Jan 1, 2021 · This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks.

Jun 14, 2022 · Furthermore, the experiments are performed using two commonly used benchmark intrusion detection datasets: NSL-KDD and KDDCUP'99. The performance of the proposed model is evaluated using four...

In this paper, we present a hybrid intrusion detection system that combines supervised and unsupervised learning models through an ensemble stacking model to increase the detection accuracy rates of attacks in networks while minimising false alarms.

Nov 24, 2023 · In this study, a new intrusion detection system in a big data environment is developed with a hybrid deep learning algorithm. The algorithm is implemented in Pyspark, Apache Spark’s Python support, using the Google Colabs environment.

This research introduces a hybrid detection approach that uses deep learning techniques to improve intrusion detection accuracy and efficiency. The proposed prototype combines the strength of the XGBoost and MaxPooling1D algorithms within an ensemble model, resulting in a stable and effective solution.

Jan 17, 2023 · To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS).

In our research, we took advantage of the Convolutional Neural Network’s ability to extract spatial features and the Long Short-Term Memory Network’s ability to extract temporal features to create a hybrid intrusion detection system model. We added batch normalization and dropout layers to the model to increase its performance.

Since Deep Learning ( DL) can derive better representations from the data and construct better models, this work proposes an Intrusion Detection System (IDS) based on DL techniques by using the Recurrent Neural Network (RNN) algorithm. Hence, this paper presents the design and implementation of the binary class IDS based on RNNs.

Jun 1, 2023 · We are motivated by deep learnings exceptional performance in various detection and identification tasks, we present an intelligent and efficient network intrusion detection system (NIDS) based on Deep Learning (DL). In this study, we present a deep learning-based IDS for attack detection.

Deep learning algorithms are used automatically to extract essential features from raw network data, which can then be fed into a shallow classifier for effective malicious attack detection.