Cyber risk and cybersecurity: a systematic review of data availability

F Cremer, B Sheehan, M Fortmann… - The Geneva papers …, 2022‏ - pmc.ncbi.nlm.nih.gov
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 …

Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset

ZK Maseer, R Yusof, N Bahaman, SA Mostafa… - IEEE …, 2021‏ - ieeexplore.ieee.org
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …

An enhanced AI-based network intrusion detection system using generative adversarial networks

C Park, J Lee, Y Kim, JG Park, H Kim… - IEEE Internet of Things …, 2022‏ - ieeexplore.ieee.org
As communication technology advances, various and heterogeneous data are
communicated in distributed environments through network systems. Meanwhile, along with …

A survey on machine learning techniques for cyber security in the last decade

K Shaukat, S Luo, V Varadharajan, IA Hameed… - IEEE …, 2020‏ - ieeexplore.ieee.org
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

Performance comparison and current challenges of using machine learning techniques in cybersecurity

K Shaukat, S Luo, V Varadharajan, IA Hameed, S Chen… - Energies, 2020‏ - mdpi.com
Cyberspace has become an indispensable factor for all areas of the modern world. The
world is becoming more and more dependent on the internet for everyday living. The …

[HTML][HTML] A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection

EUH Qazi, A Almorjan, T Zia - Applied Sciences, 2022‏ - mdpi.com
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 …

Cyber threat detection using machine learning techniques: A performance evaluation perspective

K Shaukat, S Luo, S Chen, D Liu - … international conference on …, 2020‏ - ieeexplore.ieee.org
The present-day world has become all dependent on cyberspace for every aspect of daily
living. The use of cyberspace is rising with each passing day. The world is spending more …

A machine-learning-based technique for false data injection attacks detection in industrial IoT

MMN Aboelwafa, KG Seddik… - IEEE Internet of …, 2020‏ - ieeexplore.ieee.org
The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT)
paradigm has resulted in numerous shortcomings as far as security is concerned. One of the …

Machine learning in identity and access management systems: Survey and deep dive

S Aboukadri, A Ouaddah, A Mezrioui - Computers & Security, 2024‏ - Elsevier
The evolution of identity and access management (IAM) has been driven by the expansion
of online services, cloud computing, and the Internet of Things (IoT). The proliferation of …

Genetic convolutional neural network for intrusion detection systems

MT Nguyen, K Kim - Future Generation Computer Systems, 2020‏ - Elsevier
Intrusion detection is the identification of unauthorized access of a computer network. This
paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an …