A Survey on 6G Enabled Light Weight Authentication Protocol for UAVs, Security, Open Research Issues and Future Directions

Khan, Adnan Shahid; Sattar, Muhammad Ali; Nisar, Kashif; Ibrahim, Ag Asri Ag; Annuar, Noralifah Binti; Abdullah, Johari; Memon, Shuaib K.

doi:10.3390/app13010277

Cited by 13 publications

(7 citation statements)

References 117 publications

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“…Despite its advantages over traditional networks, such as centralized monitoring and granular management, SDN is less secure due to its increased vulnerability to attacks and the introduction of new security concerns. 28 SDN can be compromised in a variety of ways, such as by spoofing, 29 tampering, repudiation, information leakage, and DoS. [30][31][32] Attacks using DoS could also be launched against the SDN.…”

Section: Problem Statementmentioning

confidence: 99%

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Nisa,

Khan,

Ahmad

et al. 2024

Int J Network Mgmt

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Software‐defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial‐of‐service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two‐Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K‐nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two‐Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.

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Section: Problem Statementmentioning

confidence: 99%

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Nisa,

Khan,

Ahmad

et al. 2024

Int J Network Mgmt

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“…According to [40], 6G-based intelligent cybersecurity will lead to new techniques; some of these are given below. Finally, several studies have focused on detection performance in mobile environments [2], which is important for enhancing cybersecurity, encrypting medical images against various threats when transmitting data via wireless broadcasting [41], and using deep-learning algorithms in segmentation tasks with various kinds of networks [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Algarni

Thayananthan

2023

IEEE Access

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Sixth-generation (6G)-based communications have many applications and are emerging as a new system to utilize existing vehicles and communication devices in autonomous vehicles (AVs). Electric vehicles and AVs not supporting the integration of intelligent cybersecurity will become vulnerable, and their internal functions, features, and devices providing services will be damaged. This paper presents an intelligent cybersecurity model integrating intelligent features according to the emerging 6G-based technology based on evolving cyberattacks. The model's novel design was developed using the necessary algorithms to provide quick and proactive decisions with intelligent cybersecurity based on 6G (IC6G) policies when AVs face cyberattacks. In this model, network security algorithms incorporating intelligent techniques are developed using applied cryptography. Money transaction handling services implemented in an AV are considered an example to determine the security and intelligence level depending on the IC6G policies. Intelligence, complexity, and energy efficiency (EE) are assessed. Finally, we conclude that the model results are effective for intelligently detecting and preventing cyberattacks on AVs.

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“…Sriram et al 25 proposed a DL‐based IoT botnet attack detection framework based on network traffic flows. The proposed solution captures the network flows, transforms them into connection records, and uses a DL‐based solution to detect anomalies from compromised IoT devices.…”

Section: Related Workmentioning

confidence: 99%

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

Ahmad

Khan

Zen

et al. 2023

Trans Emerging Tel Tech

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The innovative computing idea of Internet‐of‐Things (IoT) architecture has gained tremendous popularity over the last decade, resulting in an exponential increase in the connected devices and the data processed in the IoT networks. Since IoT devices collect a massive amount of sensitive information exchanged over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network‐based anomaly detection system can provide the much‐needed efficient security solution to the IoT network by detecting anomalies at the network entry points through constant traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false‐negative rate in classifying network traffic. To this end, this paper proposes an efficient Multistage Spectrogram image‐based network Anomaly Detection System (MS‐ADS) using a deep convolution neural network that utilizes a short‐time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re‐training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%.

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A Survey on 6G Enabled Light Weight Authentication Protocol for UAVs, Security, Open Research Issues and Future Directions

Cited by 13 publications

References 117 publications

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

TPAAD: Two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

MS‐ADS: Multistage Spectrogram image‐based Anomaly Detection System for IoT security

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