched during flash events, which are legitimate traffic and cause a denial of service. The purpose of this study is to build an intelligent network traffic classification model to improve the discrimination accuracy rate of DDoS attacks from flash events traffic. Weka is adopted as the platform for evaluating the performance of a random forest algorithm.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data
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