Machine learning and deep learning methods for intrusion detection systems: A survey
H Liu, B Lang - applied sciences, 2019 - mdpi.com
Networks play important roles in modern life, and cyber security has become a vital research
area. An intrusion detection system (IDS) which is an important cyber security technique …
area. An intrusion detection system (IDS) which is an important cyber security technique …
Analysis of cyber security attacks and its solutions for the smart grid using machine learning and blockchain methods
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …
Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks
PR Kanna, P Santhi - Expert Systems with Applications, 2022 - Elsevier
The recent advancements in information and communication technologies have led to an
increasing number of online systems and services. These online systems can utilize …
increasing number of online systems and services. These online systems can utilize …
LUCID: A practical, lightweight deep learning solution for DDoS attack detection
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Deep learning-based intrusion detection systems: a systematic review
Nowadays, the ever-increasing complication and severity of security attacks on computer
networks have inspired security researchers to incorporate different machine learning …
networks have inspired security researchers to incorporate different machine learning …
Deep learning in IoT intrusion detection
Abstract The Internet of Things (IoT) is the new paradigm of our times, where smart devices
and sensors from across the globe are interconnected in a global grid, and distributed …
and sensors from across the globe are interconnected in a global grid, and distributed …
A survey of CNN-based network intrusion detection
Over the past few years, Internet applications have become more advanced and widely
used. This has increased the need for Internet networks to be secured. Intrusion detection …
used. This has increased the need for Internet networks to be secured. Intrusion detection …
A novel approach for network intrusion detection using multistage deep learning image recognition
The current rise in hacking and computer network attacks throughout the world has
heightened the demand for improved intrusion detection and prevention solutions. The …
heightened the demand for improved intrusion detection and prevention solutions. The …
Network anomaly detection using deep learning techniques
MK Hooshmand, D Hosahalli - CAAI Transactions on …, 2022 - Wiley Online Library
Convolutional neural networks (CNNs) are the specific architecture of feed‐forward artificial
neural networks. It is the de‐facto standard for various operations in machine learning and …
neural networks. It is the de‐facto standard for various operations in machine learning and …
Hyperparameter optimization for 1D-CNN-based network intrusion detection using GA and PSO
D Kilichev, W Kim - Mathematics, 2023 - mdpi.com
This study presents a comprehensive exploration of the hyperparameter optimization in one-
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …