A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU
Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …
Comparative evaluation of ai-based techniques for zero-day attacks detection
Many intrusion detection and prevention systems (IDPS) have been introduced to identify
suspicious activities. However, since attackers are exploiting new vulnerabilities in systems …
suspicious activities. However, since attackers are exploiting new vulnerabilities in systems …
Attack classification of an intrusion detection system using deep learning and hyperparameter optimization
A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks
on a network. The success of a NIDS depends on the success of its algorithm and the …
on a network. The success of a NIDS depends on the success of its algorithm and the …
Autoencoders and their applications in machine learning: a survey
Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …
ability to learn data features and act as a dimensionality reduction method. With rapid …
Ae-mlp: A hybrid deep learning approach for ddos detection and classification
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …
connectivity massively grows in recent years. Conventional shallow machine learning-based …
Utilising deep learning techniques for effective zero-day attack detection
Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion
Detection Systems (IDS). The increase in both the number and sheer variety of new cyber …
Detection Systems (IDS). The increase in both the number and sheer variety of new cyber …
A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data
J Cui, L Zong, J **e, M Tang - Applied Intelligence, 2023 - Springer
The high dimension, complexity, and imbalance of network data are hot issues in the field of
intrusion detection. Nowadays, intrusion detection systems face some challenges in …
intrusion detection. Nowadays, intrusion detection systems face some challenges in …
GAN augmentation to deal with imbalance in imaging-based intrusion detection
Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
CNN-CNN: dual convolutional neural network approach for feature selection and attack detection on internet of things networks
The Internet of Things (IoT) has brought significant advancements that have connected our
world more closely than ever before. However, the growing number of connected devices …
world more closely than ever before. However, the growing number of connected devices …
Artificial intelligence algorithms for malware detection in android-operated mobile devices
With the rapid expansion of the use of smartphone devices, malicious attacks against
Android mobile devices have increased. The Android system adopted a wide range of …
Android mobile devices have increased. The Android system adopted a wide range of …