Detecting DDoS Attacks Using the Analysis of Network Traffic as Dynamical System

Krasnov, Andrey E.; Никольский, Д. Н.; Repin, D.S.; Galyaev, V. S.; Zykova, Evgenia

doi:10.1109/monetec.2018.8572034

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…This makes it difficult for an attack to be detected using this approach. Several other studies [49][50][51][52][53][54] have focused on the use of network traffic patterns to identify anomalies, since attack traffic will have a different pattern from that of legitimate ones. Despite the good precision recorded by this method, as reported in [50][51][52], the approach suffers from inaccurate flow feature selection.…”

Section: Key Findings and Discussionmentioning

confidence: 99%

“…Thus, by comparing the geometries of input traffic patterns and the geometries of output traffic patterns seen at a network branch point, similar to a router, the source of the attack can be determined. In [54], an algorithm for detecting attacks via network traffic pattern assessment was introduced. A simple Hilbert operator was used to describe the traffic pattern over various time intervals.…”

Section: Traffic Pattern Analysismentioning

confidence: 99%

“…Traffic pattern analysis [49][50][51][52][53][54] Compares the traffic patterns of infected hosts to the benign hosts…”

Section: Features Advantages Limitationsmentioning

confidence: 99%

See 2 more Smart Citations

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

Adedeji

Abu‐Mahfouz

Kurien

2023

JSAN

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In recent times, distributed denial of service (DDoS) has been one of the most prevalent security threats in internet-enabled networks, with many internet of things (IoT) devices having been exploited to carry out attacks. Due to their inherent security flaws, the attacks seek to deplete the resources of the target network by flooding it with numerous spoofed requests from a distributed system. Research studies have demonstrated that a DDoS attack has a considerable impact on the target network resources and can result in an extended operational outage if not detected. The detection of DDoS attacks has been approached using a variety of methods. In this paper, a comprehensive survey of the methods used for DDoS attack detection on selected internet-enabled networks is presented. This survey aimed to provide a concise introductory reference for early researchers in the development and application of attack detection methodologies in IoT-based applications. Unlike other studies, a wide variety of methods, ranging from the traditional methods to machine and deep learning methods, were covered. These methods were classified based on their nature of operation, investigated as to their strengths and weaknesses, and then examined via several research studies which made use of each approach. In addition, attack scenarios and detection studies in emerging networks such as the internet of drones, routing protocol based IoT, and named data networking were also covered. Furthermore, technical challenges in each research study were identified. Finally, some remarks for enhancing the research studies were provided, and potential directions for future research were highlighted.

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Section: Key Findings and Discussionmentioning

confidence: 99%

Section: Traffic Pattern Analysismentioning

confidence: 99%

See 1 more Smart Citation

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

Adedeji

Abu‐Mahfouz

Kurien

2023

JSAN

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Statistical Distributions of Partial Correlators of Network Traffic Aggregated Packets for Distinguishing DDoS Attacks

Krasnov

Никольский

2019

Distributed Computer and Communication Networks

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Research of multiclass fuzzy classification of traffic for attacks identification in the networks

Parfenov

Zabrodina

Zhigalov

et al. 2020

J. Phys.: Conf. Ser.

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Currently, data mining methods such as neural networks, decision trees, genetic algorithms, restricted search algorithms, evolutionary programming, reasoning systems based on similar cases, rule induction, analysis with selective action, logical regression, algorithms for determining associations and sequences, data visualization, combined methods are actively used in various specialized areas. The introduction of analytical methods of data mining is primarily aimed at adapting existing solutions to solve new problems related to the informatization of business processes. One of the actively developing areas that use data mining and artificial intelligence methods is network security. To identify and detect anomalies in networks, it is most effective to create profiles of data flow behavior depending on current conditions. In this work, we developed a method that allows us to identify Exploits, Fuzzers, and Generic attacks based on multiclass fuzzy classification. In the experimental part of the study, it was found that the proposed solution is comparable in accuracy to the Naïve Bayes, SVM, and KNN methods, but it has higher performance and less resource consumption for large data flows. This is quite effective for networks with many devices.

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Detecting DDoS Attacks Using the Analysis of Network Traffic as Dynamical System

Cited by 5 publications

References 9 publications

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

Statistical Distributions of Partial Correlators of Network Traffic Aggregated Packets for Distinguishing DDoS Attacks

Research of multiclass fuzzy classification of traffic for attacks identification in the networks

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