An intrusion detection method to detect denial of service attacks using error-correcting output codes and adaptive neuro-fuzzy inference

Majidian, Zohre; TaghipourEivazi, Shiva; Arasteh, Bahman; Babai, Shahram

doi:10.1016/j.compeleceng.2023.108600

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…However, it was complex for larger data sets. Majidian et al (2023) established a new technique for the detection of DoS attacks, in which the evaluation was conducted in three phases. Here, the principal component analysis was used in pre-processing, error correcting output codes were used in feature selection and for the classification an adaptive neuro-fuzzy inference system was used.…”

Section: Literature Surveymentioning

confidence: 99%

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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Section: Literature Surveymentioning

confidence: 99%

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

Thomas,

B.B.

2024

IJWIS

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“…Using BCP38 “Network Ingress Filtering” which, if deployed on the Internet, may stop packets with forged IP addresses from proceeding over the network, this type of attack could be mitigated [ 4 ]. However, research conducted using a random forest algorithm provided numerous benefits for the complexity, accuracy, and memory usage of DDoS attack detection systems [ 5 ]. The basis for this random forest algorithm in our recent research work is a main enhanced algorithm, Snort-based IPS.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

Bakar

Huang

Javed

et al. 2023

Sensors

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Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

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MP-GUARD: A novel multi-pronged intrusion detection and mitigation framework for scalable SD-IoT networks using cooperative monitoring, ensemble learning, and new P4-extracted feature set

El-Sayed,

Said,

Tolba

et al. 2024

Computers and Electrical Engineering

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An intrusion detection method to detect denial of service attacks using error-correcting output codes and adaptive neuro-fuzzy inference

Cited by 6 publications

References 30 publications

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

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

An Intelligent Agent-Based Detection System for DDoS Attacks Using Automatic Feature Extraction and Selection

MP-GUARD: A novel multi-pronged intrusion detection and mitigation framework for scalable SD-IoT networks using cooperative monitoring, ensemble learning, and new P4-extracted feature set

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