[HTML][HTML] A comprehensive review of AI based intrusion detection system
T Sowmya, EAM Anita - Measurement: Sensors, 2023 - Elsevier
In today's digital world, the tremendous amount of data poses a significant challenge to
cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to …
cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to …
A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection
The connectivity of devices through the internet plays a remarkable role in our daily lives.
Many network-based applications are utilized in different domains, eg, health care, smart …
Many network-based applications are utilized in different domains, eg, health care, smart …
[HTML][HTML] Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things
S Wang, W Xu, Y Liu - Computer Networks, 2023 - Elsevier
Abstract The Internet of Things (IoT), as the information carrier of the Internet and
telecommunications networks, is a new network technology comprising physical entities …
telecommunications networks, is a new network technology comprising physical entities …
A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things
The Internet of Things (IoT) is transforming how we live and work, and its applications are
widespread, spanning smart homes, industrial monitoring, smart cities, healthcare …
widespread, spanning smart homes, industrial monitoring, smart cities, healthcare …
[PDF][PDF] 基于联合注意力机制和一维卷积神经网络-双向长短期记忆网络模型的流量异常检测方法
尹梓诺， 马海龙， 胡涛 - 电子与信息学报, 2023 - jeit.ac.cn
针对流量数据集中类别不平衡限制了分类模型对少数类攻击流量的检测性能这一问题,
该文提出一种基于联合注意力机制和1 维卷积神经网络-双向长短期记忆网络(1DCNN-BiLSTM) …
该文提出一种基于联合注意力机制和1 维卷积神经网络-双向长短期记忆网络(1DCNN-BiLSTM) …
A Network Intrusion Detection Model Based on BiLSTM with Multi-Head Attention Mechanism
J Zhang, X Zhang, Z Liu, F Fu, Y Jiao, F Xu - Electronics, 2023 - mdpi.com
A network intrusion detection tool can identify and detect potential malicious activities or
attacks by monitoring network traffic and system logs. The data within intrusion detection …
attacks by monitoring network traffic and system logs. The data within intrusion detection …
A network intrusion detection system based on deep learning in the IoT
X Wang, L Dai, G Yang - The Journal of Supercomputing, 2024 - Springer
As industrial and everyday devices become increasingly interconnected, the data volume
within the Internet of Things (IoT) has experienced a substantial surge. This surge in data …
within the Internet of Things (IoT) has experienced a substantial surge. This surge in data …
Polymorphic adversarial cyberattacks using WGAN
R Chauhan, U Sabeel, A Izaddoost… - Journal of Cybersecurity …, 2021 - mdpi.com
Intrusion Detection Systems (IDS) are essential components in preventing malicious traffic
from penetrating networks and systems. Recently, these systems have been enhancing their …
from penetrating networks and systems. Recently, these systems have been enhancing their …
An intelligent SDN-IoT enabled intrusion detection system for healthcare systems using a hybrid deep learning and machine learning approach
R Arthi, S Krishnaveni, S Zeadally - China Communications, 2024 - ieeexplore.ieee.org
The advent of pandemics such as COVID-19 significantly impacts human behaviour and
lives every day. Therefore, it is essential to make medical services connected to internet …
lives every day. Therefore, it is essential to make medical services connected to internet …
[HTML][HTML] Improving intrusion detection using LSTM-RNN to protect drones' networks
M Gamal, M Elhamahmy, S Taha, H Elmahdy - Egyptian Informatics Journal, 2024 - Elsevier
The expanding use of Unmanned Aerial Vehicle (UAVs)/drones has been noticeable in
recent years. Drones have several uses in a wide range of industries, including the military …
recent years. Drones have several uses in a wide range of industries, including the military …