A SYN Flood Attack Detection Method Based on Hierarchical Multihead Self-Attention Mechanism

Guo, Xiaojun; Gao, Xuan

doi:10.1155/2022/8515836

Cited by 5 publications

(2 citation statements)

References 35 publications

(42 reference statements)

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“…The study did not conduct a thorough analysis of the MSA unit as it has already been extensively studied in the current literature (e.g., Zhou et al, 2022 ). The study conducted by Guo and Gao ( 2022 ) employed a unit comprised of H ′ heads to evaluate the similarity between a query and its corresponding keys, taking into account the allocated weight for each value. In addition, the layer normalization module is utilized to compute the mean and variance necessary for normalizing the inputs to the neurones within a layer during a single training instance (Ba et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Multi-label classification of retinal disease via a novel vision transformer model

Wang,

Lian,

Jiao

2024

Front. Neurosci.

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IntroductionThe precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously.MethodsThis study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function.Results and discussionThe experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG.

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Section: Methodsmentioning

confidence: 99%

Multi-label classification of retinal disease via a novel vision transformer model

Wang,

Lian,

Jiao

2024

Front. Neurosci.

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“…Three-way handshaking establishes connection reliability in TCP communication approach between client and server [10] that used three types of packets. Once the sender needs to create a connection with the receiver, syn request will be transmitted to the receiver side.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Wireshark Tool for Detecting Attacks and Vulnerabilities in Network Traffic

Mabsali¹,

Jassim²,

Mani³

2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Due to the popularity of using the technology, network security plays a crucial role recently which supports to establish strong systems that work against cyberattacks. Furthermore, the term "network vulnerabilities" refers to the flaws in the network which attackers exploit to break security and steal critical data. To discover the weaknesses of the network, the attackers use the mechanism of open port scanning to reach the systems and data, therefore the administrator should configure the network correctly and close any open ports. Monitoring network traffic is very important, so the developer focus to design analyzing tools that employ to inspect transmitted packets over the network to trace anomalous activities. Wireshark is one of the most well-known packets analyzing tool that is used to monitor the packets as well as used for examining the protocols. Moreover, the type of attack can be determined from the statistic report which generated by Wireshark tool. For instance, if the attacker sends syn packets to a target device, Wireshark will show the detail of syn packets. Practically, when tcp syn requests are flooded to any device, there will be a huge impact on the device's resources like consuming the bandwidth which affects system performance at the end. This paper presents a penetration test to lunch syn flood attack by sending a huge number of syn packets from Kali Linux machine to three targeted machines which are Windows 8.1, Windows 10 and Metasploitable. The test includes three scenarios, the first one focus on flooding syn packets by using the real source ip address of the attacker machine while the second scenario relays on sending syn packets by utilizing a spoof source ip address. The final scenario depends on using a random source ip address to flood syn packets. The Wireshark tool will be run in Kali machine to capture the packets and generate detailed reports. The results of captured data will be recorded to make analysis and list the capabilities of this tool.

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Impact Analysis to Detect and Mitigate Distributed Denial of Service Attacks with Ryu-SDN Controller: A Comparative Analysis of Four Different Machine Learning Classification Algorithms

Gocher,

Taterh,

Dadheech

2023

SN COMPUT. SCI.

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A SYN Flood Attack Detection Method Based on Hierarchical Multihead Self-Attention Mechanism

Cited by 5 publications

References 35 publications

Multi-label classification of retinal disease via a novel vision transformer model

Multi-label classification of retinal disease via a novel vision transformer model

Effectiveness of Wireshark Tool for Detecting Attacks and Vulnerabilities in Network Traffic

Impact Analysis to Detect and Mitigate Distributed Denial of Service Attacks with Ryu-SDN Controller: A Comparative Analysis of Four Different Machine Learning Classification Algorithms

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