Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects
IH Sarker - Annals of Data Science, 2023 - Springer
Due to the digitization and Internet of Things revolutions, the present electronic world has a
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …
Cyber risk and cybersecurity: a systematic review of data availability
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …
indicating an increase of more than 50% since 2018. With the average cyber insurance …
[HTML][HTML] A hybrid CNN+ LSTM-based intrusion detection system for industrial IoT networks
Abstract The Internet of Things (IoT) ecosystem has proliferated based on the use of the
internet and cloud-based technologies in the industrial area. IoT technology used in the …
internet and cloud-based technologies in the industrial area. IoT technology used in the …
[HTML][HTML] Deep learning-based intrusion detection approach for securing industrial Internet of Things
S Soliman, W Oudah, A Aljuhani - Alexandria Engineering Journal, 2023 - Elsevier
The widespread deployment of the Internet of Things (IoT) into critical sectors such as
industrial and manufacturing has resulted in the Industrial Internet of Things (IIoT). The IIoT …
industrial and manufacturing has resulted in the Industrial Internet of Things (IIoT). The IIoT …
M-MultiSVM: An efficient feature selection assisted network intrusion detection system using machine learning
The intrusions are increasing daily, so there is a huge amount of privacy violations, financial
loss, illegal transferring of information, etc. Various forms of intrusion occur in networks, such …
loss, illegal transferring of information, etc. Various forms of intrusion occur in networks, such …
E-XAI: Evaluating black-box explainable AI frameworks for network intrusion detection
O Arreche, TR Guntur, JW Roberts, M Abdallah - IEEE Access, 2024 - ieeexplore.ieee.org
The exponential growth of intrusions on networked systems inspires new research directions
on develo** artificial intelligence (AI) techniques for intrusion detection systems (IDS). In …
on develo** artificial intelligence (AI) techniques for intrusion detection systems (IDS). In …
Cybersecurity in the internet of things in industrial management
RJ Raimundo, AT Rosário - Applied Sciences, 2022 - mdpi.com
Nowadays, people live amidst the smart home domain, while there are business
opportunities in industrial smart cities and healthcare. However, there are concerns about …
opportunities in industrial smart cities and healthcare. However, there are concerns about …
Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing
Our world is moving fast towards the era of the Internet of Things (IoT), which connects all
kinds of devices to digital services and brings significant convenience to our lives. With the …
kinds of devices to digital services and brings significant convenience to our lives. With the …
Towards model generalization for intrusion detection: Unsupervised machine learning techniques
Through the ongoing digitization of the world, the number of connected devices is
continuously growing without any foreseen decline in the near future. In particular, these …
continuously growing without any foreseen decline in the near future. In particular, these …
An intelligent DDoS attack detection tree-based model using Gini index feature selection method
Cyber security has recently garnered enormous attention due to the popularity of the Internet
of Things (IoT), intelligent devices' rapid growth, and a vast number of real-life applications …
of Things (IoT), intelligent devices' rapid growth, and a vast number of real-life applications …