XGBoost Classifier for DDOS Attack Detection in Software Defined Network Using sFlow Protocol

Rozam, Nadhir Fachrul; Riasetiawan, Mardhani

doi:10.18517/ijaseit.13.2.17810

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Network training process: The system configuration during network training includes input data from the roll, pitch, and height sensors of the suspended floor collected at the PLC [33]. These signals are transmitted to the PC/Matlab computer via the OPC tool module.…”

Section: B Building a Neural Network Using Matlab/nntoolmentioning

confidence: 99%

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Khanh

Thao

2023

JOIV : Int. J. Inform. Visualization

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Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.

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Section: B Building a Neural Network Using Matlab/nntoolmentioning

confidence: 99%

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Khanh

Thao

2023

JOIV : Int. J. Inform. Visualization

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Detection of distributed denial of service attack using enhanced adaptive deep dilated ensemble with hybrid meta‐heuristic approach

Aliar,

Gowri,

Abins

2024

Trans Emerging Tel Tech

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The biggest firms throughout the world now are the ones that offer services in the cloud. One of the top problems for cloud users (CUs) and cloud service providers (CSPs) is the availability of cloud‐based services whenever needed. A distributed denial of service (DDoS) assault has been a significant threat to the security of the system in recent years. DDoS defense and detection of these attacks have become hot topics of research for academia and business. However, most approaches cannot accomplish efficient detection outcomes with few false alarms. As a result, minimizing the consequences of DDoS attacks allows CSPs to offer CUs high‐quality services. In the cloud sector, a collective deep structured algorithm is suggested to identify DDoS attacks successfully. The recommended method contains several stages: data acquisition, pre‐processing, optimal feature detection, and selection. The first step is the acquisition of data using the help of publicly available sources. Further, the input data undergoes preprocessing. Consequently, the optimal weighted feature selection takes place on the preprocessed data, where the optimization is done with the aid of the Hybrid Border Collie and Dragonfly Algorithm (HBCDA). Finally, DDoS attack detection is achieved via a novel method known as adaptive deep dilated ensemble (ADDE), which includes, one‐dimensional convolutional neural network (1DCNN), deep temporal convolutional neural network (DTCNN), recurrent neural network (RNN), and bidirectional long short‐term memory (Bi‐LSTM). For the attainment of optimal results, the parameter tuning is accomplished by using the HBCDA approach. The detection outcome is computed by using the fuzzy ranking mechanism. The validation is done for the suggested method model, and its corresponding findings are validated with conventional techniques. Hence, the suggested approach outperforms the detection performance and ensures more efficiency than traditional approaches.

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An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

Ige,

Kiekintveld,

Piplai

2024

2024 IEEE International Conference on Electro Information Technology (eIT)

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XGBoost Classifier for DDOS Attack Detection in Software Defined Network Using sFlow Protocol

Cited by 4 publications

References 27 publications

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Detection of distributed denial of service attack using enhanced adaptive deep dilated ensemble with hybrid meta‐heuristic approach

An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

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