Packet inspection for detecting network layer attacks using machine learning

Registration ID: IJNRD_305255

Published ID: IJNRD2504181

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SAMOJU PAVAN KALYAN, DOKKARI KOTESWARA RAO, MODE KRISHNA, Gudivada Vinay Kumar

Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The SoftMax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.

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SAMOJU PAVAN KALYAN, DOKKARI KOTESWARA RAO, MODE KRISHNA, & Gudivada Vinay Kumar (April-2025). Packet inspection for detecting network layer attacks using machine learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), b532-b536. https://ijnrd.org/papers/IJNRD2504181.pdf

Volume 10 Issue 4, April-2025

Pages : b532-b536

Paper Reg. ID: IJNRD_305255

Published Paper Id: IJNRD2504181

Research Area: Science and Technology

Author Type: Indian Author

Country: Visakhapatnam , Andhra Pradesh , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2504181.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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