RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things

Li, Jiabin; Liu, Ming; Zhi, Xue; Fan, Xiaochen; He, Xiangjian

doi:10.1109/access.2020.2974293

Cited by 65 publications

(28 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The huge shift in IoT networks has increase the amount of multidimensional data [30], [39]. Currently, ML techniques are performs specific functions.…”

Section: ) Manifoldness Using MLmentioning

confidence: 99%

Machine Learning Techniques for 5G and Beyond

et al. 2021

View full text Add to dashboard Cite

Wireless communication systems play a very crucial role in modern society for entertainment, business, commercial, health and safety applications. These systems keep evolving from one generation to next generation and currently we are seeing deployment of fifth generation (5G) wireless systems around the world. Academics and industries are already discussing beyond 5G wireless systems which will be sixth generation (6G) of the evolution. One of the main and key components of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and application layers, will involve one or another AI/ML techniques. This overview paper, presents an up-to-date review of future wireless system concepts such as 6G and role of ML techniques in these future wireless systems. In particular, we present a conceptual model for 6G and show the use and role of ML techniques in each layer of the model. We review some classical and contemporary ML techniques such as supervised and un-supervised learning, Reinforcement Learning (RL), Deep Learning (DL) and Federated Learning (FL) in the context of wireless communication systems. We conclude the paper with some future applications and research challenges in the area of ML and AI for 6G networks. INDEX TERMS Fifth generation (5G), sixth generation (6G), artificial intelligence (AI), machine learning (ML), deep learning (DL), reinforcement learning (RL), federated learning (FL).

show abstract

“…The huge shift in IoT networks has increase the amount of multidimensional data [30], [39]. Currently, ML techniques are performs specific functions.…”

Section: ) Manifoldness Using MLmentioning

confidence: 99%

Machine Learning Techniques for 5G and Beyond

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In our previous research [27], we worked on the 5-Tuple of each packet in traditional SDN, reform the 5-Tuple into sev-eral combinations, and then get the joint entropies. However, in this paper, we mainly work more on safety messages rather than the IP packets, because in the IoV environment, the IP packets are much slower than the safety messages.…”

Section: A Message Processormentioning

confidence: 99%

“…In [27], comparing other deviation check algorithms: Quartile Deviation [29,30], Generalized Extreme Studentized Deviate (GESD) [31], Linear Regression, Confidence Interval, etc., the Quartile Deviation is more sensitive and work well with continuous values. Quartile Deviation departs ascending sequence values into 4 equal-length sub-intervals and marks the 3 quartile points as Q1(25%), Q2(50%, also known as midpoint) and Q3(75%).…”

Section: Fast Quartile Deviation Check (Fqdc) 1) Deviation Check Algorithmsmentioning

confidence: 99%

“…Entropy is sensitive to the change of statistical distribution but requires great computations. We designed an optimized calculation algorithm in [27] to simplify the entropy calculation in DDoS detection in traditional software designed network, and further revise the algorithm suitable for IoV scenario, and the low response time alse meets the demand of fast edge detection [9][10][11]. We will do an entropy calculation whenever a new safety message is sent out, and can recognize the attack in real time with a newly-designed algorithm, FQDC.…”

Section: Introductionmentioning

confidence: 99%

“…The authors use two adjacent non-overlapping windows: the referenced sliding window and the test sliding window, and use an autoregressive model to recognize anomalous traffic. And in our previous research [27], we update this method to a sliding window element-by-element and calculate the entropy values without fully recalculation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

Zhi

Li³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

DDoS attack detection in SDN: Enhancing entropy‐based detection with machine learning

Santos‐Neto,

Bordim,

Alchieri

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummarySoftware defined network (SDN) has emerged as a new paradigm in terms of network architecture, providing flexibility, agility, and programmability to network management. These benefits boosted the SDN adoption, bringing new challenges mainly related to security, in particular, those related to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. The detection, prevention, and mitigation of these attacks are important since they can affect the entire network. Many current security measures use statistical techniques, as entropy, or machine learning (ML) algorithms to detect DoS and DDoS attacks. While the definition of a threshold to determine whether a traffic is an attack is not trivial in statistical techniques, ML solutions may provide better accuracy but require considerable computational resources and time to converge to a model able to detect these attacks. Trying to circumvent these limitations, current hybrid approaches either use the results from entropy as input in ML algorithms (EntropyML) or use entropy as a filter and ML algorithms to identify attacks. This work goes one step ahead and combines these techniques in a three‐step approach (EntropyMLEntropy), called ML‐Entropy, which inherits the intelligence of ML algorithms to adjust the threshold used by entropy. The proposed solution was implemented and evaluated in two datasets, the well‐known synthetic DARPA dataset and a dataset composed by traffic collected from a real‐corporate environment. Experimental results show that, in general, ML‐Entropy presents an accuracy above 99%, similar to support vector machine (SVC) and random forest (RF) algorithms, being able to converge to a detection model up to and faster than RF and SVC, respectively.

show abstract

RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things

Cited by 65 publications

References 25 publications

Machine Learning Techniques for 5G and Beyond

Machine Learning Techniques for 5G and Beyond

RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

DDoS attack detection in SDN: Enhancing entropy‐based detection with machine learning

Contact Info

Product

Resources

About