Real time DDoS detection using fuzzy estimators

Shiaeles, Stavros; Katos, Vasilios; Καράκος, Αλέξανδρος; Papadopoulos, Basil K.

doi:10.1016/j.cose.2012.06.002

Cited by 79 publications

(60 citation statements)

References 34 publications

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“…In [101], authors achieve DDoS detection with improved time limits through non-asymptotic fuzzy estimators. The estimator is deployed on mean packet inter-arrival times.…”

Section: Basis Of Defense Methodsmentioning

confidence: 99%

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A Survey on DDoS Attack and Defense Strategies: From Traditional Schemes to Current Techniques

Aamir

Zaidi

2013

IIS

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Distributed Denial of Service (DDoS) attacks exhaust victim's bandwidth or services. Traditional architecture of Internet is vulnerable to DDoS attacks and an ongoing cycle of attack & defense is observed. A recent attack report of year 2013 -'Quarter 1' from Prolexic Technologies identifies that 1.75 percent increase in total number of DDoS attacks has been recorded as compared to similar attacks of previous year's last quarter. In this paper, different types and techniques of DDoS attacks and their countermeasures are surveyed. The significance of this paper is the coverage of many aspects of countering DDoS attacks including new research on the topic. We survey different papers describing methods of defense against DDoS attacks based on entropy variations, traffic anomaly parameters, neural networks, device level defense, botnet flux identifications, application layer DDoS defense and countermeasures in wireless networks, CCN & cloud computing environments. We also discuss some traditional methods of defense such as traceback and packet filtering techniques, so that readers can identify major differences between traditional and current techniques of defense against DDoS attacks. We identify that application layer DDoS attacks possess the ability to produce greater impact on the victim as they are driven by legitimate-like traffic, making it quite difficult to identify and distinguish from legitimate requests. The need of improved defense against such attacks is therefore more demanding in research. The study conducted in this paper can be helpful for readers and researchers to recognize better techniques of defense in current times against DDoS attacks and contribute with more research on this topic in the light of future challenges identified in this paper.

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“…In [101], authors achieve DDoS detection with improved time limits through non-asymptotic fuzzy estimators. The estimator is deployed on mean packet inter-arrival times.…”

Section: Basis Of Defense Methodsmentioning

confidence: 99%

“…Non-asymptotic Fuzzy Estimator [101] Deploying proposed fuzzy estimator on mean packet inter-arrival times to detect DDoS attacks and identify victims' IP addresses.…”

Section: Defense Against Ddos Attacks In Wsns and Manetsmentioning

confidence: 99%

A Survey on DDoS Attack and Defense Strategies: From Traditional Schemes to Current Techniques

Aamir

Zaidi

2013

IIS

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“…A fuzzy estimator-based model [13] is proposed, not only to identify DDoS attacks, but also to detect offending IP addresses. It takes mean packet arrival time as the primary input metric.…”

Section: Related Workmentioning

confidence: 99%

“…The fitness of the string/solution can be calculated with the fitness definition using Equation (13).…”

Section: • Step 2: Weights Initializationmentioning

confidence: 99%

Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks

Singh

Thongam

2016

Entropy

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Distributed denial-of-service (DDoS) attack is one of the major threats to the web server. The rapid increase of DDoS attacks on the Internet has clearly pointed out the limitations in current intrusion detection systems or intrusion prevention systems (IDS/IPS), mostly caused by application-layer DDoS attacks. Within this context, the objective of the paper is to detect a DDoS attack using a multilayer perceptron (MLP) classification algorithm with genetic algorithm (GA) as learning algorithm. In this work, we analyzed the standard EPA-HTTP (environmental protection agency-hypertext transfer protocol) dataset and selected the parameters that will be used as input to the classifier model for differentiating the attack from normal profile. The parameters selected are the HTTP GET request count, entropy, and variance for every connection. The proposed model can provide a better accuracy of 98.31%, sensitivity of 0.9962, and specificity of 0.0561 when compared to other traditional classification models.

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“…Fuzzy logic helps is appropriate for nonlinear systems and helps in solving the systems which have elements of uncertainly (Ma, 2010). DDoS cluster based on fuzzy mechanisms are adopted in a recent trends of security work (Stavros et al, 2012;Kumar and Selvakumar, 2012).…”

Section: Other Detection Schemesmentioning

confidence: 99%

Review Clustering Mechanisms of Distributed Denial of Service Attacks

Bhaya¹,

Manaa²

2014

Journal of Computer Science

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Distributed Denial of Service attacks (DDoS) overwhelm network resources with useless or harmful packets and prevent normal users from accessing these network resources. These attacks jeopardize the confidentiality, privacy and integrity of information on the internet. Since it is very difficult to set any predefined rules to correctly identify genuine network traffic, an anomaly-based Intrusion Detection System (IDS) for network security is commonly used to detect and prevent new DDoS attacks. Data mining methods can be used in intrusion detection systems, such as clustering k-means, artificial neural network. Since the clustering methods can be used to aggregate similar objects, they can detect DDoS attacks to reduce false-positive rates. In this study, a review of DDoS attacks using clustering data mining techniques is presented. A review illustrates the most recent, state-of-the art science for clustering techniques to detect DDoS attacks.

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Real time DDoS detection using fuzzy estimators

Cited by 79 publications

References 34 publications

A Survey on DDoS Attack and Defense Strategies: From Traditional Schemes to Current Techniques

A Survey on DDoS Attack and Defense Strategies: From Traditional Schemes to Current Techniques

Entropy-Based Application Layer DDoS Attack Detection Using Artificial Neural Networks

Review Clustering Mechanisms of Distributed Denial of Service Attacks

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