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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 …
Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
An enhanced AI-based network intrusion detection system using generative adversarial networks
As communication technology advances, various and heterogeneous data are
communicated in distributed environments through network systems. Meanwhile, along with …
communicated in distributed environments through network systems. Meanwhile, along with …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
Performance comparison and current challenges of using machine learning techniques in cybersecurity
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 …
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
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 …
Cyber threat detection using machine learning techniques: A performance evaluation perspective
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 …
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
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 …
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
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 …
of online services, cloud computing, and the Internet of Things (IoT). The proliferation of …
Genetic convolutional neural network for intrusion detection systems
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 …
paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an …