Research on DDoS Attacks Detection Based on RDF-SVM

Wang, Chenguang; Zheng, Jing; Li, Xiaoyong

doi:10.1109/icicta.2017.43

Cited by 31 publications

(10 citation statements)

References 3 publications

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“…In a Client and server model various cryptographic technique are used to impart security and manage confidentiality, integrity, nonrepudiation and authentication [16].Here we have simulated a communication between nodes in a simulator. We have set up in total of six nodes of which node 3 and 6 are receiver nodes and node 2, 4 and 5 are used to transmit data.…”

Section: Offline Simulation and Results And Securitymentioning

confidence: 99%

Interconnected Medical Devices and their Security Aspects

Panwar¹

2019

IJRASET

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With the rapid growth of interconnected technologies in the recent time, the world is going under a huge change. Medical and healthcare sectors are also influenced by the advancement in connected technologies. Internet of Things (IoT) in medical device has played a key role in increasing the facilities which can be provided by the healthcare sector. As the healthcare sector is getting digitalized and millions of interconnected devices would communicate important information, it increases the risk factor and new privacy and security issues would arise. This paper focuses on how secure communication between the devices can be achieved and how a weak link in the chain can turn into disaster. It would also look on the research directions which can be done to strengthen the system.

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Section: Offline Simulation and Results And Securitymentioning

confidence: 99%

Interconnected Medical Devices and their Security Aspects

Panwar¹

2019

IJRASET

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“…The successful method of intrusion detection will reduce the system's false positive rate and increase the accuracy of the classification. Therefore, several SVM-based methods of intrusion detection have been proposed [78][79][80][81][82][83][84].…”

Section: Applications Of Support Vector Machine (Svm)mentioning

confidence: 99%

“…How to successfully track and prevent network interference has been a critical problem in the field of data security. Therefore, many experiments have been carried out to accomplish this goal utilizing machine learning techniques [78][79][80][81][82][83][84]. If the successful intrusion detection device is able to reduce the false-positive rate of the system and increase the classification accuracy.…”

Section: E Literature-based Intrusion Detection Techniquesmentioning

confidence: 99%

Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

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“…Various ML technologies have been employed, mainly as classifiers, in the detection of DDoS attacks. There are Support Vector Machine (SVM) [4], k-Nearest Neighbors (KNN) [5], Naïve Bayes Classifier [6], Random Forest (RF) [7], Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [8], and Artificial Neural Network (ANN) [17], to name a few. With SVM, based on labeled training data, a hyperplane is constructed in the transform domain to classify unseen data.…”

Section: And DL For Ddos Detectionmentioning

confidence: 99%

Detection of Adversarial DDoS Attacks Using Generative Adversarial Networks with Dual Discriminators

Shieh¹,

Lin²,

Nguyen³

et al. 2021

World Congress on Electrical Engineering and Computer Systems and Science

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DDoS (Distributed Denial of Service) has become a pressing and challenging threat to the security and integrity of computer networks and information systems. The detection of DDoS attacks is essential before any mitigation approaches can be taken. AI (Artificial Intelligence) and ML (Machine Learning) have been applied to the detection of DDoS attacks with satisfactory achievement. However, new types of attacks emerge as the technology for DDoS attacks keep evolving. This study investigates the impact of a new sort of DDoS attackadversarial DDoS attack. We synthesize attacking traffic using Wasserstein Generative Adversarial Networks with Gradient Penalty (GP-WGAN). Experiment results reveal that the synthesized traffic can penetrate the systems, including Random Forest, k-Nearest Neighbor, and Multi-Layer Perceptron, without being detected. This observation is an alarming and pessimistic wake-up call implying the urgent need for countermeasures to adversarial DDoS attacks. To this problem, we propose an adversarial GAN Intrusion Detection System (AG-IDS) featuring dual discriminators. The additional discriminator is designed to capture adversarial DDoS traffic. As indicated in the experimental results, the proposed AG-IDS can be an effective solution to adversarial DDoS attacks.

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Research on DDoS Attacks Detection Based on RDF-SVM

Cited by 31 publications

References 3 publications

Interconnected Medical Devices and their Security Aspects

Interconnected Medical Devices and their Security Aspects

Machine Learning Applications based on SVM Classification A Review

Detection of Adversarial DDoS Attacks Using Generative Adversarial Networks with Dual Discriminators

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