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

Singh, Khundrakpam Johnson; Thongam, Khelchandra; De, Tanmay

doi:10.3390/e18100350

Cited by 49 publications

(11 citation statements)

References 28 publications

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“…On the other hand, information theory-based metrics play a significant role in the detection of DoS attacks due to their low computation overhead [15]. During a DoS attack, entropy values decrease significantly [16].…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent approach for detecting DoS flooding attacks in software-defined networks

Latah

Toker

2018

Int. J. Adv. Intell. Informatics

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Software-Defined Networking (SDN) is an emerging networking paradigm that provides an advanced programming capability and moves the control functionality to a centralized controller. This paper proposes a two-stage novel intelligent approach that takes advantage of the SDN approach to detect Denial of Service (DoS) flooding attacks based on calculation of packet rate as the first step and followed by Support Vector Machine (SVM) classification as the second step. Flow concept is an essential idea in OpenFlow protocol, which represents a common interface between an SDN switch and an SDN controller. Therefore, our system calculates the packet rate of each flow based on flow statistics obtained by SDN controller. Once the packet rate exceeds a predefined threshold, the system will activate the packet inspection unit, which, in turn, will use the (SVM) algorithm to classify the previously collected packets. The experimental results showed that our system was able to detect DoS flooding attacks with 96.25% accuracy and 0.26% false alarm rate.

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Section: Introductionmentioning

confidence: 99%

A novel intelligent approach for detecting DoS flooding attacks in software-defined networks

Latah

Toker

2018

Int. J. Adv. Intell. Informatics

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“…Most of the analytical models used to minimize the error function use methods that require the evaluation of the local gradient of the E ( W ) function and techniques based on second-order derivatives can also be considered [ 23 , 24 ].…”

Section: Neural Network Methodsmentioning

confidence: 99%

“…Given the binning function described above, the key idea is to build the DT using the Kronecker product, assuming we have an input instance x ∈ R D with D characteristics. Associating each characteristic x d with its own NN f d (x d ), we can determine all the final nodes of the DT, in line with Equation (23).…”

Section: Deep Neural Decision Trees (Dndt)mentioning

confidence: 99%

Neural Networks for Estimating Speculative Attacks Models

Alaminos

Aguilar-Vijande

Sánchez-Serrano

2021

Entropy

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Currency crises have been analyzed and modeled over the last few decades. These currency crises develop mainly due to a balance of payments crisis, and in many cases, these crises lead to speculative attacks against the price of the currency. Despite the popularity of these models, they are currently shown as models with low estimation precision. In the present study, estimates are made with first- and second-generation speculative attack models using neural network methods. The results conclude that the Quantum-Inspired Neural Network and Deep Neural Decision Trees methodologies are shown to be the most accurate, with results around 90% accuracy. These results exceed the estimates made with Ordinary Least Squares, the usual estimation method for speculative attack models. In addition, the time required for the estimation is less for neural network methods than for Ordinary Least Squares. These results can be of great importance for public and financial institutions when anticipating speculative pressures on currencies that are in price crisis in the markets.

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“…The other study [16] proposed a distributed denial-of-service (DDoS) attack detection mechanism on the big data situation. The studies [17,18] also proposed the entropy-based DDoS attack measurement on packet size interval and the method to locate the potential DDoS attack by the SDN network. Most of the studies mentioned above also tried to detect the cyber-attacks on IoT.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

Soe

Feng

Santosa

et al. 2020

Sensors

133

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With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

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

Cited by 49 publications

References 28 publications

A novel intelligent approach for detecting DoS flooding attacks in software-defined networks

A novel intelligent approach for detecting DoS flooding attacks in software-defined networks

Neural Networks for Estimating Speculative Attacks Models

Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture

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