Network evasion detection with Bi-LSTM model

TL;DR

This paper presents a deep learning approach using Bi-LSTM neural networks for detecting network evasion, achieving high accuracy by analyzing sequential network flow data.

Contribution

It introduces a novel Bi-LSTM based architecture with feature extraction and Softmax classification for improved network evasion detection.

Findings

Achieves an average accuracy of 96.1% in detection.

Demonstrates superior performance of Bi-LSTM in serial data analysis.

Provides an effective deep learning framework for network security.

Abstract

Network evasion detection aims to distinguish whether the network flow comes from link layer exists network evasion threat, which is a means to disguise the data traffic on detection system by confusing the signature. Since the previous research works has all sorts of frauds, we propose a architecture with deep learning network to handle this problem. In this paper, we extract the critical information as key features from data frame and also specifically propose to use bidirectional long short-term memory (Bi-LSTM) neural network which shows an outstanding performance to trace the serial information, to encode both the past and future trait on the network flows. Furthermore we introduce a classifier named Softmax at the bottom of Bi-LSTM, holding a character to select the correct class. All experiments results shows that we can achieve a significant performance with a deep Bi-LSTM in network evasion detection and it's average accuracy reaches 96.1%.