Deep Learning based Anomaly Detection for a Vehicle in Swarm Drone System

Ahn, Hyojung

doi:10.1109/icuas48674.2020.9213880

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…In this study, the swarm system employed a real-time Kinematic Velocity (KV) GPS-based precision navigation method as described by [44]. The dataset used as learning data for recognizing chaotic behavior during swarm mission execution was sourced from a series of swarm drone flight tests conducted by the Korean Aerospace Research Institute, in which up to 30 quadcopter drones were deployed [41].…”

Section: Methodsmentioning

confidence: 99%

“…A K-nearest neighbor (KNN) methodology was introduced in [40] to identify the reasons and factors for drone failures and potential deteriorations in drone performance on the ground in order to assess the causes of failure and potential deterioration in drone performance during flight. An actual flight dataset was used in [41] to validate the developed Anomaly Detection (AD) model, which shows if there is any abnormality in the swarm drone flight. For the generation model, the AD model created a training model using a Deep Neural Network.…”

Section: Related Workmentioning

confidence: 99%

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Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

NEBE,

SANNI,

ADETONA

et al. 2022

IJACSA

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Most existing identification and tackling of chaos in swarm drone missions focus on single drone scenarios. There is a need to assess the status of a system with multiple drones, hence, this research presents an on-the-fly chaotic behavior detection model for large numbers of flying drones using machine learning techniques. A succession of three Artificial Intelligence knowledge discovery procedures, Logistic Regression (LR), Convolutional Neural Network (CNN), Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) were employed to reduce the dimension of the actual data of the swarm of drone's flight and classify it as non-chaotic and chaotic. A onedimensional, multi-layer perceptive, deep neural network-based classification system was also used to collect the related characteristics and distinguish between chaotic and non-chaotic conditions. The Rössler system was then employed to deal with such chaotic conditions. Validation of the proposed chaotic detection and mitigation technique was performed using realworld flight test data, demonstrating its viability for real-time implementation. The results demonstrated that swarm mobility horizon-based monitoring is a viable solution for real-time monitoring of a system's chaos with a significantly reduced commotion effect. The proposed technique has been tested to improve the performance of fully autonomous drone swarm flights.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

NEBE,

SANNI,

ADETONA

et al. 2022

IJACSA

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“…UAV swarms' security-related issues and anomaly detection approaches are explored and suggested in [6,7].…”

Section: Related Workmentioning

confidence: 99%

Tamper-Proof Hardware Solution and Algorithms for UAV Swarms Operating in Hostile Environments

Poghosyan,

Alaverdyan

2023

Proceedings of Computer Science and Information Technologies 2023 Conference

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The paper presents a zero-trust approach to ensuring cybersecurity in self-organized UAV swarms operating in hostile or dangerous environments. Depending on task performance and safety considerations, mission-oriented swarms of UAVs may normally change the planned flight routes, update session keys, automatically destroy data when being corrupted, or detect hostile outsiders, which is a significant security factor. The proposed algorithms will make it possible to assert and verify the membership of UAVs within the swarm during the flight. Identification and strong authentication of individual UAVs are based on secure multi-party computations.

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“…Ref. [67] proposed a deep-learning-based method for detecting and identifying drones. Particular attention was paid to the identification and detection of drone acoustic fingerprints.…”

Section: Machine Learning Techniques Used In the Drone Forensics Fieldmentioning

confidence: 99%

“…The researchers in [67] offered a way to spot anomalies in a swarming flight with numerous flying drones, where the adversary might purposefully influence some drones to sabotage. Flight data from several streams were examined in order to discover these irregularities.…”

Section: Machine Learning Techniques Used In the Drone Forensics Fieldmentioning

confidence: 99%

A Comprehensive Collection and Analysis Model for the Drone Forensics Field

Alotaibi

Al-Dhaqm

Al‐Otaibi

et al. 2022

Sensors

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Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain.

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Deep Learning based Anomaly Detection for a Vehicle in Swarm Drone System

Cited by 14 publications

References 9 publications

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

Tamper-Proof Hardware Solution and Algorithms for UAV Swarms Operating in Hostile Environments

A Comprehensive Collection and Analysis Model for the Drone Forensics Field

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