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An intrusion detection approach based on incremental long short-term memory

机译:一种基于增量长短期记忆的入侵检测方法

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摘要

The notorious attacks of the last few years have propelled cyber security to the top of the boardroom agenda, and raised the level of criticality to new heights. Therefore, building a secure system has become an important issue that cannot be delayed. In this paper, we propose an intrusion detection approach based on incremental long short-term memory to detect attacks. In order to capture the dynamic information of traffic, we introduce increment which is calculated as the product of function and derivative to long short-term memory (LSTM). Furthermore, the state change are applied to LSTM which is considered as incremental LSTM. Finally, we analyzed the effect of the state change on the performance of incremental LSTM by experiments. Experiments show that the intrusion detection method based on incremental LSTM has a higher accuracy than other methods.
机译:过去几年臭名昭著的攻击将网络安全推到了董事会议程的首位,并将关键性水平提升到了新的高度。因此,构建安全系统已成为刻不容缓的重要问题。在本文中,我们提出了一种基于增量长短期记忆的入侵检测方法来检测攻击。为了捕捉流量的动态信息,我们引入了增量,增量计算为长短期记忆(LSTM)的函数和导数的乘积。此外,状态更改应用于 LSTM,它被视为增量 LSTM。最后,通过实验分析了状态变化对增量LSTM性能的影响。实验表明,基于增量LSTM的入侵检测方法比其他方法具有更高的准确率。

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