利用机器学习改进网络攻击检测基于 LSTM 的新概率特征

Er. Krishna Raj Kumar.K, Dr. S Ilangovan
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引用次数: 0

摘要

本项目的重点是通过使用一种称为长短期记忆(LSTM)网络的机器学习技术来改进网络攻击的检测。LSTM 网络是一种擅长分析数据序列的神经网络,因此非常适合识别与网络入侵相关的模式。为了提高 LSTM 模型的有效性,我们引入了新的概率特征,帮助模型更好地区分正常活动和恶意活动。我们的方法包括收集网络数据,对其进行预处理使其适合训练,然后使用这些数据训练 LSTM 模型。我们使用一系列指标来评估模型的性能,以确保其准确性和可靠性。结果表明,我们的方法大大提高了网络攻击的检测率,同时也减少了误报的数量。这意味着我们基于 LSTM 的模型不仅能捕捉到更多的真实威胁,而且在将正常活动识别为攻击时也能减少失误。总之,该项目展示了 LSTM 网络等先进机器学习技术在加强网络安全措施和更有效地防范网络威胁方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LSTM Based New Probability Features Using Machine Learning to Improve Network Attack Detection
This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.
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