A neural network model for detecting DDoS attacks using darknet traffic features

Ali, Siti Hajar Aminah; Ozawa, Seiichi; Ban, Tao; Nakazato, Junji; Shimamura, Jumpei

doi:10.1109/ijcnn.2016.7727577

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Detection of DDoS attacks by utilizing ANN methods other than based on DARPA and KDDCUP99 datasets, which uses darknet packet flow data has been carried out by research [9]. In the study [10], UDP/53 and TCP/80/8080 packet flow used as ANN training inputs that have been previously extracted by the Local Sensitive Hashing (LSH) method. As compared to research [10], which only detects UDP/53 and TC /80/8080 packet data flow, the study [11] includes ICMP packet flow as ANN training input to detect illegal flows in the network.…”

Section: Introductionmentioning

confidence: 99%

“…In the study [10], UDP/53 and TCP/80/8080 packet flow used as ANN training inputs that have been previously extracted by the Local Sensitive Hashing (LSH) method. As compared to research [10], which only detects UDP/53 and TC /80/8080 packet data flow, the study [11] includes ICMP packet flow as ANN training input to detect illegal flows in the network. ANN in research [11] trained by utilizing the backpropagation function.…”

Section: Introductionmentioning

confidence: 99%

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Multischeme feedforward artificial neural network architecture for DDoS attack detection

Muhammad¹,

Foozy

Mohammed

2021

Bulletin EEI

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Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, Scaled-Conjugate and Resilient-Propagation training functions. Based on the studies conducted, it concluded that ANN Architecture sufficiently affected the Scaled-Conjugate and Resilient-Propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the Quasi-Newton training function with 6-(12)-2 neural network architecture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multischeme feedforward artificial neural network architecture for DDoS attack detection

Muhammad¹,

Foozy

Mohammed

2021

Bulletin EEI

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“…The number of space partitions/clusters does not change over time, and these algorithms adapt just the parameters of the membership functions and the local models. Category II is represented by the incremental algorithms, which are applied in this paper, with examples referred to as the widely applied algorithms RAN [12,13], SONFIN [14,15], NeuroFAST [16,17], DENFIS [18,19], SCFNN [20,21], eTS [22], [23], FLEXFIS [24,25], and PANFIS [26]. These algorithms implement just adding mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

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Abstract. This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Antilock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously

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“…DDoS packet stream with a large volume causes the target system cannot handle and end up with a loss of resources such as system shutdown, loss of data, moreover, the system loses the overall of owned services [2], [3]. Network packet classification is one of network defense system in order to avoid DDoS attacks [4].…”

Section: Introductionmentioning

confidence: 99%

“…ANN used in [6] with ResilientBackpropagation function combined with the ensemble of classifier outputs method and Neyman-Pearson cost minimization strategy for detection of DDoS attack based on DARPA and KDDCUP datasets. Research [7] adopted the ANN method to detect DDoS attacks based on darknet traffic. TCP/80 and UDP/53 packets used as input and optimized by Locally Sensitive Hashing methods.…”

Section: Introductionmentioning

confidence: 99%

Network Packet Classification using Neural Network based on Training Function and Hidden Layer Neuron Number Variation

Riadi¹,

Muhammad²,

Sunardi³

2017

ijacsa

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A neural network model for detecting DDoS attacks using darknet traffic features

Cited by 16 publications

References 8 publications

Multischeme feedforward artificial neural network architecture for DDoS attack detection

Multischeme feedforward artificial neural network architecture for DDoS attack detection

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Network Packet Classification using Neural Network based on Training Function and Hidden Layer Neuron Number Variation

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