Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic

Alipour-Fanid, Amir; Dabaghchian, Monireh; Wang, Ning; Wang, Pu; Zhao, Liang; Zeng, Kai

doi:10.1109/tifs.2019.2959899

Cited by 73 publications

(46 citation statements)

References 38 publications

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“…Therefore, an efficient and intelligent NIDS is required which can detect the intruders within UAV‐enabled networks. Furthermore, the use of AI within the NIDS for UAV‐enabled networks can be an interesting research direction, which requires more exploration and investigation 141,142 …”

Section: Observations Challenges and Future Trendsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

643

215

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The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

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Section: Observations Challenges and Future Trendsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

643

215

View full text Add to dashboard Cite

show abstract

“…Alipour-Fanid et al., in Reference [ 27 ], presented a UAV detection and operating mode recognition ML approach with minimum delay over the encrypted WiFi UAV traffic. This approach derived the main features from the packet size and the inter-arrival time and then adopted a weighted one norm regularization in the training phase taking into account the measurement time of different features.…”

Section: Related Workmentioning

confidence: 99%

RF-Based UAV Detection and Identification Using Hierarchical Learning Approach

Nemer

Sheltami

Ahmad

et al. 2021

Sensors

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Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.

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“…have been utilized to detect UAVs [27]. However, when a UAV flies in an autonomous mode without Wi-Fi communication, such a technique will not work.…”

Section: Recently Wi-fi Communication Signals Between Uavs and Remotmentioning

confidence: 99%

A three-step classification framework to handle complex data distribution for radar UAV detection

Ren

Jiang

2021

Pattern Recognition

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Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches.

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Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic

Cited by 73 publications

References 38 publications

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

RF-Based UAV Detection and Identification Using Hierarchical Learning Approach

A three-step classification framework to handle complex data distribution for radar UAV detection

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