Intrusion Detection Using Deep Learning and Statistical Data Analysis

Sarwar Wasi, Sarmad Shams, S. Nasim, Arham Shafiq
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引用次数: 1

Abstract

Innovation and creativity have played an important role in the development of every field of life, relatively less but it has created several problems too. Intrusion detection is one of those problems which became difficult with the advancement in computer networks, multiple researchers with multiple techniques have come forward to solve this crucial issue, but network security is still a challenge. In our research, we have come across an idea to detect intrusion using a deep learning algorithm in combination with statistical data analysis of KDD cup 99 datasets. Firstly, we have applied statistical analysis on the given data set to generate a simplified form of data, so that a less complex binary classification model of artificial neural network could apply for data classification. Our system has decreased the complexity of the system and has improved the response time.
基于深度学习和统计数据分析的入侵检测
创新和创造力在生活的各个领域的发展中发挥了重要作用,但相对较少,但它也产生了一些问题。入侵检测是随着计算机网络的发展而变得越来越困难的问题之一,许多研究人员用多种技术来解决这一关键问题,但网络安全仍然是一个挑战。在我们的研究中,我们遇到了一个使用深度学习算法结合KDD cup 99数据集的统计数据分析来检测入侵的想法。首先,我们对给定的数据集进行统计分析,生成数据的简化形式,从而使不太复杂的人工神经网络二元分类模型适用于数据分类。我们的系统降低了系统的复杂性,提高了响应时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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