Smurf attack using hybrid machine learning technique

Revathy, G.; Rajendran, V.; Kumar, Pradeep; Vinuharini, S.; Roopa, G. N.

doi:10.1063/5.0080211

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Smurf Attack Hazard1

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2024

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“…The distinctive characteristic of smurf attacks lies in their utilization of the Internet Control Message Protocol (ICMP) to inundate a target network with echo request (ping) packets distinguishing them from other forms of network intrusions. Originating from a malicious virus these attacks exploit the broadcast nature of ICMP queries [14,15]. Then it amplifying their impact by disseminating requests to broadcast addresses across multiple networks.…”

Section: Smurf Attack Hazardmentioning

confidence: 99%

Leveraging of Gradient Boosting Algorithm in Misuse Intrusion Detection using KDD Cup 99 Dataset

Sulaiman,

Abdulazeez

2024

ijcs

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This study addresses the persistent challenge of intrusion detection as a long-term cybersecurity issue. Investigating the efficacy of machine learning algorithms in anomaly and misuse detection. Research employs supervised learning for misuse detection and explain anomaly detection. Focused on adaptability and continual evolution the study explores the application of ensemble learning models AdaBoost, LightGBM, and XGBoost. Applying these algorithms in the context of intrusion detection. Utilizing the KDD Cup 99 dataset as a benchmark the paper assesses and compares the performance of these models. Besides, illuminating their effectiveness particularly in identifying smurf attacks within the cybersecurity landscape.

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Section: Smurf Attack Hazardmentioning

confidence: 99%