A survey on cybersecurity attacks and defenses for unmanned aerial systems

Wang, Zhaoxuan; Li, Yang; Wu, Shihao; Zhou, Yuan; Yang, Ling; Xu, Yuan; Zhang, Tianwei

doi:10.1016/j.sysarc.2023.102870

Cited by 17 publications

(3 citation statements)

References 68 publications

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“…Unauthorized entry, eavesdropping, data alteration, and denial-of-service assaults are among the dangers that might negatively affect the availability, confidentiality, and integrity of UAV communication channels. Strong security measures are thus urgently required to safeguard UAV communication infrastructure and guarantee the continued and secure execution of UAV operations [3] and [4]. In [5], we demonstrated that the detection and mitigation of security risks inside networks is greatly aided by the use of Intrusion Detection System (IDS), and we also demonstrated that our hybrid machine learning technique uses random forest and support vector machine classification to reach a positive detection rate with a small false alarm rate.…”

Section: Introductionmentioning

confidence: 85%

“…As UAVs are used increasingly often in crucial missions, it is crucial to protect the confidentiality and integrity of their communication systems [1]. UAVs extensively rely on wireless communication networks to send private information like video feeds, sensor readings, and command and control information [2] and [3]. These lines of communication are nonetheless vulnerable to a number of security risks, such as denial-of-service attacks, unauthorized access, and data manipulation [4].…”

Section: Related Workmentioning

confidence: 99%

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Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

M. Abdulghani,

L. Walters

et al. 2022

Journal of Cyber Security

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Unmanned Aerial Vehicles (UAVs) proliferate quickly and play a significant part in crucial tasks, so it is important to protect the security and integrity of UAV communication channels. Intrusion Detection Systems (IDSs) are required to protect the UAV communication infrastructure from unauthorized access and harmful actions. In this paper, we examine a new approach for enhancing intrusion detection in UAV communication channels by utilizing the Long Short-Term Memory network (LSTM) combined with the Synthetic Minority Oversampling Technique (SMOTE) algorithm, and this integration is the binary classification method (LSTM-SMOTE). We successfully achieved 99.83% detection accuracy by using the proposed approach and the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset 2017 (CICIDS2017) dataset. We demonstrated the efficiency of LSTM-SMOTE in defending UAV communication channels against possible attacks and bolstering the overall security posture through the use of a realworld scenario.

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Section: Introductionmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Improving Intrusion Detection in UAV Communication Using an LSTM-SMOTE Classification Method

M. Abdulghani,

L. Walters

et al. 2022

Journal of Cyber Security

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“…Attackers primarily hack data on digital devices using insecure networks to receive and send data. It is characterized by the fact that it doesn't show abnormalities in transmission operations hence detection is quite difficult [69]. Attackers using this method aim to access private and sensitive information such as credit card numbers and passwords passed across networks.…”

Section: Eavesdroppingmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

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