IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism

Abstract

Network attacks are illegal activities on digital resources within an organizational network with the express intention of compromising systems. A cyber attack can be directed by individuals, communities, states or even from an anonymous source. Hackers commonly conduct network attacks to alter, damage, or steal private data. Intrusion detection systems (IDS) are the best and most effective techniques when it comes to tackle these threats. An IDS is a software application or hardware device that monitors traffic to search for malevolent activity or policy breaches. Moreover, IDSs are designed to be deployed in different environments, and they can either be host-based or network-based. A host-based intrusion detection system is installed on the client computer, while a network-based intrusion detection system is located on the network. IDSs based on deep learning have been used in the past few years and proved their effectiveness. However, these approaches produce a big false negative rate, which impacts the performance and potency of network security. In this paper, a detection model based on long short-term memory (LSTM) and Attention mechanism is proposed. Furthermore, we used four reduction algorithms, namely: Chi-Square, UMAP, Principal Components Analysis (PCA), and Mutual information. In addition, we evaluated the proposed approaches on the NSL-KDD dataset. The experimental results demonstrate that using Attention with all features and using PCA with 03 components had the best performance, reaching an accuracy of 99.09% and 98.49% for binary and multiclass classification, respectively.

Journal Of Big Data: A Bibliometric Analysis

“…BUDIHARTO W has been ranked seventh his top work includes Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis [49], Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM) [50], GNSS-based navigation systems of autonomous drone for delivering items [51]. DOUZI K has been ranked eighth with 6 articles his top work includes GNSS-based navigation systems of autonomous drone for delivering items [52], An LSTM and GRU based trading strategy adapted to the Moroccan market [53] IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism [54], FURHT B has been ranked ninth also with 6m articles in total , his top work includes Deep Learning applications for COVID-19 [55], Text Data Augmentation for Deep Learning [56], Random forest implementation and optimization for Big Data analytics on LexisNexis's high performance computing cluster platform [57]', and VILLANUSTRE F has been ranked tenth with 6 articles which includes Deep learning applications and challenges in big data analytics [58], Random forest implementation and optimization for Big…”

IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism

“…The dimensional reduction process is essential in increasing performance in the PCA model. Further, to optimize the reduction, UMAP and Mutual Information (MI) were used as dimensional reduction to enhance classification tasks for IDS based on the Attention mechanism [17].…”

Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

“…As a result, the classifier's performance suffers because it has features that aren't good enough. The principal component analysis (PCA), mutual information (MI), chi-square, and UMAP are all multidimensional reduction frameworks that can help you get rid of a lot of information [6]. Our experiment used SDAE to make enhancement of dimensional reduction.…”