Smart Detection-IoT: A DDoS Sensor System for Internet of Things

Silveira, Frederico Augusto Fernandes; Lima-Filho, Francisco Pinheiro; Silva, Felipe Sampaio Dantas; Brito, Agostinho de Medeiros; Silveira, Luiz F. Q.

doi:10.1109/iwssip48289.2020.9145265

Cited by 15 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the proposed model lacks support for feature reduction. In this context, Silveira et al [33] evaluated the effectiveness of their proposed IDS in identifying LR DoS attacks in an SD-IoT system using a freely accessible CIC DoS 2017 dataset [34]. Unfortunately, this dataset is not ideal choice to employ because it has no relation to IoT network traffic features.…”

Section: Related Workmentioning

confidence: 99%

“…The authors selected a group of 28 traffic flow features to train and test the classifier effectiveness. The last three LR DoS detection techniques [32], [33], and [35] presented above are signature-based and may not accurately identify new attacks. In another attempt to provide a solution for identifying LR DoS attacks in SD-IoT, the authors of [36] introduced a FeedForward-Convolutional Neural Network (FFCNN), an AI-based anomaly detection method.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

2023

View full text Add to dashboard Cite

The absence of standards and the diverse nature of the Internet of Things (IoT) have made security and privacy concerns more acute. Attacks such as distributed denial of service (DDoS) are becoming increasingly widespread in IoT, and the need for ways to stop them is growing. The use of newly formed Software-Defined Networking (SDN) significantly lowers the computational burden on IoT network nodes and makes it possible to perform more security measurements. This paper proposes an SDNbased, four-module DDoS attack detection and mitigation framework for IoT networks called FMDADM. The proposed FMDADM framework comprises four main modules and five-tier architecture. The first module implements an early detection process based on the average drop rate (ADR) principle using a 32packet window size. The second module uses a novel double-check mapping function (DCMF), that aids in earlier attack detection at the data plane level. The third module is an ML-based detection application comprising four phases: data preprocessing, feature extraction, training and testing, and classification. This module detects DDoS attacks using only seven features: two selected and five newly computed features. The last module introduces an attack mitigation process. We applied the proposed framework to three test cases: one single-node attack test case and two multi-node attack test cases, all with real IoT traffic generated and deployed in Mininet-IoT. The proposed FMDADM framework efficiently detects DDoS attacks at high and low rates, can discriminate between attack traffic and flash crowds, and protects both local and remote IoT nodes by preventing infection from propagating to the ISP level. The FMDADM outperformed most existing cutting-edge approaches across ten different evaluation criteria. According to the experimental results, FMDADM achieved the following accuracy,

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

2023

View full text Add to dashboard Cite

show abstract

“…A smart DDoS detection system for IoT devices proposed in [12]. The system is designed by using SDN (Software Defined Network) and tested on three datasets including CICIDS2017, CIC-DoS, and a customized dataset which contains DDoS attacks.…”

Section: State-of-the-art Studies On Ddos Detection and Preventionmentioning

confidence: 99%

A Methodology to Detect Distributed Denial of Service Attacks

Aslan

2022

Bilişim Teknolojileri Dergisi

View full text Add to dashboard Cite

Distributed denial of service (DDoS) attacks is one of the most destructive cyber attacks which target the availability of the system when legitimate users try to access the system. Not only computers, but also the growing number of smartphones as well as Internet of Things (IoT) devices are affected by DDoS attacks. There is no well-known system which effectively stops or prevents DDoS attacks. Designing an effective DDoS detector with high accuracy with low computational overhead is still a very challenging task. In this paper, a methodology, which is used to detect and classify the types of DDoS attacks, is proposed. Our methodology is divided into three parts: pre-processing, feature selection, and classification. First, pre-processing is performed to eliminate some features which are not suitable for our model. Second, most significant features are selected by using Information Gain, Gain Ratio, Correlation Coefficient, and Relief. We declined the number of features from 87 to 20. Finally, various classifiers are used to detect DDoS attacks from the bening ones. The proposed methodology is performed on the CIC-DDoS2019 dataset. The experimental results show that the proposed methodology performed pretty well when it is compared to leading methods in the literature.

show abstract

“…Any flow whose features are different from the model of benign flow will be considered malicious. In this regard, Silveira et al (2020) have used the publicly available CIC DoS 2017 dataset to assess the performance of their proposed IDS in detecting LR DoS attacks in an IoT-SDN environment. Similarly, Tang et al (2020) have proposed a signature-based LR DoS detection algorithm using Adaboost.…”

Section: Introductionmentioning

confidence: 99%

A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT

Ilango

2022

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Smart Detection-IoT: A DDoS Sensor System for Internet of Things

Cited by 15 publications

References 14 publications

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

A Methodology to Detect Distributed Denial of Service Attacks

A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT

Contact Info

Product

Resources

About