Detection of Denial of Service Attack Using Deep Learning and Genetic Algorithm

Saha, Sangeeta; Singh, Neema; Rudra, Bhawana

doi:10.1007/978-981-16-1740-9_1

Cited by 1 publication

(5 citation statements)

References 7 publications

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Section: Resultsmentioning

confidence: 99%

“…The developed ADHO-enabled DBN model is compared with various existing methods, such as RBM (Imamverdiyev and Abdullayeva, 2018), DL-enabled GA (Saha et al, 2022), DARLH (Veluchamy and Kathavarayan, 2021) and LSTM (Yeom et al, 2021). The comparative assessment using NSL-KDD under various percentages of learning sets is briefly discussed and represented in Figure 6.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Yadigar Imamverdiyev and Fargana Abdullayeva (Imamverdiyev and Abdullayeva, 2018) developed a DL-based Gaussian–Bernoulli type restricted Boltzmann machine (RBM), which was used to detect the increase in accuracy of DoS attacks, but this model failed to consider big data sets to improve the performance outcomes. Saha et al (2022) established DL and genetic algorithm (GA), followed DL techniques with classifiers for DoS attack detection using GA and the results are compared on the basis of performance and accuracy. Moreover, the method does not use novel algorithms to speed up the detection process.…”

Section: Literature Surveymentioning

confidence: 99%

“…In recent years, the internet has begun to expand and take part in all aspects of our daily life, from billing payments to expanding social network connections worldwide (Wankhede and Kshirsagar, 2018). So, it is important to identify these attacks and follow different prevention techniques (Saha et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

“…It might use the server’s resources, including sockets, ports, memory, databases and input/output bandwidth. In the former case, attacks are due to network-level flooding, whereas the attack in the latter is known as application-level DoS flooding and often the process is carried out in the HTTP Webpage (Saha et al , 2022). Protection of internet services requires effective DoS attack detection.…”

Section: Introductionmentioning

confidence: 99%

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DoS attack detection using Aquila deer hunting optimization enabled deep belief network

Thomas,

B.B.

2024

IJWIS

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Purpose Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible. Design/methodology/approach This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks. Findings The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6. Originality/value The introduced detection approach effectively detects DoS attacks available on the internet.

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