Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers

Dick, Uwe; Scheffer, Tobias

doi:10.1007/s10994-016-5581-9

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…PTrace controlled such attack sources from two aspects, packet filtering and malware tracing, to prevent the cloud from becoming a tool for DDOS attacks. Other studies such as [13] approach the problem of filtering by using a set of security services called filter trees. In the study, XML and HTTP based DDOS attacks are filtered out using five filters for detection and resolution.…”

Section: Related Workmentioning

confidence: 99%

DDOS Detection on Internet of Things Using Unsupervised Algorithms

Odumuyiwa

Alabi

2021

JCSANDM

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The increase in the deployment of IOT networks has improved productivity of humans and organisations. However, IOT networks are increasingly becoming platforms for launching DDOS attacks due to inherent weaker security and resource-constrained nature of IOT devices. This paper focusses on detecting DDOS attack in IOT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDOS attacks. Emphasis was laid on exploitation based DDOS attacks which include Transmission Control Protocol SYN-Flood attacks and UDP-Lag attacks. Mirai, BASHLITE and CICDDOS2019 datasets were used in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.

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Section: Related Workmentioning

confidence: 99%

DDOS Detection on Internet of Things Using Unsupervised Algorithms

Odumuyiwa

Alabi

2021

JCSANDM

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“…Trace controlled such attack sources from two aspects, packet filtering and malware tracing, to prevent the cloud from becoming a tool for DDoS attacks. Other studies such as [13] approach the problem of filtering by using a set of security services called filter trees. In the study, XML and HTTP based DDOS attacks are filtered out using five filters for detection and resolution.…”

Section: Related Workmentioning

confidence: 99%

DDoS Detection on Internet of Things using Unsupervised Algorithms

Tekleselassie

2021

E3S Web Conf.

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Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

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“…Table 1 shows the characteristics of the data flow of each client, the characteristics of IP packets in a time interval and the behavior patterns of each user. They are extracted at intervals of time when a client connects to a domain [51]. These characteristics are of the statistical type and record the client's access to system resources and the frequency with which each client requests a resource in the domain.…”

Section: Literature Review Of Featuresmentioning

confidence: 99%

“…[56] Users browsing process We see average and total length of such browsing sequences. [51] Variance of the entropy Variance of the entropy value, since the value of the variance provides the variations in the entropy value. [58] Web page requested In the case of an application level DDoS attack, the attack packets are in the form of web page requests.…”

Section: Http Get Request Countmentioning

confidence: 99%

New Features of User’s Behavior to Distributed Denial of Service Attacks Detection in Application Layer

Bravo

Mauricio

2018

Int. J. Onl. Eng.

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Distributed Denial of Service (DDoS) attacks are a threat to the security of red. In recent years, these attacks have been directed especially towards the application layer. This phenomenon is mainly due to the large number of existing tools for the generation of this type of attack. The highest detection rate achieved by a method in the application capacity is 98.5%. Therefore, the problem of detecting DDoS attacks persists. In this work an alternative of detection based on the dynamism of the web user is proposed. To do this, evaluate the user's characteristics, mouse functions and right click. For the evaluation, a data set of 11055 requests was used, from which the characteristics were extracted and entered into a classification algorithm. To that end, it can be applied once in Java for the classification of real users and DDoS attacks. The results showed that the evaluated characteristics achieved an efficiency of 100%. Therefore, it is concluded that these characteristics show the dynamism of the user and can be used in a detection method of DDoS attacks.

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Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers

Cited by 9 publications

References 20 publications

DDOS Detection on Internet of Things Using Unsupervised Algorithms

DDOS Detection on Internet of Things Using Unsupervised Algorithms

DDoS Detection on Internet of Things using Unsupervised Algorithms

New Features of User’s Behavior to Distributed Denial of Service Attacks Detection in Application Layer

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